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Precise mapping and continuous monitoring of fine-scale surface albedo are indispensable for assessing and optimizing renewable energy sources. Understanding the variations in surface reflectivity is crucial in capturing the full potential of renewable technologies, as it directly impacts the efficiency of harnessing solar energy for sustainable power generation. Satellite remote sensing stands out as the sole practical approach for estimating surface albedo at both regional and global scales. Although there are different methods to calculate albedo from satellite data, most satellite products result in low spatial resolution for large heterogeneous areas, such as urban and peri-urban areas. This paper evaluates and compares several methodologies to calculate surface albedo from Landsat 8 imagery. As a result, a new methodology for estimating surface albedo for heterogeneous areas has been proposed. The new methodology has been compared with direct and indirect albedo measurements, improving the original methodologies of Baldinelli and Silva with respect to the Arctic-Boreal Vulnerability Experiment (ABOVE) albedo images by reducing the RMSE by 85% and 52%, respectively. [ABSTRACT FROM AUTHOR]

The ground is typically hidden by cloud and snow in satellite images, which have a similar visible spectrum and complex spatial distribution characteristics. The detection of cloud and snow is important for increasing image availability and studying climate change. To address the issues of the low classification accuracy and poor generalization effect by the traditional threshold method, as well as the problems of the misdetection of overlapping regions, rough segmentation results, and a loss of boundary details in existing algorithms, this paper designed a Multi-level Attention Interaction Network (MAINet). The MAINet uses a modified ResNet50 to extract features and introduces a Detail Feature Extraction module to extract multi-level information and reduce the loss of details. In the last down-sampling, the Deep Multi-head Information Enhancement module combines a CNN and a Transformer structure to make deep semantic features more distinct and reduce redundant information. Then, the Feature Interactive and Fusion Up-sampling module enhances the information extraction of deep and shallow information and, then, guides and aggregates each to make the learned semantic features more comprehensive, which can better recover remote sensing images and increase the prediction accuracy. The MAINet model we propose performed satisfactorily in handling cloud and snow detection and segmentation tasks in multiple scenarios. Experiments on related data sets also showed that the MAINet algorithm exhibited the best performance. [ABSTRACT FROM AUTHOR]

In recent years, there has been significant progress in object detection within the domain of natural images. However, the field of satellite remote sensing images has consistently presented challenges due to its significant scale variations and complex background interference. Achieving satisfactory results by directly applying conventional image object detection models has proven to be difficult. To address these challenges, this paper introduces BA-YOLO, an improved version of the YOLOv8 object detection model. It incorporates several notable enhancements. Firstly, to fuse an increased number of features more effectively, we introduce the design concept of a higher-performing Bi-directional Feature Pyramid Network (BiFPN). Secondly, to retain sufficient global contextual information, we integrated a module in BA-YOLO that combines multi-head self-attention and convolutional networks. Finally, we employed various data augmentation techniques such as Mixup, Cutout, Mosaic, and multi-scale training to enhance the model's accuracy and robustness. Experimental results demonstrate that BA-YOLO outperforms state-of-the-art detectors and has been evaluated on the DOTA dataset. BA-YOLO achieves a mean average precision (mAP) of 0.722 on the DOTA dataset. [ABSTRACT FROM AUTHOR]

The classification of ship images has become a significant area of research within the remote sensing community due to its potential applications in maritime security, traffic monitoring, and environmental protection. Traditional monitoring methods like the Automated Identification System (AIS) and the Constant False Alarm Rate (CFAR) have their limitations, such as challenges with sea clutter and the problem of ships turning off their transponders. Additionally, classifying ship images in remote sensing is a complex task due to the spatial arrangement of geospatial objects, complex backgrounds, and the resolution limitations of sensor platforms. To address these challenges, this paper introduces a novel approach that leverages a unique dataset termed Heterogeneous Ship data and a new technique called the Spatial–Channel Attention with Bilinear Pooling Network (SCABPNet). First, we introduce the Heterogeneous Ship data, which combines Synthetic Aperture Radar (SAR) and optical satellite imagery, to leverage the complementary features of the SAR and optical modalities, thereby providing a richer and more-diverse set of features for ship classification. Second, we designed a custom layer, called the Spatial–Channel Attention with Bilinear Pooling (SCABP) layer. This layer sequentially applies the spatial attention, channel attention, and bilinear pooling techniques to enhance the feature representation by focusing on extracting informative and discriminative features from input feature maps, then classify them. Finally, we integrated the SCABP layer into a deep neural network to create a novel model named the SCABPNet model, which is used to classify images in the proposed Heterogeneous Ship data. Our experiments showed that the SCABPNet model demonstrated superior performance, surpassing the results of several state-of-the-art deep learning models. SCABPNet achieved an accuracy of 97.67% on the proposed Heterogeneous Ship dataset during testing. This performance underscores SCABPNet's capability to focus on ship-specific features while suppressing background noise and feature redundancy. We invite researchers to explore and build upon our work. [ABSTRACT FROM AUTHOR]

The Baikal Natural Territory (BNT) and Khuvsgul region have similar environmental problems. It is relevant to carry out integrated scientific studies and monitor the state of the components of the natural environment in these areas. This paper presents the results of a number of joint Russian–Mongolian projects aimed at studying and developing new methods and technologies for integrated environmental monitoring and prediction. A digital platform has been created to support scientific research and environmental monitoring. This platform makes it possible to collect, store, process, and analyze large arrays of heterogeneous spatiotemporal data and predict environmental situations using a set of mathematical and information models, services, and machine learning methods. The authors have also developed methods and web services for environmental monitoring based on the processing of Earth remote sensing (RS) data. A technology for classifying multispectral Sentinel-2 satellite images has been created that makes it possible to distinguish 12 classes of the land cover using artificial intelligence methods. A service for monitoring the state of the atmosphere over large areas has been created based on the processing of Sentinel-5P satellite data. This service makes it possible to display the concentrations of SO2, NO2, CO, CH4, H2O, O3, formaldehydes, and aerosols in the air. [ABSTRACT FROM AUTHOR]

The future of agriculture may be fully realized using knowledge of artificial intelligence (AI) accumulated by human expertise. With increasing climate change and population pressure challenges, new technologies, such as AI and RS in vital sectors like agriculture is urgently required to assess agricultural suitability, optimize productivity, and then support sustainable development. This paper presents bibliometric and review analyses on AI and geospatial analysis applied to the agricultural field. Based on the prestigious database, Scopus, thousands of documents were retrieved in the period 1992–2021 and processed using Scopus online tool and VOSviewer software. The results show a growing trend in the number of publications during this period. Earth and Planetary Sciences is the top domain, remote sensing (RS) is the leading source, while the United States and India are the most influential countries. RS, machine learning (ML), GIS, land-use, decision-making, decision trees, and satellite imagery are the most occurring topics in this search. Otherwise, research in this field is turning toward ML and deep learning techniques, whereas agricultural robots and antennas are the top trending tools. The current study provides specific information about the connections between artificial intelligence, geospatial analysis, and agriculture in the period 1992–2021, the fruitful period where the AI coupled to geospatial technologies is in full development. [ABSTRACT FROM AUTHOR]

Semantic segmentation of high-resolution remote sensing images is important in land cover classification, road extraction, building extraction, water extraction, etc. However, high-resolution remote-sensing images have a lot of details. Due to the fixed receptive field of convolution blocks, it is impossible to model the correlation of global features. In addition, complex fusion methods cannot integrate spatial and global context information. In order to solve these problems, this paper proposes a cross-linear attention network (CLANet) to capture spatial and context information in images. The structure consists of a spatial branch and a context branch. The spatial branch is constructed by stacked convolution to better capture spatial information. The context branch models the global information based on the transformer deformation module. In addition, to effectively fuse spatial and context information, this paper also designs a feature fusion module (FFM), which uses a cross-linear attention mechanism for feature aggregation. Finally, this paper conducts many experiments on the ISPRS Vaihingen and the ISPRS Potsdam datasets. Among them, 82.28% of mIoU achieves on the ISPRS Vaihingen dataset. The experimental results show that CLANet has better performance and effect than the methods in recent years. [ABSTRACT FROM AUTHOR]

Change detection in high-resolution satellite images is essential to understanding the land surface (e.g. agriculture and urban change) or maritime surface (e.g. oil spilling). Many deep-learning-based change detection methods have been proposed to enhance the performance of the classical techniques. However, the massive amount of satellite images and missing ground-truth images are still challenging concerns. In this paper, we propose a supervised deep network for change detection in bi-temporal remote sensing images. We feed multi-level features from convolutional networks of two images (feature-extraction) into one architecture (feature-difference) to have better shape and texture properties using a dual attention module We also utilize a multi-scale dice coefficient error function to decrease overlapping between changed and background pixel. The network is applied to public datasets (ACD, SYSU-CD and OSCD). We compare the proposed architecture with various attention modules and loss functions to verfiy the performance of the proposed method. We also compare the proposed method with the stateof-the-art methods in terms of three metrics: precision, recall and F1-score. The experimental outcomes confirm that the proposed method has good performance compared to benchmark methods. [ABSTRACT FROM AUTHOR]

In the second half of the twentieth century, after the Second World War, a considerable migration from inland to coastal areas was observed in many territories, especially in the Mediterranean. This process has caused the expansion of existing coastal settlements and the construction of numerous new towns, often substituting beaches and dune systems. All this has often altered the equilibrium conditions of several coastal ecosystems thereby triggering shoreline erosion processes. This issue is particularly important in territories such as Calabria, a region of southern Italy subject to considerable anthropogenic pressures and characterized by over 750 km of coast. The paper analyses the correlations between shoreline erosion and anthropogenic pressure in Calabria. For this purpose, historical and current cartographic Open Data, such as shapefiles, cartography, and satellite imagery, were analysed using QGIS. The analysis showed a strong correlation between the anthropogenic pressures, in terms of the expansion of the inhabited centres and, therefore, of the man-made areas near the shoreline, and the triggering of shoreline erosion processes. Finally, this analysis is of interest in the field of coastal area planning and management, and it is easily applicable and replicable as it is based on Open Data and free software. [ABSTRACT FROM AUTHOR]

Remote sensing involves actions to obtain information about an area located on Earth. In the Amazon region, the presence of clouds is a common occurrence, and the visualization of important terrestrial information in the image, like vegetation and temperature, can be difficult. In order to estimate land surface temperature (LST) and the normalized difference vegetation index (NDVI) from satellite images with cloud coverage, the inpainting approach will be applied to remove clouds and restore the image of the removed region. This paper proposes the use of the neural network LaMa (large mask inpainting) and the scalable model named Big LaMa for the automatic reconstruction process in satellite images. Experiments are conducted on Landsat-8 satellite images of the Amazon rainforest in the state of Acre, Brazil. To evaluate the architecture's accuracy, the RMSE (root mean squared error), SSIM (structural similarity index) and PSNR (peak signal-to-noise ratio) metrics were used. The LST and NDVI of the reconstructed image were calculated and compared qualitatively and quantitatively, using scatter plots and the chosen metrics, respectively. The experimental results show that the Big LaMa architecture performs more effectively and robustly in restoring images in terms of visual quality. And the LaMa network shows minimal superiority for the measured metrics when addressing medium marked areas. When comparing the results achieved in NDVI and LST of the reconstructed images with real cloud coverage, great visual results were obtained with Big LaMa. [ABSTRACT FROM AUTHOR]

Remote sensing image semantic segmentation plays a crucial role in various fields, such as environmental monitoring, urban planning, and agricultural land classification. However, most current research primarily focuses on utilizing the spatial and spectral information of single-temporal remote sensing images, neglecting the valuable temporal information present in historical image sequences. In fact, historical images often contain valuable phenological variations in land features, which exhibit diverse patterns and can significantly benefit from semantic segmentation tasks. This paper introduces a semantic segmentation framework for satellite image time series (SITS) based on dilated convolution and a Transformer encoder. The framework includes spatial encoding and temporal encoding. Spatial encoding, utilizing dilated convolutions exclusively, mitigates the loss of spatial accuracy and the need for up-sampling, while allowing for the extraction of rich multi-scale features through a combination of different dilation rates and dense connections. Temporal encoding leverages a Transformer encoder to extract temporal features for each pixel in the image. To better capture the annual periodic patterns of phenological phenomena in land features, position encoding is calculated based on the image's acquisition date within the year. To assess the performance of this framework, comparative and ablation experiments were conducted using the PASTIS dataset. The experiments indicate that this framework achieves highly competitive performance with relatively low optimization parameters, resulting in an improvement of 8 percentage points in the mean Intersection over Union (mIoU). [ABSTRACT FROM AUTHOR]

This paper presents an R-based approach to mapping dynamics of the flooded areas in the Inner Niger Delta (IND), Mali, using time series analysis of Landsat 8–9 satellite images. As the largest inland wetland in West Africa, the habitats of IND offers high potential for biodiversity of the flood-dependent eco systems. IND is one of the most productive areas in West Africa. Mapping flooded areas based on satellite images enables to provide strategies for land management and rice planting and modelling vegetation types of IND. Our approach is based on using libraries of R programming language for processing six Landsat images, and each image was taken on November from 2013 to 2022. By capturing spatial and temporal structures of the satellite images on 2013, 2015, 2018, 2020, 2021 and 2022, the remote sensing data are combined to yield estimates of landscape dynamics that is temporally coherent, while helping to analyse fluctuations of spatial extent in fluvial wetlands caused by the hydrological processes of seasonal flooding. Further, by allowing packages of R to support image processing, an approach to mapping vegetation by NDVI, SAVI and EVI indices and visualising changes in distribution of different land cover classes over time is realised. In this context, processing Earth observation data by advanced scripting tools of R language provides new insights into complex interlace of climate-hydrological processes and vegetation responses. Our study contributes to the sustainable management of natural resources and improving knowledge on the functioning of IND ecosystems in Mali, West Africa. [ABSTRACT FROM AUTHOR]

Fire accidents cause alarming damage. They result in the loss of human lives, damage to property, and significant financial losses. Early fire ignition detection systems, particularly smoke detection systems, play a crucial role in enabling effective firefighting efforts. In this paper, a novel DL (Deep Learning) method, namely BoucaNet, is introduced for recognizing smoke on satellite images while addressing the associated challenging limitations. BoucaNet combines the strengths of the deep CNN EfficientNet v2 and the vision transformer EfficientFormer v2 for identifying smoke, cloud, haze, dust, land, and seaside classes. Extensive results demonstrate that BoucaNet achieved high performance, with an accuracy of 93.67%, an F1-score of 93.64%, and an inference time of 0.16 seconds compared with baseline methods. BoucaNet also showed a robust ability to overcome challenges, including complex backgrounds; detecting small smoke zones; handling varying smoke features such as size, shape, and color; and handling visual similarities between smoke, clouds, dust, and haze. [ABSTRACT FROM AUTHOR]

Recently, deep learning-based object detection techniques have arisen alongside time-consuming training and data collection challenges. Although few-shot learning techniques can boost models with few samples to lighten the training load, these approaches still need to be improved when applied to remote-sensing images. Objects in remote-sensing images are often small with an uncertain scale. An insufficient amount of samples would further aggravate this issue, leading to poor detection performance. This paper proposes a Gaussian-scale enhancement (GSE) strategy and a multi-branch patch-embedding attention aggregation (MPEAA) module for cross-scale few-shot object detection to address this issue. Our model can enrich the scale information of an object and learn better multi-scale features to improve the performance of few-shot object detectors on remote sensing images. [ABSTRACT FROM AUTHOR]

Total phosphorus (TP) and total nitrogen (TN) represent the primary water quality parameters indicative of the eutrophication status in the mainstream of the Yangtze River. Nowadays, satellite remote sensing offers an economical and efficient method for monitoring the water environment with a broad geographical scope, while single satellite and traditional methods are still limited. In this paper, inversion models of TN and TP are constructed and evaluated based on the neural networks (NNs) algorithm and random forest (RF) algorithm in the upper, middle, and lower reaches of the Yangtze River, respectively. Subsequently, the monthly variations of TN and TP concentrations are estimated and analyzed in the mainstream of the Yangtze River using Landsat-8 and Sentinel-2 satellites images from January 2016 to December 2022. The results show that the NNs model exhibits better estimation performance than the RF model within the study area. The accuracy of the TN model varies across different sections, with R2 values of 0.70 in the upstream, 0.67 in the midstream, and 0.74 in the downstream, accompanied by respective RMSE values of 0.21 mg/L, 0.21 mg/L, and 0.23 mg/L. Similarly, the TP model exhibits varying accuracy in different sections, with R2 values of 0.71 in the upstream, 0.69 in the midstream, and 0.78 in the downstream, along with corresponding RMSE values of 0.008 mg/L, 0.012 mg/L, and 0.008 mg/L. From 2016 to 2022, the concentrations of TN and TP in the mainstream of the Yangtze River exhibited an overall downward trend, with TN decreasing by 13.7% and TP decreasing by 46.2%. Furthermore, this study also gives the possible causes of water quality changes in the mainstream of the Yangtze River with a specific focus on hydrometeorological factors. [ABSTRACT FROM AUTHOR]

Several cities have been greatly affected by economic crisis, unregulated gentrification, and the pandemic, resulting in increased vacancy rates. Abandoned buildings have various negative implications on their neighborhoods, including an increased chance of fire and crime and a drastic reduction in their monetary value. This paper focuses on the use of satellite data and machine learning to provide insights for businesses and policymakers within Greece and beyond. Our objective is two-fold: to provide a comprehensive literature review on recent results concerning the opportunities offered by satellite images for business intelligence and to design and implement an open-source software system for the detection of abandoned or disused buildings based on nighttime lights and built-up area indices. Our preliminary experimentation provides promising results that can be used for location intelligence and beyond. [ABSTRACT FROM AUTHOR]

Due to growing demand for ground-truth in deep learning-based remote sensing satellite image fusion, numerous approaches have been presented. Of these approaches, Wald's protocol is the most commonly used. In this paper, a new workflow is proposed consisting of two main parts. The first part targets obtaining the ground-truth images using the results of a pre-designed and well-tested hybrid traditional fusion method. This method combines the Gram-Schmidt and curvelet transform techniques to generate accurate and reliable fusion results. The second part focuses on the training of a proposed deep learning model using rich and informative data provided by the first stage to improve the fusion performance. The demonstrated deep learning model relies on a series of residual dense blocks to enhance network depth and facilitate the effective feature learning process. These blocks are designed to capture both low-level and high-level information, enabling the model to extract intricate details and meaningful features from the input data. The performance evaluation of the proposed model is carried out using seven metrics such as peak-signal-to-noise-ratio and quality without reference. The experimental results demonstrate that the proposed approach outperforms state-of-the-art methods in terms of image quality. It also exhibits the robustness and powerful nature of the proposed approach which has the potential to be applied to many remote sensing applications in agriculture, environmental monitoring, and change detection. [ABSTRACT FROM AUTHOR]

ABSTRACT Zhang, X.;Wu Q.Y., and Chen Y.X., 2018. Multiple Satellite Image Data Coordinative Computation Technology and Application in Coast Region. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 1436–1440. Coconut Creek (Florida), ISSN 0749-0208. Remote sensing data from different sensors have different advantages in wave band, space resolution, time resolution, imaging mode and different adaptabilities. The ecological environment and dynamic change monitoring of coast zones requires mutual image data coordinative computation. First, the earth observation net is introduced which can provide the cooperative observation ability. Second, this paper studies coordinative computing method of multi-source images which integrates multiple imaging advantages. This paper tests fine extraction and quantitative computing for pattern patches of the non-point source polluted scenery centralized layout in the coast zone, Fujian province, China. The results show that this method improves spatio-temporal recognition precision and quantitation precision. [ABSTRACT FROM AUTHOR]

Preservation and revitalization of our cultural and natural heritage has a vital role not only in understanding our past, but also in our future development as humans. This paper aims to present the current directions and the use of remote sensing satellite imagery in identifying different terrain forms, routes, objectives and analyzing their evolution over time both for cultural and environmental purposes. A case study was carried out to investigate the contribution of processing remote sensing images to the research and detection of an important former commercial connection between Europe and Asia, the old Silk Road. The analysis of the multi-spectral remote sensing data was performed with the help of innovative programs developed by PIESAT Information Technology Co Ltd. These softwares use an object-oriented analyzing method and have made a qualitative leap in image information extraction. The actuality of the paper is given by the fact that preserving cultural heritage and historical sites represents an important issue that must be taken into account when sustainable development projects are required for environmental policies. [ABSTRACT FROM AUTHOR]

Remote sensing image segmentation has large uncertainty related to the heterogeneity of similar objects and complex spectrum in satellite images, causing the traditional segmentation methods to be greatly limited. Existing semantic segmentation methods represented by deep learning have made breakthrough progress. However, traditional deep learning methods, such as deep convolution neural network, are a completely deterministic model, which cannot describe the uncertainty of remote sensing image well. To solve this problem, a new deep neural network combined with fuzzy logic units is proposed in this paper, called RSFCNN (Remote Sensing image segmentation with Fuzzy Convolutional Neural Network). The network integrates convolution units and fuzzy logic units. Convolution units are used to extract discriminant features with different proportions, thus providing comprehensive information for pixel-level remote sensing image segmentation. Fuzzy logic units are used to deal with various uncertainties and provide more reliable segmentation results, and each unit handles the feature maps at a particular image scale by Gaussian blur function. Experiments were carried out on two data sets, and the results showed that RSFCNN has higher segmentation accuracy and better performance than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Remote sensing has become an important technique for monitoring vegetation phenology. The quality of remote-sensing images and derived products is key to successful extraction of vegetation phenophases. There is a need to develop quality management methods to evaluate the data uncertainty and assist the removal of the noises. This paper developed a shape quality assurance score threshold (SQAT) method which accounts for the trend in satellite-derived vegetation index associated with the process of vegetation growth. The proposed method was tested on six widely used methods for extracting vegetation phenophases. Results showed that the SQAT method can effectively identify noises in the vegetation index time series and improve the accuracies of estimated start of season (SOS) and end of season (EOS) of the six methods. After removal of identified noises, the Pearson correlation coefficient (r) averagely increased by 8% for SOS, and 11% for EOS. Regression analyses of vegetation phenophases between the PhenoCam observations and MCD12Q2 product showed that the proposed method performs better than the QA score of MCD12Q2 for quality management. This paper provides promising method for quality management; it has the potential to reduce the uncertainty of the vegetation index time series that can support studies of vegetation phenology monitoring. [ABSTRACT FROM AUTHOR]

Accurate detection of cloud cover is an important task in the field of Remote Sensing of the environment. Currently, a lot of development is going on in this field by using various methods. Some of the methods apply concepts of machine learning (ML) whereas some apply deep learning. Since the accuracy of ML being lower than deep learning, the latter is preferred. This paper also uses the method of deep learning to detect cloud cover using images of satellite. This paper proposes a modified U-Net based deep learning model for cloud cover detection in satellite images. The model proposed is not as accurate as the original model, but it compensates for it by reducing the time for learning. The accuracy of the model came out to be 89.73%. [ABSTRACT FROM AUTHOR]

The rapid development of remote sensing technology has brought about a sharp increase in the amount of remote sensing image data. However, due to the satellite's limited hardware resources, space, and power consumption constraints, it is difficult to process massive remote sensing images efficiently and robustly using the traditional remote sensing image processing methods. Additionally, the task of satellite-to-ground target detection has higher requirements for speed and accuracy under the conditions of more and more remote sensing data. To solve these problems, this paper proposes an extremely efficient and reliable acceleration architecture for forward inference of the YOLOX-s detection network an on-orbit FPGA. Considering the limited onboard resources, the design strategy of the parallel loop unrolling of the input channels and output channels is adopted to build the largest DSP computing array to ensure a reliable and full utilization of the limited computing resources, thus reducing the inference delay of the entire network. Meanwhile, a three-path cache queue and a small-scale cascaded pooling array are designed, which maximize the reuse of on-chip cache data, effectively reduce the bandwidth bottleneck of the external memory, and ensure an efficient computing of the entire computing array. The experimental results show that at the 200 MHz operating frequency of the VC709, the overall inference performance of the FPGA acceleration can reach 399.62 GOPS, the peak performance can reach 408.4 GOPS, and the overall computing efficiency of the DSP array can reach 97.56%. Compared with the previous work, our architecture design further improves the computing efficiency under limited hardware resources. [ABSTRACT FROM AUTHOR]

Object detection has made significant progress in computer vision. However, challenges remain in detecting small, arbitrarily oriented, and densely distributed objects, especially in aerial remote sensing images. This paper presents MATDet, an end-to-end encoder-decoder detection network based on the Transformer designed for oriented object detection. The network employs multi-layer feature aggregation and rotated anchor matching methods to improve oriented small and densely distributed object detection accuracy. Specifically, the encoder is responsible for encoding labeled image blocks using convolutional neural network (CNN) feature maps. It efficiently fuses these blocks with higher resolution multi-scale features through cross-layer connections, facilitating the extraction of global contextual information. The decoder then performs an upsampling of the encoded features, effectively recovering the full spatial resolution of the feature maps to capture essential local–global semantic features for accurate object localization. In addition, high quality proposed anchor boxes are generated by refined convolution, and the convolved features are adaptively aligned according to the anchor boxes to reduce redundant computation. The proposed MATDet achieves mAPs of 80.35%, 78.83%, 73.60%, and 98.01% on the DOTAv1.0, DOTAv1.5, DIOR, and HRSC2016 datasets, respectively, proving that it outperforms the baseline model for oriented object detection. This validation confirms the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of modern land use and climate change on mounded prehistoric sites, considering the evidence from Kur River Basin (southwestern Iran). By applying remote sensing and GIS techniques to historical and recent satellite imagery, we monitored an assemblage of 45 Neolithic mounds in order to map and quantify their taphonomic signatures over the last 53 years. Our results highlight that the deterioration of sites occurred as a result of landscape transformation due to land use and socio-economic dynamics that were exacerbated by current climate change. Increasing demands for fertile land and water led to the over-exploitation of sources, resulting in changing hydrological regimes and ecological systems and loss of the archaeological record. By contextualizing these processes over a historical perspective, our findings contribute to archaeological heritage management efforts. [ABSTRACT FROM AUTHOR]

The process of estimating and counting tree density using only a single aerial or satellite image is a difficult task in the fields of photogrammetry and remote sensing. However, it plays a crucial role in the management of forests. The huge variety of trees in varied topography severely hinders tree counting models to perform well. The purpose of this paper is to propose a framework that is learnt from the source domain with sufficient labeled trees and is adapted to the target domain with only a limited number of labeled trees. Our method, termed as AdaTreeFormer, contains one shared encoder with a hierarchical feature extraction scheme to extract robust features from the source and target domains. It also consists of three subnets: two for extracting self-domain attention maps from source and target domains respectively and one for extracting cross-domain attention maps. For the latter, an attention-to-adapt mechanism is introduced to distill relevant information from different domains while generating tree density maps; a hierarchical cross-domain feature alignment scheme is proposed that progressively aligns the features from the source and target domains. We also adopt adversarial learning into the framework to further reduce the gap between source and target domains. Our AdaTreeFormer is evaluated on six designed domain adaptation tasks using three tree counting datasets, i.e. Jiangsu, Yosemite, and London. Experimental results show that AdaTreeFormer significantly surpasses the state of the art, e.g. in the cross domain from the Yosemite to Jiangsu dataset, it achieves a reduction of 15.9 points in terms of the absolute counting errors and an increase of 10.8% in the accuracy of the detected trees' locations. The codes and datasets are available at https://github.com/HAAClassic/AdaTreeFormer. [ABSTRACT FROM AUTHOR]

The aging condition of asphalt pavement is an invaluable basis for traffic infrastructure evaluation. Due to the amount of time and high cost of monitoring and identifying asphalt pavement aging, many current studies focus on satellite remote sensing methods. In this paper, some methods and technologies for monitoring asphalt pavement degradation by optical satellite remote sensing are introduced as a literature review. Many researchers have developed spectrum libraries based on the actual aging of asphalt pavements, and it is possible to construct pavement health indices based on spectrum changes. Some indexes can extract different aging degrees of asphalt pavement from optical satellite images. Of course, current research can only preliminarily reflect the aging phenomenon of asphalt pavement and cannot accurately describe the distress characteristics of asphalt pavement. Future research needs to further strengthen mechanism research, develop higher resolution images, improve image processing technology, and adopt multi-means fusion analysis methods. [ABSTRACT FROM AUTHOR]

Shi et al. [[9]] emphasized the significance of remote sensing reflectance as a crucial parameter in the remote sensing inversion of plateau inland water colors. Remote sensing has played a pivotal role in advancing the fields of archaeology, geography, and earth sciences, offering new perspectives and unparalleled opportunities for research, analysis, and interpretation within these disciplines. [Extracted from the article]

Recently, target analysis using satellite SAR images has received much attention in the area of satellite SAR remote sensing. Because the spatial resolution of the target response in the satellite SAR image is a main factor that has a large effect on target analysis performances, the improvement of the spatial resolution of target response is required to enhance the target analysis capability. However, the spatial resolution is already determined in the satellite SAR system design process. To solve the above problem, the super-resolution techniques that have been applied to radar images can be utilized. However, the application of the super-resolution techniques to the target response in the satellite SAR image is not simple due to the following reasons. First, the target's motion induces severe blurring of the target response, which impedes the successful improvement of spatial resolution. Next, the zero-region in the frequency spectrum of the target image containing the target response also hinders the generation of the super-resolved image. To successfully improve spatial resolution of the satellite SAR image, the super-resolution techniques should be combined with proper preprocessing steps that can cope with the above two issues. In this paper, the whole super-resolution procedure for target responses in KOMPSAT-5 images is described. To the best of the authors' knowledge, the description of the whole super-resolution procedure for target responses is the first ever attempt in the area of satellite SAR. First, a target image containing the target response is extracted from a large-scale KOMPSAT-5 image. Subsequently, the target image is transformed to be appropriate for the utilization of super-resolution techniques by proper preprocessing steps, considering the direction of super resolution and the motion of the target. Then, some super-resolution techniques are utilized to improve the spatial resolutions and qualities of the target images. The super-resolution performances of the proposed scheme are validated using various target images for point static, extended static, and extended moving targets. The novelties of this paper can be summarized as follows: (1) the practical design of whole super-resolution processing for real satellite SAR images; (2) the performance evaluation of super-resolution techniques on real satellite SAR images. The results show that the proposed scheme can led to noticeable improvements of spatial resolution of the target images for various types of targets with reliable computation times. In addition, the proposed scheme also enhanced PSLR, ISLR, and IC, leading to clearer scattering information of the principal scatterers. Consequently, the proposed method can assist in extracting more precise and meaningful information for targets represented in KOMPSAT-5 images, which means great potential for target recognition. [ABSTRACT FROM AUTHOR]

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method. [ABSTRACT FROM AUTHOR]

Monitoring water quality with classical methods is not an easy task. Remote sensing with wide coverage and multiple temporal monitoring is the best solution for surface water quality monitoring. This paper demonstrates the determination of surface water quality parameters by using principal component analysis (PCA) data fusion and mining techniques with the aid of Landsat 8 OLI (L8 OLI), Sentinel 2A (S2A), and Göktürk-2 (GK2) satellite sensors. Chlorophyll-a, dissolved oxygen, total suspended solids, Secchi disk depth, total dissolved substance, and pH were the parameters selected for surface water quality analysis. High spectral resolution of L8 OLI/S2A images and the high spatial resolution of GK2 images were fused and analyzed by a suite of data mining models to provide more reliable images with both high spatial and temporal resolutions. Surface water quality parameters calculated by PCA-based response surface regression (RSR) method were compared with results obtained from multiple linear regression (MLR), artificial neural network (ANN), and support vector machines (SVMs) data mining methods. The performance of the data mining models derived using only multispectral band data and PCA fused data were quantified using four statistical indices; such as mean-square error (MSE), root MSE, mean absolute error, and coefficient of determination (R2). The analysis confirmed that the PCA-based RSR method is superior to MLR, ANN, and SVM data mining models to accurately estimate water quality parameters in lakes. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real‐time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll‐a concentrations (chl‐a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real‐time monitoring of HABs. Landsat‐8 and Sentinel‐2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl‐a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R2 values of 0.75, 0.82, and 0.79 for chl‐a, turbidity, and phycocyanin, while multiple regression had R2 values of 0.29, 0.51, and 0.46 and SVR had R2 values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open‐source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality. [ABSTRACT FROM AUTHOR]

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m3), the ratio of crop output per unit of water input (or crop per drop). Previous large-scale CWP studies have been useful for broad water use modeling at coarser resolutions. However, obtaining more precise CWP, especially for specific crop types in a particular area and growing season as outlined here are important for informing farm-scale water management decision making. Therefore, this study focused on California's Central Valley utilizing high-spatial resolution satellite imagery of 30 m (0.09 hectares per pixel) to generate more precise CWP for commonly grown and water-intensive irrigated crops. First, two products were modeled and mapped. 1. Landsat based Actual Evapotranspiration (ETa; mm/d) to determine Crop Water Use (CWU; m3/m2), and 2. Crop Productivity (CP; kg/m2) to estimate crop yield per growing season. Then, CWP was calculated by dividing CP by CWU and mapped. The amount of water that can be saved by increasing CWP of each crop was further calculated. For example, in the 434 million m2 study area, a 10% increase in CWP across the 9 crops analyzed had a potential water savings of 31.5 million m3 of water. An increase in CWP is widely considered the best approach for saving maximum quantities of water. This paper proposed, developed, and implemented a workflow of combined methods utilizing cloud computing based remote sensing data. The environmental implications of this work in assessing water savings for food and water security in the 21st century are expected to be significant. [ABSTRACT FROM AUTHOR]

Insufficient image spatial resolution is a serious limitation in many practical scenarios, especially when acquiring images at a finer scale is infeasible or brings higher costs. This is inherent to remote sensing, including Sentinel-2 satellite images that are available free of charge at a high revisit frequency, but whose spatial resolution is limited to 10m ground sampling distance. The resolution can be increased with super-resolution algorithms, in particular when performed from multiple images captured at subsequent revisits of a satellite, taking advantage of information fusion that leads to enhanced reconstruction accuracy. One of the obstacles in multi-image super-resolution consists in the scarcity of real-world benchmarks—commonly, simulated data are exploited which do not fully reflect the operating conditions. In this paper, we introduce a new benchmark (named MuS2) for super-resolving multiple Sentinel-2 images, with WorldView-2 imagery used as the high-resolution reference. Within MuS2, we publish the first end-to-end evaluation procedure for this problem which we expect to help the researchers in advancing the state of the art in multi-image super-resolution. [ABSTRACT FROM AUTHOR]

This paper presents a unique case of the application of remote sensing methods in archaeological survey devoted to ancient pre-Roman Imperial roads in the Southern Levant. The results of our preparatory remote sensing research and subsequent fieldwork in Jordan and Israel between 22 February and 23 March 2023, within the framework of the research project entitled "Travel and Mobility in Hellenistic and Early Roman Palestine", are reported and discussed. Part of this project is a large-scale, systematic research attempt to discover additional ancient pre-Roman roads and to suggest a working methodology for future research. The methodology is supposed to combine remote sensing research and archaeological survey. The project's first fieldwork achieved several goals. First, the modern methods enabled us to provide a high-resolution capture of the detected features and artifacts, including the courses of ancient roads and the locations of road-related archaeological sites. Altogether, 105 road remains, 62 archaeological sites, and 14 pottery findings were identified; what is more, 11 GPS (Global Positioning System) tracks of ancient roads were registered. Second, we suggested necessary revisions to the previous state of research and reported new findings. For instance, newly discovered rock art evidence found along Glueck's Road confirms the continuity of the use of this road long into late antiquity and early Islam. Third, some methodological conclusions were reached. For example, a multi-source approach to identifying ancient roads including the use of archival cartographic sources, archival and modern satellite and aerial imagery, and the databases of archaeological sites is still necessary. However, there can be no doubt that spatial analyses and remote sensing studies must be accompanied by archaeological fieldwork, which is absolutely necessary for determining the dating of the roads (by dating the settlement and pottery) and a detailed identification of the road courses (particularly through the discovery of road-related infrastructure). [ABSTRACT FROM AUTHOR]

Change in land use and land cover (LULC) contributes in worsening ecological issues. Studying the trends of change in land use is highly significant to deal with global climate change and sustainable development. The aim of this paper is to evaluate the spatial-temporal dynamics of LULC of the Bamenda Mountains (BM) in the North West region of Cameroon, over a period of 34 years (1988–2022) and predict 34 years (2022–2056) future land use scenario of this site using time series satellite imagery (MSS, TM, ETM+, and OLI-TIRS) and ancillary data and to comprehend the driving forces of land use/land cover change (LULCC). The trends of LULCC were quantified; LULC maps were derived by classifying time series satellite images. Six LULC categories were identified during the study period (1988–2022). The research revealed a significant LULCC of the BM which can be justified by increase in the human population observed in the study area and the desire to extend agricultural lands to sustain the growing population. Overall, cultivated area 5684 ha (10.47%), 10680 ha (19.57 %), and 15163 ha (27.78%) and built-up area 449 ha (0.83%), 996 ha (1.83%), and 3242 ha (5.94%) for the study years 1988, 2003, and 2022, respectively, were all on the increase throughout the study period at the expense of other land cover types. The predicted figures of 2056 showed a continuous reduction of montane forest and savanna: 2401.92 ha (4.40%) and 25,862.67 ha (47.39%), respectively. Bare area is expected to drop in 2056 (2905.92 ha (5.32%)). The above decrease, when compared to 2022 figures, represents a loss of 3.97%, 4.53%, and 0.57%, respectively. The losses observed are gained by built-up and cultivated land (5.72% and 3.39%, respectively), covering surfaces areas of 6364.89 ha (11.66%) and 17,008.56 ha (31.17%), respectively. The above findings suggest that population growth is likely the major menace to the natural environment. It is thus safe to say that substantial LULCC was observed throughout the study period and will undoubtedly continue if nothing is done. This necessitates urgent measures such as reforestation and afforestation, encouraging off-farm activities and even improving technologies to combat the rate of forest degradation of the BM. Additionally, rebuilding trust between the French and English Cameroons through dialogue is premodial, to end the curent conflictual civil war and lessen the landscape configuration in Bamenda. [ABSTRACT FROM AUTHOR]

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Regulated water bodies like lakes and reservoirs are increasingly becoming an object of attention due to the problems of greenhouse gas emissions, regional ecology, and the necessity to ensure safe environmental management. However, for some local tasks, it is important to assess the contribution of a hydroelectric power plant (HPP) to various parameters of the nearest water environment, for example, mortality of zooplankton, transfer of suspended matter and phytoplankton, formation of secondary deposits, methane emissions, spatial features of stratification, etc. An example of such studies is the present paper. It is based on unique data of complex measurements of hydrophysical, hydrooptical, hydrobiological, and hydrochemical water parameters, as well as methane fluxes, that were collected at the Gorky Reservoir nearby a HPP in the spring, summer, and autumn of 2022. Preliminary correlations between these parameters were obtained. The results are useful for the correct interpretation of satellite images of inland waters, quantitative description of HPPs' influence on the water environment, knowledge of the main patterns of transformation of aquatic organism communities under conditions of runoff regulation, determination of water quality by hydrobiological parameters, development of mechanisms for improving the ecological state of water bodies, and accounting spatial heterogeneity of methane flows from the surface of the reservoir. [ABSTRACT FROM AUTHOR]

This paper presents analyses of the potential use of Sentinel-1 (S-1) and Sentinel-2 (S-2) imagery to generate models of winter wheat growth under agricultural drought vs. normal conditions identified based on potential yield losses calculated in the Agricultural Drought Monitoring System (ADMS). The analyses carried out showed the sensitivity of satellite images to agricultural drought conditions determined in ADMS. The study was conducted in a large region, the West Pomeranian Voivodeship (NUTS PL42), and the analysis covered about 22,935 polygons with winter wheat production that constituted a total area of about 108,000 ha in the period from the 1st of April to the 1st of July 2021. For S-1 data, VH and VV backscatter and the VH/VV ratio were calculated, and for S-2 data, NDVI and NDWI indices were calculated, which were used to build models of winter wheat growth under water stress and in normal conditions. The obtained results presented in this work include: (i) Development of a test version of a model describing the winter wheat crop's growth, with a preliminary assessment showing the potential for recognizing water shortage effects; and (ii) identification of promising indicators of water scarcity for crops, calculated based on S-1 and S-2 images, that could be recommended for application in remote sensing (RS) of drought effects as complementary multispectral and radar observations. The results obtained in this work also gave many clues regarding the direction and method of including satellite remote sensing in national monitoring programmes, which involves operations on many types of big data sets. [ABSTRACT FROM AUTHOR]

Despite significant advancements in remote sensing object tracking (RSOT) in recent years, achieving accurate and continuous tracking of tiny-sized targets remains a challenging task due to similar object interference and other related issues. In this paper, from the perspective of feature enhancement and a better feature matching strategy, we present a tracker SiamTM specifically designed for RSOT, which is mainly based on a new target information enhancement (TIE) module and a multi-level matching strategy. First, we propose a TIE module to address the challenge of tiny object sizes in satellite videos. The proposed TIE module goes along two spatial directions to capture orientation and position-aware information, respectively, while capturing inter-channel information at the global 2D image level. The TIE module enables the network to extract discriminative features of the targets more effectively from satellite images. Furthermore, we introduce a multi-level matching (MM) module that is better suited for satellite video targets. The MM module firstly embeds the target feature map after ROI Align into each position of the search region feature map to obtain a preliminary response map. Subsequently, the preliminary response map and the template region feature map are subjected to the Depth-wise Cross Correlation operation to get a more refined response map. Through this coarse-to-fine approach, the tracker obtains a response map with a more accurate position, which lays a good foundation for the prediction operation of the subsequent sub-networks. We conducted extensive experiments on two large satellite video single-object tracking datasets: SatSOT and SV248S. Without bells and whistles, the proposed tracker SiamTM achieved competitive results on both datasets while running at real-time speed. [ABSTRACT FROM AUTHOR]

Image-based geo-localization focuses on predicting the geographic information of query images by matching them with annotated images in a database. To facilitate relevant studies, researchers collect numerous images to build the datasets, which explore many challenges faced in real-world geo-localization applications, significantly improving their practicability. However, a crucial challenge that often arises is overlooked, named the cross-time challenge in this paper, i.e., if query and database images are taken from the same landmark but at different time periods, the significant difference in their image content caused by the time gap will notably increase the difficulty of image matching, consequently reducing geo-localization accuracy. The cross-time challenge has a greater negative influence on non-real-time geo-localization applications, particularly those involving a long time span between query and database images, such as satellite-view geo-localization. Furthermore, the rough geographic information (e.g., names) instead of precise coordinates provided by most existing datasets limits the geo-localization accuracy. Therefore, to solve these problems, we propose a dataset, MTGL40-5, which contains remote sensing (RS) satellite images captured from 40 large-scale geographic locations spanning five different years. These large-scale images are split to create query images and a database with landmark labels for geo-localization. By observing images from the same landmark but at different time periods, the cross-time challenge becomes more evident. Thus, MTGL40-5 supports researchers in tackling this challenge and further improving the practicability of geo-localization. Moreover, it provides additional geographic coordinate information, enabling the study of high-accuracy geo-localization. Based on the proposed MTGL40-5 dataset, many existing geo-localization methods, including state-of-the-art approaches, struggle to produce satisfactory results when facing the cross-time challenge. This highlights the importance of proposing MTGL40-5 to address the limitations of current methods in effectively solving the cross-time challenge. [ABSTRACT FROM AUTHOR]

Hyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, the spatial resolution of available hyperspectral imagery is inferior, severely affecting the accuracy of ground object identification. In the paper, we propose an adaptively optimized pulse-coupled neural network (PCNN) model to sharpen the spatial resolution of the hyperspectral imagery to the scale of the multispectral imagery. Firstly, a SAM-CC strategy is designed to assign hyperspectral bands to the multispectral bands. Subsequently, an improved PCNN (IPCNN) is proposed, which considers the differences of the neighboring neurons. Furthermore, the Chameleon Swarm Optimization (CSA) optimization is adopted to generate the optimum fusion parameters for IPCNN. Hence, the injected spatial details are acquired in the irregular regions generated by the IPCNN. Extensive experiments are carried out to validate the superiority of the proposed model, which confirms that our method can realize hyperspectral imagery with high spatial resolution, yielding the best spatial details and spectral information among the state-of-the-art approaches. Several ablation studies further corroborate the efficiency of our method. [ABSTRACT FROM AUTHOR]

With satellite quantity and quality development in recent years, remote sensing products in vast areas are becoming widely used in more and more fields. The acquisition of large regional images requires the scientific and efficient utilization of satellite resources through imaging satellite task planning technology. However, for imaging satellite task planning in a vast area, a large number of decision variables are introduced into the imaging satellite task planning model, making it difficult for existing optimization algorithms to obtain reliable solutions. This is because the search space of the solution increases the exponential growth with the increase in the number of decision variables, which causes the search performance of optimization algorithms to decrease significantly. This paper proposes a large-scale multi-objective optimization algorithm based on efficient competition learning and improved non-dominated sorting (ECL-INS-LMOA) to efficiently obtain satellite imaging schemes for large areas. ECL-INS-LMOA adopted the idea of two-stage evolution to meet the different needs in different evolutionary stages. In the early stage, the proposed efficient competitive learning particle update strategy (ECLUS) and the improved NSGA-II were run alternately. In the later stage, only the improved NSGA-II was run. The proposed ECLUS guarantees the rapid convergence of ECL-INS-LMOA in the early evolution by accelerating particle update, introducing flight time, and proposing a binary competitive swarm optimizer BCSO. The results of the simulation imaging experiments on five large areas with different scales of decision variables show that ECL-INS-LMOA can always obtain the imaging satellite mission planning scheme with the highest regional coverage and the lowest satellite resource consumption within the limited evaluation times. The experiments verify the excellent performance of ECL-INS-LMOA in solving vast area mapping planning problems. [ABSTRACT FROM AUTHOR]

Optical satellite image change detection has attracted extensive research due to its comprehensive application in earth observation. Recently, deep learning (DL)-based methods have become dominant in change detection due to their outstanding performance. Remote sensing (RS) images contain different sizes of ground objects, so the information at different scales is crucial for change detection. However, the existing DL-based methods only employ summation or concatenation to aggregate several layers of features, lacking the semantic association of different layers. On the other hand, the UNet-like backbone is favored by deep learning algorithms, but the gradual downscaling and upscaling operation in the mainstream UNet-like backbone has the problem of misalignment, which further affects the accuracy of change detection. In this paper, we innovatively propose a hierarchical feature association and global correction network (HFA-GCN) for change detection. Specifically, a hierarchical feature association module is meticulously designed to model the correlation relationship among different scale features due to the redundant but complementary information among them. Moreover, a global correction module on Transformer is proposed to alleviate the feature misalignment in the UNet-like backbone, which, through feature reuse, extracts global information to reduce false alarms and missed alarms. Experiments were conducted on several publicly available databases, and the experimental results show the proposed method is superior to the existing state-of-the-art change detection models. [ABSTRACT FROM AUTHOR]

The subject of the research paper is the use of remote sensing in monitoring and analyzing the impact of climate change on the occurrence of extreme precipitation, and the causeand-effect occurrence of floods in the area of the Sana River Basin in Bosnia and Herzegovina. The goal is to process the "product" of remote sensing to identify the time intervals of occurrence of extreme precipitation, to assess their impact on water levels, and to map potential floods in space. Spatial identification of zones that are at risk of flooding is an integral part of the aforementioned goal. Precipitation monitoring was performed by processing Climate Hazards Group InfraRed Precipitation with Station Data through the Google Earth Engine platform. The observed 30-year period (1992-2022) was compared with the average precipitation for 2017, 2018 and 2019. The impact of extreme precipitation on the water level of the Sana River was analyzed. Flooding periods have been identified: February and December 2017, March 2018 and May 2019. Mapping of flooded areas was carried out by pre-processing and post-processing of Sentinel-1 radar satellite images. The total flooded area is: 710.38 ha (February 2017), 496.79 ha (December 2017), 417.86 ha (March 2018) and 422.42 ha (May 2019). Based on the identified flooded areas, a flood risk map was created on the main course of the Sana River. The research contributes to a better understanding of the changes that occur in the area under the influence of climate change, and the data presented are important for numerous practical issues in the field of water resource management and flood protection. [ABSTRACT FROM AUTHOR]

Sea fog detection (SFD) presents a significant challenge in the field of intelligent Earth observation, particularly in analyzing meteorological satellite imagery. Akin to various vision tasks, ImageNet pre-training is commonly used for pre-training SFD. However, in the context of multi-spectral meteorological satellite imagery, the initial step of deep learning has received limited attention. Recently, pre-training with Very High-Resolution (VHR) satellite imagery has gained increased popularity in remote-sensing vision tasks, showing the potential to replace ImageNet pre-training. However, it is worth noting that the meteorological satellite imagery applied in SFD, despite being an application of computer vision in remote sensing, differs greatly from VHR satellite imagery. To address the limitation of pre-training for SFD, this paper introduces a novel deep-learning paradigm to the meteorological domain driven by Masked Image Modeling (MIM). Our research reveals two key insights: (1) Pre-training with meteorological satellite imagery yields superior SFD performance compared to pre-training with nature imagery and VHR satellite imagery. (2) Incorporating the architectural characteristics of SFD models into a vanilla masked autoencoder (MAE) can augment the effectiveness of meteorological pre-training. To facilitate this research, we curate a pre-training dataset comprising 514,655 temporal multi-spectral meteorological satellite images, covering the Bohai Sea and Yellow Sea regions, which have the most sea fog occurrence. The longitude ranges from 115.00E to 128.75E, and the latitude ranges from 27.60N to 41.35N. Moreover, we introduce SeaMAE, a novel MAE that utilizes a Vision Transformer as the encoder and a convolutional hierarchical decoder, to learn meteorological representations. SeaMAE is pre-trained on this dataset and fine-tuned for SFD, resulting in state-of-the-art performance. For instance, using the ViT-Base as the backbone, SeaMAE pre-training which achieves 64.18 % surpasses from-scratch learning, natural imagery pre-training, and VRH satellite imagery pre-training by 5.53 % , 2.49 % , and 2.21 % , respectively, in terms of Intersection over Union of SFD. [ABSTRACT FROM AUTHOR]

Analysis of aerial images provided by satellites enables continuous monitoring and is a central component of many applications, including precision farming. Nonetheless, this analysis is often impeded by the presence of clouds and cloud shadows, which obscure the underlying region of interest and introduce incorrect values that bias analysis. In this paper, we outline a method for cloud shadow detection, and demonstrate our method using Canadian farmland data obtained from the Sentinel-2 satellite. Our approach builds on other object-based cloud and cloud shadow detection methods that generate preliminary shadow candidate masks which are refined by matching clouds to their respective shadows. We improve on these components by using ray-casting and inverse texture mapping methods to quickly identify cloud shadows, allowing for the immediate removal of false positives during image processing. Leveraging our ray-casting-based approach, we further improve our results by implementing a probability analysis based on the cloud probability layer provided by the Sentinel-2 satellite to account for missed shadow pixels. An evaluation of our method using the average producer (82.82%) and user accuracy (75.55%) both show a marked improvement over the performance of other object-based methods. Methodologically, our work demonstrates how incorporating probability analysis as a post-processing step can improve the generation of shadow masks. [ABSTRACT FROM AUTHOR]

With the rapid development of internet technology in recent years, the available remote sensing image data have also been growing rapidly, which has led to an increased demand for remote sensing image retrieval. Remote sensing images contain rich visual and semantic features, and have high variability and complexity. Therefore, remote sensing image retrieval needs to fully utilize the information in the images to perform feature extraction and matching. Metric learning has been widely used in image retrieval as it can train embedding spaces with high discriminability. However, existing deep metric learning methods learn embedding spaces with high discriminability by maximizing the differences between classes, while ignoring inherent intra-class differences during the learning process. In this paper, we design a new sample generation mechanism to generate samples from positive samples that meet the boundary constraints, thus obtaining quantifiable intra-class differences from real positive samples. Based on the sample generation relationship, we use a self-supervised approach to design an intra-class ranking loss function, which improves the discriminability of the generated embedding space for samples of the same class and maintains their ranking relationship in the embedding space. Moreover, this loss function can be easily combined with existing deep metric learning methods. Our aim is to help the network to better extract features and further improve the performance of remote sensing image retrieval through the sample generation mechanism and intra-class ranking loss. Finally, we conduct extensive experiments on multiple remote-sensing image datasets using multiple evaluation metrics such as mAP@K, which demonstrate that using the sample-generated intra-class ranking loss function can effectively improve the performance of remote sensing image retrieval. [ABSTRACT FROM AUTHOR]

This paper is uniquely focused on mapping business land in satellite imagery, with the aim to introduce a standardized approach to estimating how much land in an observed area is allocated to business. Business land and control categories of land are defined and operationalized in a straightforward setting of pixel-based classification. The resultant map as well as information from a sample-based quantification of the map's accuracy are used jointly to estimate business land's total area more precisely. In particular, areas where so-called errors of omission are possibly concentrated are accounted for by post-stratifying the map in an extension of recent advances in remote sensing. In specific, a post-stratum is designed to enclose areas where business activity is co-located. This then enhances the area estimation in a spatially explicit way that is informed by urban and regional economic thought and observation. In demonstrating the methodology, a map for the San Francisco Bay Area metropolitan area is obtained at a producer's accuracy of 0.89 (F1-score = 0.84) or 0.82 to 0.94 when sub-selecting reference sample pixels by confidence in class assignment. Overall, the methodological approach is able to infer the allocation of land to business (in km2 ± 95% C.I.) on a timely and accurate basis. This inter-disciplinary study may offer some fundamental ground for a potentially more refined assessment and understanding of the spatial distribution of production factors as well as the related structure and implications of land use. [ABSTRACT FROM AUTHOR]