The 100 most-cited articles in Remote Sensing, 2023

Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformer-based architectures, originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular convolution operator for capturing long-range dependencies. Inspired by recent advances in computer vision, the remote sensing community has also witnessed an increased exploration of vision transformers for a diverse set of tasks. Although a number of surveys have focused on transformers in computer vision in general, to the best of our knowledge we are the first to present a systematic review of recent advances based on transformers in remote sensing. Our survey covers more than 60 recent transformer-based methods for different remote sensing problems in sub-areas of remote sensing: very high-resolution (VHR), hyperspectral (HSI) and synthetic aperture radar (SAR) imagery. We conclude the survey by discussing different challenges and…

In recent years, unmanned aerial vehicles (UAVs) have gained popularity due to their flexibility, mobility, and accessibility in various fields, including search and rescue (SAR) operations. The use of UAVs in SAR can greatly enhance the task success rates in reaching inaccessible or dangerous areas, performing challenging operations, and providing real-time monitoring and modeling of the situation. This article aims to help readers understand the latest progress and trends in this field by synthesizing and organizing papers related to UAV search and rescue. An introduction to the various types and components of UAVs and their importance in SAR operations is settled first. Additionally, we present a comprehensive review of sensor integrations in UAVs for SAR operations, highlighting their roles in target perception, localization, and identification. Furthermore, we elaborate on the various applications of UAVs in SAR, including on-site monitoring and modeling, perception and localization of targets, and SAR operations such as task assignment, path planning, and collision avoidance.…

The key elements that underpin food security require the adaptation of agricultural systems to support productivity increases while minimizing inputs and the adverse effects of climate change. The advances in precision agriculture over the past few years have substantially enhanced the efficiency of applying spatially variable agronomic inputs for irrigation, such as fertilizers, pesticides, seeds, and water, and we can attribute them to the increasing number of innovations that utilize new technologies that are capable of monitoring field crops for varying spatial and temporal changes. Remote sensing technology is the primary driver of success in precision agriculture, along with other technologies, such as the Internet of Things (IoT), robotic systems, weather forecasting technology, and global positioning systems (GPSs). More specifically, multispectral imaging (MSI) and hyperspectral imaging (HSI) have made the monitoring of the field crop health to aid decision making and the application of spatially and temporally variable agronomic inputs possible. Furthermore, the fusion of…

Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the…

This study provides a comprehensive review of the efforts utilized in the measurement of water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total suspended solids (TSS). The current method used in the measurement of TDS and TSS includes conventional field and gravimetric approaches. These methods are limited due to the associated cost and labor, and limited spatial coverages. Remote Sensing (RS) applications have, however, been used over the past few decades as an alternative to overcome these limitations. Although they also present underlying atmospheric interferences in images, radiometric and spectral resolution issues. Studies of these WQPs with RS, therefore, require the knowledge and utilization of the best mechanisms. The use of RS for retrieval of TDS, TSS, and their forms has been explored in many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) and satellite sensors such as those onboard the Landsat, Sentinel-2, Aqua, and Terra platforms. The images and their spectral properties serve as inputs for deep…

With the rapid development of object detection technology for unmanned aerial vehicles (UAVs), it is convenient to collect data from UAV aerial photographs. They have a wide range of applications in several fields, such as monitoring, geological exploration, precision agriculture, and disaster early warning. In recent years, many methods based on artificial intelligence have been proposed for UAV object detection, and deep learning is a key area in this field. Significant progress has been achieved in the area of deep-learning-based UAV object detection. Thus, this paper presents a review of recent research on deep-learning-based UAV object detection. This survey provides an overview of the development of UAVs and summarizes the deep-learning-based methods in object detection for UAVs. In addition, the key issues in UAV object detection are analyzed, such as small object detection, object detection under complex backgrounds, object rotation, scale change, and category imbalance problems. Then, some representative solutions based on deep learning for these issues are summarized.…

This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among others. To be effectively retrieved by RS, these WQPs are categorized as optically active or inactive based on their influence on the optical characteristics measured by RS sensors. RS applications offer the opportunity for decisionmakers to quantify and monitor WQPs on a spatiotemporal scale effectively. The use of RS for water quality monitoring has been explored in many studies using empirical, analytical, semi-empirical, and machine-learning algorithms. RS spectral signatures have been applied for the estimation of WQPs using two categories of RS, namely, microwave and optical sensors. Optical RS, which has been heavily applied in the estimation of WQPs, is further grouped as spaceborne and airborne sensors based on the platform they are on board. The choice…

Reliable and timely crop-yield prediction and crop mapping are crucial for food security and decision making in the food industry and in agro-environmental management. The global coverage, rich spectral and spatial information and repetitive nature of remote sensing (RS) data have made them effective tools for mapping crop extent and predicting yield before harvesting. Advanced machine-learning methods, particularly deep learning (DL), can accurately represent the complex features essential for crop mapping and yield predictions by accounting for the nonlinear relationships between variables. The DL algorithm has attained remarkable success in different fields of RS and its use in crop monitoring is also increasing. Although a few reviews cover the use of DL techniques in broader RS and agricultural applications, only a small number of references are made to RS-based crop-mapping and yield-prediction studies. A few recently conducted reviews attempted to provide overviews of the applications of DL in crop-yield prediction. However, they did not cover crop mapping and did not…

This study aimed to provide a systematic overview of the progress made in utilizing remote sensing for assessing the impacts of land use and land cover (LULC) changes on water resources (quality and quantity). This review also addresses research gaps, challenges, and opportunities associated with the use of remotely sensed data in assessment and monitoring. The progress of remote sensing applications in the assessment and monitoring of LULC, along with their impacts on water quality and quantity, has advanced significantly. The availability of high-resolution satellite imagery, the integration of multiple sensors, and advanced classification techniques have improved the accuracy of land cover mapping and change detection. Furthermore, the study highlights the vast potential for providing detailed information on the monitoring and assessment of the relationship between LULC and water resources through advancements in data science analytics, drones, web-based platforms, and balloons. It emphasizes the importance of promoting research efforts, and the integration of remote sensing data…

Weeds are a crucial threat to agriculture, and in order to preserve crop productivity, spreading agrochemicals is a common practice with a potential negative impact on the environment. Methods that can support intelligent application are needed. Therefore, identification and mapping is a critical step in performing site-specific weed management. Unmanned aerial vehicle (UAV) data streams are considered the best for weed detection due to the high resolution and flexibility of data acquisition and the spatial explicit dimensions of imagery. However, with the existence of unstructured crop conditions and the high biological variation of weeds, it remains a difficult challenge to generate accurate weed recognition and detection models. Two critical barriers to tackling this challenge are related to (1) a lack of case-specific, large, and comprehensive weed UAV image datasets for the crop of interest, (2) defining the most appropriate computer vision (CV) weed detection models to assess the operationality of detection approaches in real case conditions. Deep Learning (DL) algorithms,…

The continuous increase in the volume of geospatial data has led to the creation of storage tools and the cloud to process data. Google Earth Engine (GEE) is a cloud-based platform that facilitates geoprocessing, making it a tool of great interest to the academic and research world. This article proposes a bibliometric analysis of the GEE platform to analyze its scientific production. The methodology consists of four phases. The first phase corresponds to selecting “search” criteria, followed by the second phase focused on collecting data during the 2011 and 2022 periods using Elsevier’s Scopus database. Software and bibliometrics allowed to review the published articles during the third phase. Finally, the results were analyzed and interpreted in the last phase. The research found 2800 documents that received contributions from 125 countries, with China and the USA leading as the countries with higher contributions supporting an increment in the use of GEE for the visualization and processing of geospatial data. The intellectual structure study and knowledge mapping showed that…

Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for revolutionizing data analysis and applications in many domains of Earth sciences. This review paper synthesizes the existing literature on AI applications in remote sensing, consolidating and analyzing AI methodologies, outcomes, and limitations. The primary objectives are to identify research gaps, assess the effectiveness of AI approaches in practice, and highlight emerging trends and challenges. We explore diverse applications of AI in remote sensing, including image classification, land cover mapping, object detection, change detection, hyperspectral and radar data analysis, and data fusion. We present an overview of the remote sensing technologies, methods employed, and relevant use cases. We further explore challenges associated with practical AI in remote sensing, such as data quality and availability, model uncertainty and interpretability, and integration with domain expertise as well as potential solutions, advancements, and future directions. We provide a comprehensive…

During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the rapid development of telecommunications and mobile technology, alongside the emergence of online search engines and social media platforms with geotagging technology, has fundamentally changed how human activities and the urban landscape are recorded and depicted. The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development. Urban scholars are now equipped with abundant data to examine many theoretical arguments that often result from limited and indirect observations and less-than-ideal controlled experiments. For the first time, urban scholars can model, simulate, and predict changes in the urban landscape using real-time…

Drones, also known as unmanned aerial vehicles (UAVs), have gained numerous applications due to their low cost, ease of use, vertical takeover and landing, and ability to operate in high-risk or hard-to-reach areas. The contribution of this review is that of building the bridge between drone technology and its application and advancements in the field of Geographic Information System (GIS). The integration of drones and GIS is valuable as it reduces costs and improves accessibility for geospatial data collection. Traditional methods involving aircraft for aerial photography are expensive, requiring the hiring of aircraft, pilots, and photographers. Drones equipped with advanced cameras and artificial intelligence software can replace the conventional technique and at the same time, be economical and time-efficient. The integration of drones and GIS is expected to bring revolutionary benefits in the fields of precision agriculture, urban planning, emergency health response, disaster management, the development of smart cities, food delivery, etc. In this paper, a state-of-the-art…

As the scale of the power grid continues to expand, the human-based inspection method struggles to meet the needs of efficient grid operation and maintenance. Currently, the existing UAV inspection system in the market generally has short endurance power time, high flight operation requirements, low degree of autonomous flight, low accuracy of intelligent identification, slow generation of inspection reports, and other problems. In view of these shortcomings, this paper designs an intelligent inspection system based on self-developed UAVs, including autonomous planning of inspection paths, sliding film control algorithms, mobile inspection schemes and intelligent fault diagnosis. In the first stage, basic data such as latitude, longitude, altitude, and the length of the cross-arms are obtained from the cloud database of the power grid, while the lateral displacement and vertical displacement during the inspection drone operation are calculated, and the inspection flight path is generated independently according to the inspection type. In the second stage, in order to make the UAV’s…

In the cultural heritage field, point clouds, as important raw data of geomatics, are not only three-dimensional (3D) spatial presentations of 3D objects but they also have the potential to gradually advance towards an intelligent data structure with scene understanding, autonomous cognition, and a decision-making ability. The approach of point cloud semantic segmentation as a preliminary stage can help to realize this advancement. With the demand for semantic comprehensibility of point cloud data and the widespread application of machine learning and deep learning approaches in point cloud semantic segmentation, there is a need for a comprehensive literature review covering the topics from the point cloud data acquisition to semantic segmentation algorithms with application strategies in cultural heritage. This paper first reviews the current trends of acquiring point cloud data of cultural heritage from a single platform with multiple sensors and multi-platform collaborative data fusion. Then, the point cloud semantic segmentation algorithms are discussed with their advantages,…

Meeting current needs without compromising future generations’ ability to meet theirs is the only path toward achieving environmental sustainability. As the most valuable natural resource, soil faces global, regional, and local challenges, from quality degradation to mass losses brought on by salinization. These issues affect agricultural productivity and ecological balance, undermining sustainability and food security. Therefore, timely monitoring and accurate mapping of salinization processes are crucial, especially in semi-arid and arid regions where climate variability impacts have already reached alarming levels. Salt-affected soil mapping has enormous potential thanks to recent progress in remote sensing. This paper comprehensively reviews the potential of remote sensing to assess soil salinization. The review demonstrates that large-scale soil salinity estimation based on remote sensing tools remains a significant challenge, primarily due to data resolution and acquisition costs. Fundamental trade-offs constrain practical remote sensing applications in salinization mapping…

Metropolitan areas worldwide have grown rapidly and are usually surrounded by peri-urban zones that are neither urban nor rural. Despite widespread use of the term ‘peri-urban’, physical determination of these spaces is difficult due to their transient nature and multiple definitions. While many have identified peri-urban areas regionally or globally, questions persist on where exactly the peri-urban is located, and what are the most apt methods to delineate its boundaries. The answers are pertinent towards framing targeted policies for governing the dynamic socio-spatial transformations in these zones. This paper reviews peri-urban research over the last 50-plus years to discern the existing methodologies for its identification/demarcation and their applications. For this, a total of 3124 documents on peri-urban studies were identified through keyword searches in Scopus and Google Scholar databases. Thereafter, 56 documents were examined that explicitly dealt with demarcating peri-urban zones. Results reveal that there is no standout/generalized method for peri-urban demarcation.…

Land use/land cover change evaluation and prediction using spatiotemporal data are crucial for environmental monitoring and better planning and management of land use. The main objective of this study is to evaluate land use/land cover changes for the time period of 1991–2022 and predict future changes using the CA-ANN model in the Upper Omo–Gibe River basin. Landsat-5 TM for 1991, 1997, and 2004, Landsat-7 ETM+ for 2010, and Landsat-8 (OLI) for 2016 and 2022 were downloaded from the USGS Earth Explorer Data Center. A random forest machine learning algorithm was employed for LULC classification. The LULC classification result was evaluated using an accuracy assessment technique to assure the correctness of the classification method employing the kappa coefficient. Kappa coefficient values of the classification indicate that there was strong agreement between the classified and reference data. Using the MOLUSCE plugin of QGIS and the CA-ANN model, future LULC changes were predicted. Artificial neural network (ANN) and cellular automata (CA) machine learning methods were made…

In recent years, remote sensing target recognition algorithms based on deep learning technology have gradually become mainstream in the field of remote sensing because of the great improvements that have been made in the accuracy of image target recognition through the use of deep learning. In the research of remote sensing image target recognition based on deep learning, an insufficient number of research samples is often an encountered issue; too small a number of research samples will cause the phenomenon of an overfitting of the model. To solve this problem, data augmentation techniques have also been developed along with the popularity of deep learning, and many methods have been proposed. However, to date, there is no literature aimed at expounding and summarizing the current state of the research applied to data augmentation for remote sensing object recognition, which is the purpose of this article. First, based on the essential principles of data augmentation methods, the existing methods are divided into two categories: data-based data augmentation methods and…

Precise object detection for unmanned aerial vehicle (UAV) images is a prerequisite for many UAV image applications. Compared with natural scene images, UAV images often have many small objects with few image pixels. These small objects are often obscured, densely distributed, or in complex scenes, which causes great interference to object detection. Aiming to solve this problem, a GhostConv-based lightweight YOLO network (GCL-YOLO) is proposed. In the proposed network, a GhostConv-based backbone network with a few parameters was firstly built. Then, a new prediction head for UAV small objects was designed, and the original prediction head for large natural scene objects was removed. Finally, the focal-efficient intersection over union (Focal-EIOU) loss was used as the localization loss. The experimental results of the VisDrone-DET2021 dataset and the UAVDT dataset showed that, compared with the YOLOv5-S network, the mean average precision at IOU = 0.5 achieved by the proposed GCL-YOLO-S network was improved by 6.9% and 1.8%, respectively, while the parameter amount and the…

Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1°) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1° to 0.25°. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized…

In recent years, major natural disasters and public safety accidents have frequently occurred worldwide. In order to deal with various disasters and accidents using rapidly deployable, reliable, efficient, and stable emergency communication networks, all countries in the world are strengthening and improving emergency communication network construction and related technology research. Motivated by these situations, in this paper, we provide a state-of-the-art survey of the current situation and development of emergency communication networks. In this detailed investigation, our primary focus is the extensive discussion of emergency communication network technology, including satellite networks, ad hoc networks, cellular networks, and wireless private networks. Then, we explore and analyze the networks currently applied in emergency rescue, such as the 370M narrowband private network, broadband cluster network, and 5G constellation plan. We propose a broadband-narrowband integrated emergency communication network to provide an effective solution for visual dispatch of emergency…

In recent years, the realm of deep learning has witnessed significant advancements, particularly in object detection algorithms. However, the unique challenges posed by remote sensing images, such as complex backgrounds, diverse target sizes, dense target distribution, and overlapping or obscuring targets, demand specialized solutions. Addressing these challenges, we introduce a novel lightweight object detection algorithm based on Yolov5s to enhance detection performance while ensuring rapid processing and broad applicability. Our primary contributions include: firstly, we implemented a new Lightweight Asymmetric Detection Head (LADH-Head), replacing the original detection head in the Yolov5s model. Secondly, we introduce a new C3CA module, incorporating the Coordinate Attention mechanism, strengthening the network’s capability to extract precise location information. Thirdly, we proposed a new backbone network, replacing the C3 module in the Yolov5s backbone with a FasterConv module, enhancing the network’s feature extraction capabilities. Additionally, we introduced a…

High-temporal-frequency monitoring of transport infrastructure is crucial to facilitate maintenance and prevent major service disruption or structural failures. Ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards for data quality and accuracy. However, routine campaigns and long inspection times are required for data collection and their implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructure (roads, railways and airfields) with a much higher temporal frequency of investigation and the capability to cover wider areas. Nevertheless, the integration of information from (i) satellite remote sensing and (ii) ground-based NDT methods is a subject that is still to be fully explored in civil engineering. This paper aims to review significant stand-alone and combined applications…

Recently, deep learning techniques have been extensively used to detect ships in synthetic aperture radar (SAR) images. The majority of modern algorithms can achieve successful ship detection outcomes when working with multiple-scale ships on a large sea surface. However, there are still issues, such as missed detection and incorrect identification when performing multi-scale ship object detection operations in SAR images of complex scenes. To solve these problems, this paper proposes a complex scenes multi-scale ship detection model, according to YOLOv7, called CSD-YOLO. First, this paper suggests an SAS-FPN module that combines atrous spatial pyramid pooling and shuffle attention, allowing the model to focus on important information and ignore irrelevant information, reduce the feature loss of small ships, and simultaneously fuse the feature maps of ship targets on various SAR image scales, thereby improving detection accuracy and the model’s capacity to detect objects at several scales. The model’s optimization is then improved with the aid of the SIoU loss function. Finally,…

One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world’s forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection…

Hyperspectral Image (HSI) classification methods that use Deep Learning (DL) have proven to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have demonstrated extremely powerful performance in such tasks. However, the lack of training samples is one of the main contributors to low classification performance. Traditional CNN-based techniques under-utilize the inter-band correlations of HSI because they primarily use 2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial information using the same operation. While this overcomes the limitation of 2D-CNNs, it may lead to insufficient extraction of features. In order to overcome this issue, we propose an HSI classification approach named Tri-CNN which is based on a multi-scale 3D-CNN and three-branch feature fusion. We first extract HSI features using 3D-CNN at various scales. The three different features are then flattened and concatenated. To obtain the classification results, the fused features then traverse a number of fully connected layers and eventually a softmax…

With the accelerated development of artificial intelligence, remote-sensing image technologies have gained widespread attention in smart cities. In recent years, remote sensing object detection research has focused on detecting and counting small dense objects in large remote sensing scenes. Small object detection, as a branch of object detection, remains a significant challenge in research due to the image resolution, size, number, and orientation of objects, among other factors. This paper examines object detection based on deep learning and its applications for small object detection in remote sensing. This paper aims to provide readers with a thorough comprehension of the research objectives. Specifically, we aggregate the principal datasets and evaluation methods extensively employed in recent remote sensing object detection techniques. We also discuss the irregularity problem of remote sensing image object detection and overview the small object detection methods in remote sensing images. In addition, we select small target detection methods with excellent performance in…

Object detection in drone-captured images is a popular task in recent years. As drones always navigate at different altitudes, the object scale varies considerably, which burdens the optimization of models. Moreover, high-speed and low-altitude flight cause motion blur on densely packed objects, which leads to great challenges. To solve the two issues mentioned above, based on YOLOv5, we add an additional prediction head to detect tiny-scale objects and replace CNN-based prediction heads with transformer prediction heads (TPH), constructing the TPH-YOLOv5 model. TPH-YOLOv5++ is proposed to significantly reduce the computational cost and improve the detection speed of TPH-YOLOv5. In TPH-YOLOv5++, cross-layer asymmetric transformer (CA-Trans) is designed to replace the additional prediction head while maintain the knowledge of this head. By using a sparse local attention (SLA) module, the asymmetric information between the additional head and other heads can be captured efficiently, enriching the features of other heads. In the VisDrone Challenge 2021, TPH-YOLOv5 won 4th place and…

Remote sensing technology is vital for precision agriculture, aiding in early issue detection, resource management, and environmentally friendly practices. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Given the growing body of scholarly research centered on UAV-based disease detection, a comprehensive review and analysis of current studies becomes imperative to provide a panoramic view of evolving methodologies in plant disease monitoring and to strategically evaluate the potential and limitations of such strategies. This study undertakes a systematic quantitative literature review to summarize existing literature and discern current research trends in UAV-based applications for plant disease detection and monitoring. Results reveal a global disparity in research on the topic, with Asian countries being the top contributing countries (43 out of 103 papers). World regions such as Oceania and Africa…

Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter…

Studying the spatiotemporal distribution pattern of carbon storage, balancing land development and utilization with ecological protection, and promoting urban low-carbon sustainable development are important topics under China’s “dual carbon strategy” (Carbon emissions stabilize and harmonize with natural carbon absorption). However, existing research has paid little attention to the impact of land use changes under different spatial policies on the provincial-scale ecosystem carbon storage. In this study, we established a carbon density database for Liaoning Province and obtained the spatial and temporal distribution of carbon storage over the past 20 years. Then, based on 16 driving factors and multiple spatial policies in Liaoning Province, we predicted land use and land cover changes (LUCC) under three scenarios for 2050 and analyzed the spatiotemporal distribution characteristics and response mechanisms of carbon storage under different scenarios. The results showed that (1) LUCC directly affected carbon storage, with a 35.61% increase in construction land and a decrease in…

Automatic identification and mapping of tree species is an essential task in forestry and conservation. However, applications that can geolocate individual trees and identify their species in heterogeneous forests on a large scale are lacking. Here, we assessed the potential of the Convolutional Neural Network algorithm, Faster R-CNN, which is an efficient end-to-end object detection approach, combined with open-source aerial RGB imagery for the identification and geolocation of tree species in the upper canopy layer of heterogeneous temperate forests. We studied four tree species, i.e., Norway spruce (Picea abies (L.) H. Karst.), silver fir (Abies alba Mill.), Scots pine (Pinus sylvestris L.), and European beech (Fagus sylvatica L.), growing in heterogeneous temperate forests. To fully explore the potential of the approach for tree species identification, we trained single-species and multi-species models. For the single-species models, the average detection accuracy (F1 score) was 0.76. Picea abies was detected with the highest accuracy, with an average F1 of 0.86, followed by A.…

Modeling of land use and land cover (LULC) is a very important tool, particularly in the agricultural field: it allows us to know the potential changes in land area in the future and to consider developments in order to prevent probable risks. The idea is to give a representation of probable future situations based on certain assumptions. The objective of this study is to make future predictions in land use and land cover in the watershed “9 April 1947”, and in the years 2028, 2038 and 2050. Then, the maps obtained with the climate predictions will be integrated into an agro-hydrological model to know the water yield, the sediment yield and the water balance of the studied area by 2050.The future land use and land cover (LULC) scenarios were created using a CA-Markov forecasting model. The results of the simulation of the LULC changes were considered satisfactory, as shown by the values obtained from the kappa indices for agreement (κstandard) = 0.73, kappa for lack of information (κno) = 0.76, and kappa for location at grid cell level (κlocation) = 0.80. Future scenarios modeled in…

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a…

High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution image into a corresponding high-resolution image by a specific algorithm. With the emergence and swift advancement of generative adversarial networks (GANs), image super-resolution reconstruction is experiencing a new era of progress. Unfortunately, there has been a lack of comprehensive efforts to bring together the advancements made in the field of super-resolution reconstruction using generative adversarial networks. Hence, this paper presents a comprehensive overview of the super-resolution image reconstruction technique that utilizes generative adversarial networks. Initially, we examine the operational principles of generative adversarial networks, followed by an overview of the relevant research and background information on reconstructing remote sensing images through super-resolution techniques. Next, we discuss significant…

This paper presents a critical analysis of image-based 3D reconstruction using neural radiance fields (NeRFs), with a focus on quantitative comparisons with respect to traditional photogrammetry. The aim is, therefore, to objectively evaluate the strengths and weaknesses of NeRFs and provide insights into their applicability to different real-life scenarios, from small objects to heritage and industrial scenes. After a comprehensive overview of photogrammetry and NeRF methods, highlighting their respective advantages and disadvantages, various NeRF methods are compared using diverse objects with varying sizes and surface characteristics, including texture-less, metallic, translucent, and transparent surfaces. We evaluated the quality of the resulting 3D reconstructions using multiple criteria, such as noise level, geometric accuracy, and the number of required images (i.e., image baselines). The results show that NeRFs exhibit superior performance over photogrammetry in terms of non-collaborative objects with texture-less, reflective, and refractive surfaces. Conversely,…

The water cycle around the globe is significantly impacted by the moisture in the soil. However, finding a quick and practical model to cope with the enormous amount of data is a difficult issue for remote sensing practitioners. The traditional methods of measuring soil moisture are inefficient at large sizes, which can be replaced by remote sensing techniques for obtaining soil moisture. While determining the soil moisture, the low return frequency of satellites and the lack of images pose a severe challenge to the current remote sensing techniques. Therefore, this paper suggested a novel technique for Soil Moisture Retrieval. In the initial phase, image acquisition is made. Then, VI indexes (NDVI, GLAI, Green NDVI (GNDVI), and WDRVI features) are derived. Further, an improved Water Cloud Model (WCM) is deployed as a vegetation impact rectification scheme. Finally, soil moisture retrieval is determined by the hybrid model combining Deep Max Out Network (DMN) and Bidirectional Gated Recurrent Unit (Bi-GRU) schemes, whose outputs are then passed on to enhanced score level fusion that…

The prevention of soil salinization and managing agricultural irrigation depend greatly on accurately estimating soil salinity. Although the long-standing laboratory method of measuring salinity composition is accurate for determining soil salinity parameters, its use is frequently constrained by the high expense and difficulty of long-term in situ measurement. Soil salinity in the northern Nile Delta of Egypt severely affects agriculture sustainability and food security in Egypt. Understanding the spatial distribution of soil salinity is a critical factor for agricultural development and management in drylands. This research aims to improve soil salinity prediction by using a combined data collection method consisting of Sentinel-1 C radar data and Sentinel-2 optical data acquired simultaneously via integrated radar and optical sensor variables. The modelling approach focuses on feature selection strategies and regression learning. Feature selection approaches that include the filter, wrapper, and embedded methods were used with 47 selected variables depending on a genetic…

Typical representations for arbitrary-oriented object detection tasks include the oriented bounding box (OBB), the quadrilateral bounding box (QBB), and the point set (PointSet). Each representation encounters problems that correspond to its characteristics, such as boundary discontinuity, square-like problems, representation ambiguity, and isolated points, which lead to inaccurate detection. Although many effective strategies have been proposed for various representations, there is still no unified solution. Current detection methods based on Gaussian modeling have demonstrated the possibility of resolving this dilemma; however, they remain limited to OBB. To go further, in this paper, we propose a unified Gaussian representation called G-Rep to construct Gaussian distributions for OBB, QBB, and PointSet, which achieves a unified solution to various representations and problems. Specifically, PointSet- or QBB-based object representations are converted into Gaussian distributions and their parameters are optimized using the maximum likelihood estimation algorithm. Then, three…

Remote sensing image scene classification with deep learning (DL) is a rapidly growing field that has gained significant attention in the past few years. While previous review papers in this domain have been confined to 2020, an up-to-date review to show the progression of research extending into the present phase is lacking. In this review, we explore the recent articles, providing a thorough classification of approaches into three main categories: Convolutional Neural Network (CNN)-based, Vision Transformer (ViT)-based, and Generative Adversarial Network (GAN)-based architectures. Notably, within the CNN-based category, we further refine the classification based on specific methodologies and techniques employed. In addition, a novel and rigorous meta-analysis is performed to synthesize and analyze the findings from 50 peer-reviewed journal articles to provide valuable insights in this domain, surpassing the scope of existing review articles. Our meta-analysis shows that the most adopted remote sensing scene datasets are AID (41 articles) and NWPU-RESISC45 (40). A notable paradigm…

In the context of sustainable development and dual-carbon construction, to quantify the carbon storage and its spatial-temporal distribution characteristics of Guilin City and predict the carbon storage of Guilin City in 2035 under different future scenarios, this study set four future scenarios based on SDGs and the sustainable development plan of Guilin City: natural development, economic priority, ecological priority, and sustainable development. At the same time, FLUS and InVEST models and GeoDa 1.20and ArcGIS software were used to establish a coupling model of land use change and ecosystem carbon storage to simulate and predict the distribution and change of ecosystem carbon storage based on land use change in the future. The results showed that: (1) From 2005 to 2020, forest land was the main type of land use in Guilin, and cropland and impervious continued to expand. In 2035, the forest land under four different future scenarios will be an important transformation type; (2) From 2005 to 2020, the carbon storage in the northwest of Guilin was relatively high, and the carbon…

Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with…

To restrict the entry of polluting components into water bodies, particularly rivers, it is critical to undertake timely monitoring and make rapid choices. Traditional techniques of assessing water quality are typically costly and time-consuming. With the advent of remote sensing technologies and the availability of high-resolution satellite images in recent years, a significant opportunity for water quality monitoring has arisen. In this study, the water quality index (WQI) for the Hudson River has been estimated using Landsat 8 OLI-TIRS images and four Artificial Intelligence (AI) models, such as M5 Model Tree (MT), Multivariate Adaptive Regression Spline (MARS), Gene Expression Programming (GEP), and Evolutionary Polynomial Regression (EPR). In this way, 13 water quality parameters (WQPs) (i.e., Turbidity, Sulfate, Sodium, Potassium, Hardness, Fluoride, Dissolved Oxygen, Chloride, Arsenic, Alkalinity, pH, Nitrate, and Magnesium) were measured between 14 March 2021 and 16 June 2021 at a site near Poughkeepsie, New York. First, Multiple Linear Regression (MLR) models were created…

Remote sensing is a tool of interest for a large variety of applications. It is becoming increasingly more useful with the growing amount of available remote sensing data. However, the large amount of data also leads to a need for improved automated analysis. Deep learning is a natural candidate for solving this need. Change detection in remote sensing is a rapidly evolving area of interest that is relevant for a number of fields. Recent years have seen a large number of publications and progress, even though the challenge is far from solved. This review focuses on deep learning applied to the task of change detection in multispectral remote-sensing images. It provides an overview of open datasets designed for change detection as well as a discussion of selected models developed for this task—including supervised, semi-supervised and unsupervised. Furthermore, the challenges and trends in the field are reviewed, and possible future developments are considered.

Grasslands are one of the world’s largest ecosystems, accounting for 30% of total terrestrial biomass. Considering that aboveground biomass (AGB) is one of the most essential ecosystem services in grasslands, an accurate and faster method for estimating AGB is critical for managing, protecting, and promoting ecosystem sustainability. Unmanned aerial vehicles (UAVs) have emerged as a useful and practical tool for achieving this goal. Here, we review recent research studies that employ UAVs to estimate AGB in grassland ecosystems. We summarize different methods to establish a comprehensive workflow, from data collection in the field to data processing. For this purpose, 64 research articles were reviewed, focusing on several features including study site, grassland species composition, UAV platforms, flight parameters, sensors, field measurement, biomass indices, data processing, and analysis methods. The results demonstrate that there has been an increase in scientific research evaluating the use of UAVs in AGB estimation in grasslands during the period 2018–2022. Most of the studies…

Large-volume hydraulic concrete structures, such as concrete dams, often suffer from damage due to the influence of alternating loads and material aging during the service process. The occurrence and further expansion of cracks will affect the integrity, impermeability, and durability of the dam concrete. Therefore, monitoring the changing status of cracks in hydraulic concrete structures is very important for the health service of hydraulic engineering. This study combines computer vision and artificial intelligence methods to propose an automatic damage detection and diagnosis method for hydraulic structures. Specifically, to improve the crack feature extraction effect, the Xception backbone network, which has fewer parameters than the ResNet backbone network, is adopted. With the aim of addressing the problem of premature loss of image detail information and small target information of tiny cracks in hydraulic concrete structures, an adaptive attention mechanism image semantic segmentation algorithm based on Deeplab V3+ network architecture is proposed. Crack images collected…

Abundant spectral information endows unique advantages of hyperspectral remote sensing images in target location and recognition. Target detection techniques locate materials or objects of interest from hyperspectral images with given prior target spectra, and have been widely used in military, mineral exploration, ecological protection, etc. However, hyperspectral target detection is a challenging task due to high-dimension data, spectral changes, spectral mixing, and so on. To this end, many methods based on optimization and machine learning have been proposed in the past decades. In this paper, we review the representatives of hyperspectral image target detection methods and group them into seven categories: hypothesis testing-based methods, spectral angle-based methods, signal decomposition-based methods, constrained energy minimization (CEM)-based methods, kernel-based methods, sparse representation-based methods, and deep learning-based methods. We then comprehensively summarize their basic principles, classical algorithms, advantages, limitations, and connections. Meanwhile,…

The rapid growth of urban populations and the need for sustainable urban planning and development has made Unmanned Aerial Vehicles (UAVs) a valuable tool for data collection, mapping, and monitoring. This article reviews the applications of UAV technology in sustainable urban development, particularly in Malaysia. It explores the potential of UAVs to transform infrastructure projects and enhance urban systems, underscoring the importance of advanced applications in Southeast Asia and developing nations worldwide. Following the PRISMA 2020 statement, this article adopts a systematic review process and identifies 98 relevant studies out of 591 records, specifically examining the use of UAVs in urban planning. The emergence of the UAV-as-a-service sector has led to specialized companies offering UAV operations for site inspections, 3D modeling of structures and terrain, boundary assessment, area estimation, master plan formulation, green space analysis, environmental monitoring, and archaeological monument mapping. UAVs have proven to be versatile tools with applications across…

Although existing anchor-based oriented object detection methods have achieved remarkable results, they require manual preset boxes, which introduce additional hyper-parameters and calculations. These methods often use more complex architectures for better performance, which makes them difficult to deploy on computationally constrained embedded platforms, such as satellites and unmanned aerial vehicles. We aim to design a high-performance algorithm that is simple, fast, and easy to deploy for aerial image detection. In this article, we propose a one-stage anchor-free rotated object detector, FCOSR, that can be deployed on most platforms and uses our well-defined label assignment strategy for the features of the aerial image objects. We use the ellipse center sampling method to define a suitable sampling region for an oriented bounding box (OBB). The fuzzy sample assignment strategy provides reasonable labels for overlapping objects. To solve the problem of insufficient sampling, we designed a multi-level sampling module. These strategies allocate more appropriate labels to training…

Due to the advantages of Synthetic Aperture Radar (SAR), the study of Automatic Target Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a Convolutional Neural Network (CNN), works in an end-to-end way and has powerful feature-extracting abilities. Thus, researchers in SAR ATR also seek solutions from deep learning. We review the related algorithms with regard to SAR ATR in this paper. We firstly introduce the commonly used datasets and the evaluation metrics. Then, we introduce the algorithms before deep learning. They are template-matching-, machine-learning- and model-based methods. After that, we introduce mainly the SAR ATR methods in the deep-learning era (after 2017); those methods are the core of the paper. The non-CNNs and CNNs, that is, those used in SAR ATR, are summarized at the beginning. We found that researchers tend to design specialized CNN for SAR ATR. Then, the methods to solve the problem raised by limited samples are reviewed. They are data augmentation, Generative Adversarial Networks (GAN), electromagnetic simulation, transfer…

Object detection is essential to the interpretation of optical remote sensing images and can serve as a foundation for research into additional visual tasks that utilize remote sensing. However, the object detection network currently employed in optical remote sensing images underutilizes the output of the feature pyramid, so there remains potential for an improved detection. At present, a suitable balance between the detection efficiency and detection effect is difficult to attain. This paper proposes an enhanced YOLOv5 algorithm for object detection in high-resolution optical remote sensing images, utilizing multiple layers of the feature pyramid, a multi-detection-head strategy, and a hybrid attention module to improve the effect of object-detection networks for use with optical remote sensing images. According to the SIMD dataset, the mAP of the proposed method was 2.2% better than YOLOv5 and 8.48% better than YOLOX, achieving an improved balance between the detection effect and speed.

The world has seen an increase in the number of wildland fires in recent years due to various factors. Experts warn that the number of wildland fires will continue to increase in the coming years, mainly because of climate change. Numerous safety mechanisms such as remote fire detection systems based on deep learning models and vision transformers have been developed recently, showing promising solutions for these tasks. To the best of our knowledge, there are a limited number of published studies in the literature, which address the implementation of deep learning models for wildland fire classification, detection, and segmentation tasks. As such, in this paper, we present an up-to-date and comprehensive review and analysis of these vision methods and their performances. First, previous works related to wildland fire classification, detection, and segmentation based on deep learning including vision transformers are reviewed. Then, the most popular and public datasets used for these tasks are presented. Finally, this review discusses the challenges present in existing works. Our…

The global navigation satellite system-reflectometry (GNSS-R) field has experienced an exponential growth as it is becoming relevant to many applications and has captivated the attention of an elevated number of research scholars, research centers and companies around the world. Primarily based on the contents of two Special Issues dedicated to the applications of GNSS-R to Earth observation, this review article provides an overview of the latest advances in the GNSS-R field. Studies are reviewed from four perspectives: (1) technology advancements, (2) ocean applications, (3) the emergent land applications, and (4) new science investigations. The technology involved in the GNSS-R design has evolved from its initial GPS L1 LHCP topology to include the use of other GNSS bands (L2, L5, Galileo, etc.), as well as consider RHCP/LHCP-receiving polarizations in order to perform polarimetric studies. Ocean applications have included developments towards ocean wind speed retrievals, swell and altimetry. Land applications have evolved considerably in the past few years; studies have used…

Nowadays, millions of photovoltaic (PV) plants are installed around the world. Given the widespread use of PV supply systems and in order to keep these PV plants safe and to avoid power losses, they should be carefully protected, and eventual faults should be detected, classified and isolated. In this paper, different machine learning (ML) and deep learning (DL) techniques were assessed for fault detection and diagnosis of PV modules. First, a dataset of infrared thermography images of normal and failure PV modules was collected. Second, two sub-datasets were built from the original one: The first sub-dataset contained normal and faulty IRT images, while the second one comprised only faulty IRT images. The first sub-dataset was used to develop fault detection models referred to as binary classification, for which an image was classified as representing a faulty PV panel or a normal one. The second one was used to design fault diagnosis models, referred to as multi-classification, where four classes (Fault1, Fault2, Fault3 and Fault4) were examined. The investigated faults were,…

Acquiring road information is important for smart cities and sustainable urban development. In recent years, significant progress has been made in the extraction of urban road information from remote sensing images using deep learning (DL) algorithms. However, due to the complex shape, narrowness, and high span of roads in the images, the results are often unsatisfactory. This article proposes a Seg-Road model to improve road connectivity. The Seg-Road uses a transformer structure to extract the long-range dependency and global contextual information to improve the fragmentation of road segmentation and uses a convolutional neural network (CNN) structure to extract local contextual information to improve the segmentation of road details. Furthermore, a novel pixel connectivity structure (PCS) is proposed to improve the connectivity of road segmentation and the robustness of prediction results. To verify the effectiveness of Seg-Road for road segmentation, the DeepGlobe and Massachusetts datasets were used for training and testing. The experimental results show that Seg-Road achieves…

Automatic ship detection in SAR images plays an essential role in both military and civilian fields. However, most of the existing deep learning detection methods introduce complex models and huge calculations while improving the detection accuracy, which is not conducive to the application of real-time ship detection. To solve this problem, an efficient lightweight network YOLO-Lite is proposed for SAR ship detection in this paper. First, a lightweight feature enhancement backbone (LFEBNet) is designed to reduce the amount of calculation. Additionally, a channel and position enhancement attention (CPEA) module is constructed and embedded into the backbone network to more accurately locate the target location by capturing the positional information. Second, an enhanced spatial pyramid pooling (EnSPP) module is customized to enhance the expression ability of features and address the position information loss of small SAR ships in high-level features. Third, we construct an effective multi-scale feature fusion network (MFFNet) with two feature fusion channels to obtain feature maps…

The development of UAVs and multispectral cameras has led to remote sensing applications with unprecedented spatial resolution. However, uncertainty remains on the radiometric calibration process for converting raw images to surface reflectance. Several calibration methods exist, but the advantages and disadvantages of each are not well understood. We performed an empirical analysis of five different methods for calibrating a 10-band multispectral camera, the MicaSense RedEdge MX Dual Camera System, by comparing multispectral images with spectrometer measurements taken in the field on the same day. Two datasets were collected, one in clear-sky and one in overcast conditions on the same field. We found that the empirical line method (ELM), using multiple radiometric reference targets imaged at mission altitude performed best in terms of bias and RMSE. However, two user-friendly commercial solutions relying on one single grey reference panel were only slightly less accurate and resulted in sufficiently accurate reflectance maps for most applications, particularly in clear-sky…

On 6 February 2023, a devastating doublet of earthquakes with magnitudes of Mw 7.8 and Mw 7.6 successively struck southeastern Turkey near the border of Syria. The earthquake sequence represents the strongest earthquakes in Turkey during the past 80 years and caused an extensive loss of life and property. In this study, we processed Sentinel-1 and GPS data to derive the complete surface displacement caused by the earthquake sequence. The surface displacements were adopted to invert for the fault geometry and coseismic slip distribution on the seismogenic faults of the earthquake sequence. The results indicate that the coseismic rupture of the Turkey earthquake sequence was dominated by left-lateral strike slips with a maximum slip of ~10 m on the East Anatolian Fault Zone (EAFZ) and the Sürgü fault (SF). Significant surface ruptures are recognized based on the geodetic inversion, which is consistent with the analysis of post-earthquake satellite images. The cumulative released moment of the two earthquakes reached 9.62 × 1020 Nm, which corresponds to an event of Mw 7.95.…

Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global Circulation Models (GCMs) are evaluated against corresponding indices with observations from the Global Climate Center Precipitation Dataset (GPCC V2020) over five sub-regions across Arid Central Asia (ACA), using the Taylor diagram, interannual variability skill score (IVS) and comprehensive rating index (MR). Moreover, we compare four multi-model ensemble approaches: arithmetic average multi-model ensemble (AMME), median multi-model ensemble (MME), pattern performance-based multi-model ensemble (MM-PERF) and independence weighted mean (IWM). The results show that CMIP6 models have a certain ability to simulate the spatial distribution of extreme precipitation in ACA and the best ability to simulate simple daily intensity (SDII), but it is difficult to capture the spatial bias of consecutive wet days (CWD). Almost all models represent…

Craters on the lunar surface are the most direct method for the study of geological processes and are of great significance to the study of lunar evolution. In order to fill the research gap on small craters (diameter less than 3 m), we focus on the small craters around the moving path of the Yutu-2 lunar rover and carry out a 3D reconstruction and geometrical morphology analysis on them. First, a self-calibration model with multiple feature constraints is used to calibrate the navigation camera and obtain the internal and external parameters. Then, the sequence images with overlapping regions from neighboring stations are used to obtain the precise position of the rover through the bundle adjustment (BA) method. After that, a cross-scale cost aggregation for a stereo matching network is proposed to obtain a parallax map, which can further obtain 3D point clouds of the lunar surface. Finally, the indexes of the craters are extracted (diameter D, depth d, and depth–diameter ratio dr), and the different indicators are fitted and analyzed. The results suggest that CscaNet has an…

The detection of infrared vehicle targets by UAVs poses significant challenges in the presence of complex ground backgrounds, high target density, and a large proportion of small targets, which result in high false alarm rates. To alleviate these deficiencies, a novel YOLOv7-based, multi-scale target detection method for infrared vehicle targets is proposed, which is termed YOLO-ViT. Firstly, within the YOLOV7-based framework, the lightweight MobileViT network is incorporated as the feature extraction backbone network to fully extract the local and global features of the object and reduce the complexity of the model. Secondly, an innovative C3-PANet neural network structure is delicately designed, which adopts the CARAFE upsampling method to utilize the semantic information in the feature map and improve the model’s recognition accuracy of the target region. In conjunction with the C3 structure, the receptive field will be increased to enhance the network’s accuracy in recognizing small targets and model generalization ability. Finally, the K-means++ clustering method is utilized to…

The automatic detection of tree crowns and estimation of crown areas from remotely sensed information offer a quick approach for grasping the dynamics of forest ecosystems and are of great significance for both biodiversity and ecosystem conservation. Among various types of remote sensing data, unmanned aerial vehicle (UAV)-acquired RGB imagery has been increasingly used for tree crown detection and crown area estimation; the method has efficient advantages and relies heavily on deep learning models. However, the approach has not been thoroughly investigated in deciduous forests with complex crown structures. In this study, we evaluated two widely used, deep-learning-based tree crown detection and delineation approaches (DeepForest and Detectree2) to assess their potential for detecting tree crowns from UAV-acquired RGB imagery in an alpine, temperate deciduous forest with a complicated species composition. A total of 499 digitized crowns, including four dominant species, with corresponding, accurate inventory data in a 1.5 ha study plot were treated as training and validation…

In the context of climate change, the occurrence of water stress in forest ecosystems, which are solely dependent on precipitation, has exhibited a rising trend, even among species that are typically regarded as drought-tolerant. Remote sensing techniques offer an efficient, comprehensive, and timely approach for monitoring forests at local and regional scales. These techniques also enable the development of diverse indicators of plant water status, which can play a critical role in evaluating forest water stress. This review aims to provide an overview of remote sensing applications for monitoring water stress in forests and reveal the potential of remote sensing and geographic information system applications in monitoring water stress for effective forest resource management. It examines the principles and significance of utilizing remote sensing technologies to detect forest stress caused by water deficit. In addition, by a quantitative assessment of remote sensing applications of studies in refereed publications, the review highlights the overall trends and the value of the…

Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level and capabilities of military and civilian automation. Although RATR has been successfully applied in some aspects, the complete theoretical system has not been established. At present, deep learning algorithms have received a lot of attention and have emerged as potential and feasible solutions in RATR. This paper mainly reviews related articles published between 2010 and 2022, which corresponds to the period when deep learning methods were introduced into RATR research. In this paper, the current research status of radar target characteristics is summarized, including motion, micro-motion, one-dimensional, and two-dimensional characteristics, etc. This paper reviews the progress of deep learning methods in the feature extraction and recognition of radar target characteristics in recent years, including space, air, ground, sea-surface targets, etc.…

Owning to the nature of flood events, near-real-time flood detection and mapping is essential for disaster prevention, relief, and mitigation. In recent years, the rapid advancement of deep learning has brought endless possibilities to the field of flood detection. However, deep learning relies heavily on training samples and the availability of high-quality flood datasets is rather limited. The present study collected 16 flood events in the Yangtze River Basin and divided them into three categories for different purpose: training, testing, and application. An efficient methodology of dataset-generation for training, testing, and application was proposed. Eight flood events were used to generate strong label datasets with 5296 tiles as flood training samples along with two testing datasets. The performances of several classic convolutional neural network models were evaluated with those obtained datasets, and the results suggested that the efficiencies and accuracies of convolutional neural network models were obviously higher than that of the threshold method. The effects of VH…

Many fields have identified an increasing need to use global satellite precipitation products for hydrological applications, especially in ungauged basins. In this study, we conduct a comprehensive evaluation of three Satellite-based Precipitation Products (SPPs): Integrated Multi–satellitE Retrievals for GPM (IMERG) Final run V6, Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT) V1.5, and Multi-Source Weighted-Ensemble Precipitation (MSWEP) V2.2 for a subbasin of the Mekong River Basin (MRB). The study area of the Srepok River basin (SRB) represents the Central Highland sub-climatic zone in Vietnam under the impacts of newly built reservoirs during 2001–2018. In this study, our evaluation was performed using the Rainfall Assessment Framework (RAF) with two separated parts: (1) an intercomparison of rainfall characteristics between rain gauges and SPPs; and (2) a hydrological comparison of simulated streamflow driven by SPPs and rain gauges. Several key findings are: (1) IMERGF-V6 shows the highest performance compared to other SPP products, followed by SM2RAIN-ASCAT…

After decades of research, automatic synthetic aperture radar (SAR)-optical registration remains an unsolved problem. SAR and optical satellites utilize different imaging mechanisms, resulting in imagery with dissimilar heterogeneous characteristics. Transforming and translating these characteristics into a shared domain has been the main challenge in SAR-optical matching for many years. Combining the two sensors will improve the quality of existing and future remote sensing applications across multiple industries. Several approaches have emerged as promising candidates in the search for combining SAR and optical imagery. In addition, recent research has indicated that machine learning-based approaches have great potential for filling the information gap posed by utilizing only one sensor type in Earth observation applications. However, several challenges remain, and combining them is a multi-step process where no one-size-fits-all approach is available. This article reviews traditional, state-of-the-art, and recent development trends in SAR-optical co-registration methods.

An enhanced Doppler resolution and sidelobe suppression have long been practical issues for moving target detection using Golay complementary waveforms. In this paper, Golay complementary waveform radar returns are combined with a proposed processor, the pointwise thresholding processor (PTP). Compared to the pointwise minimization processor (PMP) illustrated in a previous work, which could only achieve a Doppler resolution comparable to existing methods, this approach essentially increases the Doppler resolution to a very high level in theory. This study also introduced a further filtering process for the delay-Doppler map of the PTP, and simulations verified that the method results in a delay-Doppler map virtually free of range sidelobes.

Unmanned aerial vehicles (UAVs) equipped with computer vision capabilities have been widely utilized in several remote sensing applications, such as precision agriculture, environmental monitoring, and surveillance. However, the commercial usage of these UAVs in such applications is mostly performed manually, with humans being responsible for data observation or offline processing after data collection due to the lack of on board AI on edge. Other technical methods rely on the cloud computation offloading of AI applications, where inference is conducted on video streams, which can be unscalable and infeasible due to remote cloud servers’ limited connectivity and high latency. To overcome these issues, this paper presents a new approach to using edge computing in drones to enable the processing of extensive AI tasks onboard UAVs for remote sensing. We propose a cloud–edge hybrid system architecture where the edge is responsible for processing AI tasks and the cloud is responsible for data storage, manipulation, and visualization. We designed AERO, a UAV brain system with onboard AI…

Coastal areas are increasingly endangered by climate change and associated sea level rise, which could have serious consequences, such as shoreline erosion and coastal city submergence. The current study aims to conduct a historical trend analysis (HTA) and predict the shoreline changes of the Nile Delta coasts. The Digital Shoreline Analysis System (DSAS) software, with the GIS environment, is used for monitoring the shoreline changes using a number of statistical methods (SCE, NSM, EPR, WLR and LRR). Satellite images from 1974 to 2022 were collected and geometrically corrected using supervised classification to detect the shoreline change of the Nile Delta. The GIS was used for detecting and monitoring changes in the shoreline, as well as forecasting future changes in the shoreline for the next 10 and 20 years (2033–2043). The critical sections of the Nile Delta were identified, and a time series analysis of shoreline changes was conducted. For each section, linear equations were established to predict probable changes in the shoreline. Between 1974 and 2022, the shoreline of the…

The coastal zone represents a unique interface between land and sea, and addressing the ecological crisis it faces is of global significance. One of the most fundamental and effective measures is to extract the coastline’s location on a large scale, dynamically, and accurately. Remote sensing technology has been widely employed in coastline extraction due to its temporal, spatial, and sensor diversity advantages. Substantial progress has been made in coastline extraction with diversifying data types and information extraction methods. This paper focuses on discussing the research progress related to data sources and extraction methods for remote sensing-based coastline extraction. We summarize the suitability of data and some extraction algorithms for several specific coastline types, including rocky coastlines, sandy coastlines, muddy coastlines, biological coastlines, and artificial coastlines. We also discuss the significant challenges and prospects of coastline dataset construction, remotely sensed data selection, and the applicability of the extraction method. In particular, we…

Object detection for remote sensing is a fundamental task in image processing of remote sensing; as one of the core components, small or tiny object detection plays an important role. Despite the considerable advancements achieved in small object detection with the integration of CNN and transformer networks, there remains untapped potential for enhancing the extraction and utilization of information associated with small objects. Particularly within transformer structures, this potential arises from the disregard of the complex and the intertwined interplay between spatial context information and channel information during the global modeling of pixel-level information within small objects. As a result, valuable information is prone to being obfuscated and annihilated. To mitigate this limitation, we propose an innovative framework, YOLO-DCTI, that capitalizes on the Contextual Transformer (CoT) framework for the detection of small or tiny objects. Specifically, within CoT, we seamlessly incorporate global residuals and local fusion mechanisms throughout the entire input-to-output…

Land scene classification in satellite imagery has a wide range of applications in remote surveillance, environment monitoring, remote scene analysis, Earth observations and urban planning. Due to immense advantages of the land scene classification task, several methods have been proposed during recent years to automatically classify land scenes in remote sensing images. Most of the work focuses on designing and developing deep networks to identify land scenes from high-resolution satellite images. However, these methods face challenges in identifying different land scenes. Complex texture, cluttered background, extremely small size of objects and large variations in object scale are the common challenges that restrict the models to achieve high performance. To tackle these challenges, we propose a multi-branch deep learning framework that efficiently combines global contextual features with multi-scale features to identify complex land scenes. Generally, the framework consists of two branches. The first branch extracts global contextual information from different regions of the…

Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the increasing amount of data and the rapid development of physical models, deep learning (DL) and its related technique have become an irreplaceable solution for solving various tasks and breaking the limitations of traditional methods. PI and DL have been combined successfully to provide brand-new solutions to many practical applications. This review briefly introduces PI and DL’s most relevant concepts and models. It then shows how DL has been applied for PI tasks, including image restoration, object detection, image fusion, scene classification, and resolution improvement. The review covers the state-of-the-art works combining PI with DL algorithms and recommends some potential future research directions. We hope that the present work will be helpful for researchers in the fields of both…

Deep learning has achieved great success in remote sensing image change detection (CD). However, most methods focus only on the changed regions of images and cannot accurately identify their detailed semantic categories. In addition, most CD methods using convolutional neural networks (CNN) have difficulty capturing sufficient global information from images. To address the above issues, we propose a novel symmetric multi-task network (SMNet) that integrates global and local information for semantic change detection (SCD) in this paper. Specifically, we employ a hybrid unit consisting of pre-activated residual blocks (PR) and transformation blocks (TB) to construct the (PRTB) backbone, which obtains more abundant semantic features with local and global information from bi-temporal images. To accurately capture fine-grained changes, the multi-content fusion module (MCFM) is introduced, which effectively enhances change features by distinguishing foreground and background information in complex scenes. In the meantime, the multi-task prediction branches are adopted, and the multi-task…

The accurate mapping of crop types is crucial for ensuring food security. Remote Sensing (RS) satellite data have emerged as a promising tool in this field, offering broad spatial coverage and high temporal frequency. However, there is still a growing need for accurate crop type classification methods using RS data due to the high intra- and inter-class variability of crops. In this vein, the current study proposed a novel Parallel-Cascaded ensemble structure (Pa-PCA-Ca) with seven target classes in Google Earth Engine (GEE). The Pa section consisted of five parallel branches, each generating Probability Maps (PMs) for different target classes using multi-temporal Sentinel-1/2 and Landsat-8/9 satellite images, along with Machine Learning (ML) models. The PMs exhibited high correlation within each target class, necessitating the use of the most relevant information to reduce the input dimensionality in the Ca part. Thereby, Principal Component Analysis (PCA) was employed to extract the top uncorrelated components. These components were then utilized in the Ca structure, and the final…

Efficient land management and farming practices are critical to maintaining agricultural production, especially in Europe with limited arable land. It is very time consuming to rely on a manual field inspection of cultivated land to archive farm crops. But with the help of satellite monitoring data on the earth’s surface, it is a new vision to classify farmland based on deep learning. This article has studied the Sentinel 2 (S2) data, which are top-of-atmosphere (TOA) reflectance values at the processing level-1C (L1C) observed from some areas of Germany and France. Aiming at the problem that the interference of atmosphere and cloud coverage weakens the recognition accuracy of subsequent algorithms, a method of combining feature expansion and feature importance analysis is proposed to optimize the raw S2 data. Specifically, the new 13 spectral features are expanded based on the linear and nonlinear combination of the raw 13 spectral bands of S2. The random forest (RF) algorithm is used to score the importance of features, and the important features of each time series are selected…

Information on the spatiotemporal variability of soil properties and states within the agricultural landscape is vital to identify management zones supporting precision agriculture (PA). Ground-penetrating radar (GPR) and electromagnetic induction (EMI) techniques have been applied to assess soil properties, states, processes, and their spatiotemporal variability. This paper reviews the fundamental operating principles of GPR and EMI, their applications in soil studies, advantages and disadvantages, and knowledge gaps leading to the identification of the difficulties in integrating these two techniques to complement each other in soil data studies. Compared to the traditional methods, GPR and EMI have advantages, such as the ability to take non-destructive repeated measurements, high resolution, being labor-saving, and having more extensive spatial coverage with geo-referenced data within agricultural landscapes. GPR has been widely used to estimate soil water content (SWC) and water dynamics, while EMI has broader applications such as estimating SWC, soil salinity, bulk density,…

Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). SSEBop has two key model parameters that define the model boundary conditions. The FANO algorithm computes the wet-bulb boundary condition using a linear FANO Equation relating surface temperature, surface psychrometric constant, and the Normalized Difference Vegetation Index (NDVI). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: (1) Google Earth Engine (GEE) and (2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration (ETa) estimates with AmeriFlux eddy covariance (EC) and water…

Classification of remotely sensed imagery for reliable land use and land cover (LULC) remains a challenge in areas where spectrally similar LULC features occur. For example, bare soils of harvested crop fields in agricultural watersheds exhibit spectral characteristics similar to high-intensity developed regions and impede an accurate classification. The goal of this study is to improve the accuracy of LULC classification of satellite imagery for the Big Sunflower River Watershed, Mississippi using ancillary data, multiple classification methods, and a post-classification correction (PCC). To determine the best approach, the methodology was applied to Landsat 8 Operational Land Imager (OLI) imagery during the growing season and post-harvest. Imagery for the growing season was acquired on 25 August 2015, and post-harvest was acquired on 7 January 2018. Three classification methods were applied: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). LULC imagery was classified as open water, woody wetlands, harvested crop, rangeland, cultivated crop,…

The frequent occurrence and spread of wildfires pose a serious threat to the ecological environment and urban development. Therefore, assessing regional wildfire susceptibility is crucial for the early prevention of wildfires and formulation of disaster management decisions. However, current research on wildfire susceptibility primarily focuses on improving the accuracy of models, while lacking in-depth study of the causes and mechanisms of wildfires, as well as the impact and losses they cause to the ecological environment and urban development. This situation not only increases the uncertainty of model predictions but also greatly reduces the specificity and practical significance of the models. We propose a comprehensive evaluation framework to analyze the spatial distribution of wildfire susceptibility and the effects of influencing factors, while assessing the risks of wildfire damage to the local ecological environment and urban development. In this study, we used wildfire information from the period 2013–2022 and data from 17 susceptibility factors in the city of Guilin as…

Precise yield predictions are useful for implementing precision agriculture technologies and making better decisions in crop management. Convolutional neural networks (CNNs) have recently been used to predict crop yields in unmanned aerial vehicle (UAV)-based remote sensing studies, but weather data have not been considered in modeling. The aim of this study was to explore the potential of multimodal deep learning on rice yield prediction accuracy using UAV multispectral images at the heading stage, along with weather data. The effects of the CNN architectures, layer depths, and weather data integration methods on the prediction accuracy were evaluated. Overall, the multimodal deep learning model integrating UAV-based multispectral imagery and weather data had the potential to develop more precise rice yield predictions. The best models were those trained with weekly weather data. A simple CNN feature extractor for UAV-based multispectral image input data might be sufficient to predict crop yields accurately. However, the spatial patterns of the predicted yield maps differed from…

Autonomous localization and navigation, as an essential research area in robotics, has a broad scope of applications in various scenarios. To widen the utilization environment and augment domain expertise, simultaneous localization and mapping (SLAM) in underwater environments has recently become a popular topic for researchers. This paper examines the key SLAM technologies for underwater vehicles and provides an in-depth discussion on the research background, existing methods, challenges, application domains, and future trends of underwater SLAM. It is not only a comprehensive literature review on underwater SLAM, but also a systematic introduction to the theoretical framework of underwater SLAM. The aim of this paper is to assist researchers in gaining a better understanding of the system structure and development status of underwater SLAM, and to provide a feasible approach to tackle the underwater SLAM problem.

Recently, the rapid development of deep learning has greatly improved the performance of image classification. However, a central problem in hyperspectral image (HSI) classification is spectral uncertainty, where spectral features alone cannot accurately and robustly identify a pixel point in a hyperspectral image. This paper presents a novel HSI classification network called MS-RPNet, i.e., multiscale superpixelwise RPNet, which combines superpixel-based S3-PCA with two-dimensional singular spectrum analysis (2D-SSA) based on the Random Patches Network (RPNet). The proposed frame can not only take advantage of the data-driven method, but can also apply S3-PCA to efficiently consider more global and local spectral knowledge at the super-pixel level. Meanwhile, 2D-SSA is used for noise removal and spatial feature extraction. Then, the final features are obtained by random patch convolution and other steps according to the cascade structure of RPNet. The layered extraction superimposes the different sparial information into multi-scale spatial features, which complements the features…

Horticulture and agriculture are considered as the important pillars of any economy. Current technological advancements have led to the development of several new technologies which are useful in atomizing the agriculture process. Apple farming has a significant role in Italy’s agriculture domain where manual labor is widely employed for apple picking which can be replaced by automated robot mechanisms. However, these mechanisms are based on computer vision methods. These methods focus on detection, localization and tracking the apple fruits in given video frames. Later, appropriate actions can be taken to enhance the production and harvesting. Several techniques have been presented for apple detection, but complex background, noise and image blurriness are the major causes which can deteriorate the performance of the system. Thus, in this work, we present a deep learning-based scheme to detect apples which uses Yolov5 architecture in live apple farm images. We further improve the Yolov5 architecture by incorporating an adaptive pooling scheme and attribute augmentation model. This…

Knowledge of the precise water depth in shallow areas of the ocean is of great significance to the safe navigation of ships and hydrographic surveying. Compared with traditional bathymetry, satellite remote sensing for water depth determination makes it possible to cover large areas by dynamic observation. In this paper, we conducted an optically shallow water bathymetric inversion study using a Stumpf empirical model, random forest model, neural network model, and support vector machine model based on Sentinel-2 satellite images and Ganquan Dao measured bathymetry data. We compared and analyzed the inversion results based on the empirical model and different machine learning models. The results show that the Stumpf empirical and machine learning models are capable of inverting optically shallow water depth. Moreover, the machine learning models had better fitting ability than the Stumpf empirical model with a sufficient number of samples, especially when the water depth was greater than 15 m. In addition, the random forest model had the highest overall accuracy among these models,…

There is an urgent need to explore and analyze new aquifer recharge potential zones (ARPZ) in arid regions exposed mainlyto hard rock local aquifers, whether fractured or non-fractured, for investment and fulfillment of the Saudi Vision 2030. Over-pumping, seawater intrusion, climatological changes, population growth, lack of traditional water supplies, expensive desalinized water, and excessive evaporation have characterized the Duba region of Tabuk province of Saudi Arabia (SA). Aquifer productivity and potentiality are affected by surface geology, rainfall, lineament density, drainage density, slope, elevation, soil, and normalized difference vegetation index (NDVI). This study aims to demarcate the ARPZ using integrated remote sensing and geographic information system (GIS) and (RS) approaches. The relative importance of each parameter was determined based on its impact on the aquifer’s potential through the analytical hierarchical process (AHP). The ARPZ zones are categorized into five classes starting from very low to very high potentiality. Southern, western, and northern…

In the 21st Century, disasters have severe negative impacts on cities worldwide. Given the significant casualties and property damage caused by disasters, it is necessary for disaster management organizations and the public to enhance urban disaster management. As an effective method, BIM (Building Information Modeling)–GIS (Geographic Information System) integration can significantly improve urban disaster management. Despite the significance of BIM–GIS integration, there is rarely the adoption of BIM–GIS integration in urban disaster management, which significantly hinders the development of the quality and efficiency of urban disaster management. To enhance urban disaster management and reduce the negative impact caused by disasters, this study is developed to perform a systematic review of the utilization of BIM–GIS integration in urban disaster management. Through the systematic review, the capabilities of BIM–GIS integration in disaster prevention and mitigation, disaster response, and post-disaster recovery are reviewed and analyzed. Moreover, the data acquisition approaches,…

The deformation characteristics and instability patterns of rotational landslides are complicated. Such landslides are large and occur continuously, seriously threatening people’s lives. We used interferometry synthetic aperture radar (InSAR), digital elevation models of difference (DODs), numerical simulations, and other techniques for analyzing the topographic changes, surface deformation and movement process before, during and after a landslide. Based on the high-resolution terrain data before and after the landslide, the topographic changes were analyzed, and the active zone of the landslide was identified. The areas of the topographic changes were mainly located on the main scarp, toe and secondary landslides. The topographic changes were influenced by rainfall and rill erosion. The geomorphologically-guided InSAR interpretation method was applied to explore the displacement pattern. The deformation area in the middle of the landslide coincided with the secondary landslides. A time-series InSAR analysis revealed the dynamic evolution of the deformation before and after the…

Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in image processing, signal recognition, and object detection, has facilitated scientific research in EDA. This paper analyses 204 articles through a systematic literature review to investigate the status quo, development, and challenges of DL for EDA. The paper first examines the distribution characteristics and trends of the two categories of EDA assessment objects, including earthquakes and secondary disasters as disaster objects, buildings, infrastructure, and areas as physical objects. Next, this study analyses the application distribution, advantages, and disadvantages of the three types of data (remote sensing data, seismic data, and social media data) mainly involved in these studies. Furthermore, the review identifies the characteristics and application of six commonly used DL models in EDA, including convolutional neural network (CNN), multi-layer perceptron (MLP), recurrent neural network (RNN),…

The expansion of mountainous urban areas and road networks can influence the terrain, vegetation, and material characteristics, thereby altering the susceptibility of landslides. Understanding the relationship between human engineering activities and landslide occurrence is of great significance for both landslide prevention and land resource management. In this study, an analysis was conducted on the landslide caused by Typhoon Megi in 2016. A representative mountainous area along the eastern coast of China—characterized by urban development, deforestation, and severe road expansion—was used to analyze the spatial distribution of landslides. For this purpose, high-precision Planet optical remote sensing images were used to obtain the landslide inventory related to the Typhoon Megi event. The main innovative features are as follows: (i) the newly developed patch generating land-use simulation (PLUS) model simulated and analyzed the driving factors of land-use land-cover (LULC) from 2010 to 2060; (ii) the innovative stacking strategy combined three strong ensemble models—Random…

Aboveground biomass (AGB) of shrub communities in the desert is a basic quantitative characteristic of the desert ecosystem and an important index to measure ecosystem productivity and monitor desertification. An accurate and efficient method of predicting the AGB of a shrub community is essential for studying the spatial patterns and ecological functions of the desert region. Even though there are several entries in the literature on the AGB prediction of desert shrub communities using remote sensing data, the applicability and accuracy of airborne LiDAR data and prediction methods have not been well studied. We first extracted the elevation, density and intensity variables based on the airborne LiDAR, and then sample plot-level AGB prediction models were constructed using the parametric regression (nonlinear regression) and nonparametric methods (Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting Machine, and Multivariate adaptive regression splines). We evaluated accuracies of all the AGB prediction models we developed based on the fit statistics.…

Current deep learning-based change detection approaches mostly produce convincing results by introducing attention mechanisms to traditional convolutional networks. However, given the limitation of the receptive field, convolution-based methods fall short of fully modelling global context and capturing long-range dependencies, thus insufficient in discriminating pseudo changes. Transformers have an efficient global spatio-temporal modelling capability, which is beneficial for the feature representation of changes of interest. However, the lack of detailed information may cause the transformer to locate the boundaries of changed regions inaccurately. Therefore, in this article, a hybrid CNN-transformer architecture named CTCANet, combining the strengths of convolutional networks, transformer, and attention mechanisms, is proposed for high-resolution bi-temporal remote sensing image change detection. To obtain high-level feature representations that reveal changes of interest, CTCANet utilizes tokenizer to embed the features of each image extracted by convolutional network into a…

Cracks in concrete surfaces are one of the most prominent causes of the degradation of concrete structures such as bridges, roads, buildings, etc. Hence, it is very crucial to detect cracks at an early stage to inspect the structural health of the concrete structure. To solve the drawbacks of manual inspection, Image Processing Techniques (IPTs), especially those based on Deep Learning (DL) methods, have been investigated for the past few years. Due to the groundbreaking development of this field, researchers have devoted their endeavors to detecting cracks using DL-based IPTs and as a result, the techniques have given answers to many challenging problems. However, to the best of our knowledge, a state-of-the-art systematic review paper is lacking in this field that would present a scientometric analysis as well as a critical survey of the existing works to document the research trends and summarize the prominent IPTs for detecting cracks in concrete structures. Therefore, this article comes forward to spur researchers with a systematic review of the relevant literature, which will…

The rapid advancement of remote sensing technology has significantly enhanced the temporal resolution of remote sensing data. Multitemporal remote sensing image classification can extract richer spatiotemporal features. However, this also presents the challenge of mining massive data features. In response to this challenge, deep learning methods have become prevalent in machine learning and have been widely applied in remote sensing due to their ability to handle large datasets. The combination of remote sensing classification and deep learning has become a trend and has developed rapidly in recent years. However, there is a lack of summary and discussion on the research status and trends in multitemporal images. This review retrieved and screened 170 papers and proposed a research framework for this field. It includes retrieval statistics from existing research, preparation of multitemporal datasets, sample acquisition, an overview of typical models, and a discussion of application status. Finally, this paper discusses current problems and puts forward prospects for the future from…

Early-season crop mapping and information extraction is essential for crop growth monitoring and yield prediction, and it facilitates agricultural management and rapid response to agricultural disasters. However, training classifiers by remote sensing classification features for early crop prediction can be challenging, as early-season mapping can only use remote sensing image data during part of the crop growth period. In order to overcome this limitation, this study takes the Sanjiang Plain as an example to investigate the earliest identification time of rice, maize and soybean based on Sentinel-2 time-series data and the random forest classification algorithm. Crop information extraction was then performed. Following the analysis of the remote sensing classification features by the random forest importance approach and the subsequent normalization, the optimal features greater than or equal to 0.5 have yielded quite results in early crop mapping, and their overall accuracy was the highest in early-season mapping. The overall accuracy was observed to improve by 5% for 10 to 20…

A technical and scientific overview regarding satellite-derived bathymetry (SDB)—one of the most promising and relatively cheap methods of shallow water depth determination—is presented. The main goal of the article is to present information about the possibilities of the SDB method to meet the demanding standard of bathymetric measurements in coastal mapping areas up to 20 m deep, i.e., up to depth areas where the largest number of ports and access waterways are located, as obtained using the bibliometric analysis. The Web of Science (WoS) and Scopus scientific databases, as well as R studio applications Bibliometrix and Biblioshiny, were used for scientific analysis. The bibliometric analysis presents the quantitative aspects of producing and disseminating scientific and professional articles with SDB as their topic. Therefore, the purpose of this study was to give the academic community an insight into the current knowledge about the SDB method, its achievements and shortcomings. The results of the bibliometric analysis of articles dealing with SDB show that most authors use…

Change detection is a technique that can observe changes in the surface of the earth dynamically. It is one of the most significant tasks in remote sensing image processing. In the past few years, with the ability of extracting rich deep image features, the deep learning techniques have gained popularity in the field of change detection. In order to obtain obvious image change information, the attention mechanism is added in the decoder and output stage in many deep learning-based methods. Many of these approaches neglect to upgrade the ability of the encoders and the feature extractors to extract the representational features. To resolve this problem, this study proposes a deep multi-scale multi-attention siamese transformer network. A special contextual attention module combining a convolution and self-attention module is introduced into the siamese feature extractor to enhance the global representation ability. A lightly efficient channel attention block is added in the siamese feature extractor to obtain the information interaction among different channels. Furthermore, a…