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Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth's surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakly supervised, and unsupervised. Initially, the review introduces the basic network architectures for CD methods using DL. Then, it provides a comprehensive analysis of CD methods under different learning paradigms, summarizing commonly used frameworks. Additionally, an overview of publicly available datasets for CD is offered. Finally, the review addresses the opportunities and challenges in the field, including: (a) incomplete supervised CD, encompassing semi-supervised and weakly supervised methods, which is still in its infancy and requires further in-depth investigation; (b) the potential of self-supervised learning, offering significant opportunities for Few-shot and One-shot Learning of CD; (c) the development of Foundation Models, with their multi-task adaptability, providing new perspectives and tools for CD; and (d) the expansion of data sources, presenting both opportunities and challenges for multimodal CD. These areas suggest promising directions for future research in CD. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the CD field. [ABSTRACT FROM AUTHOR]

In the change detection (CD) task, the substantial variation in feature distributions across different CD datasets significantly limits the reusability of supervised CD models. To alleviate this problem, we propose an illumination–reflection decoupled change detection multi-scale unsupervised domain adaptation model, referred to as IRD-CD-UDA. IRD-CD-UDA maintains its performance on the original dataset (source domain) and improves its performance on unlabeled datasets (target domain) through a novel CD-UDA structure and methodology. IRD-CD-UDA synergizes mid-level global feature marginal distribution domain alignment, classifier layer feature conditional distribution domain alignment, and an easy-to-hard sample selection strategy to increase the generalization performance of CD models on cross-domain datasets. Extensive experiments conducted on the LEVIR, SYSU, and GZ optical remote sensing image datasets demonstrate that the IRD-CD-UDA model effectively mitigates feature distribution discrepancies between source and target CD data, thereby achieving optimal recognition performance on unlabeled target domain datasets. [ABSTRACT FROM AUTHOR]

High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps. [ABSTRACT FROM AUTHOR]

One of the most important aspects of climate change is alterations in the amount and characteristics of precipitation. The present study intended to project precipitation changes over Iran based on the regional models of the CORDEX project under two scenarios (namely, RCP4.5 and RCP8.5). For this purpose, six indices, including maximum 1-day precipitation, simple precipitation intensity index, maximum length of dry spell, annual count of days when PRCP ≥ 10 mm, annual count of days when PRCP ≥ 1 mm, and annual total precipitation in wet days, were extracted. MERRA2 reanalysis precipitation data, highly correlated with observation data from 48 weather stations all over the country, were used for the 1980–2006 time period to assess the CORDEX project outputs. The results were presented in three different time periods, including the base period (1980–2006), the near future (2021–2050), and the far future (2071–2100). The results of the analyses indicated that four indices, namely the maximum 1-day precipitation, the annual count of days when PRCP ≥ 1 mm, the annual count of days when PRCP ≥ 10 mm, and the annual total precipitation in wet days, demonstrated a decrease in precipitation and an increase in dry spells all over the country except for the east and the southeast regions where these indices will increase compared to the base period (except for the maximum length of the dry spell). Research Highlights: 1. The study focuses on monitoring and projecting spatiotemporal changes in extreme precipitation indices over Iran for historic (1980 to 2006), near future (2021-2050), and far future (2071-2100) time periods. 2. From 1980 to 2006, MERRA2 reanalysis precipitation data show a strong spatial correlation with monthly observational data, and total precipitation shows negative significant trends in the northwest and positive significant trends in the north. 2. From 1980 to 2006, MERRA2 reanalysis precipitation data show a strong spatial correlation with monthly observational data, and total precipitation shows negative significant trends in the northwest and positive significant trends in the north. 3. For projecting extreme precipitation, four CORDEX-WAS models based on two RCPs (RCP4.5 and RCP8.5) are used. 4. A precise plane is required to deal with precipitation reduction and increased dry spells in the near and far future. [ABSTRACT FROM AUTHOR]

The natural terrain (e.g., farm and forest) in temperate zones changes dramatically between seasons due to distinct temperatures and precipitation variations from summer to winter. Moreover, built-up areas vary little in this short period. Therefore, extracting built-up areas via change detection on polarimetric synthetic aperture radar (PolSAR) images is feasible. A common type of PolSAR change detection method is based on hypothesis testing theory. However, in these methods, pixels are selected as the processing units; as a result, these models are computationally complex and poorly maintain the boundaries of built-up areas. In this paper, we innovatively introduce superpixels into the hypothesis test theory and propose a superpixelwise PolSAR change detection method for built-up area extraction. First, we oversegment the PolSAR images into a set of superpixels and derive the probability density function (PDF) of a superpixel's reflectivity on a PolSAR image. Based on this distribution, we present a superpixelwise likelihood-ratio test (LRT) statistic to measure the similarity of two superpixelwise covariance matrices for unsupervised change detection. When actually computing the superpixelwise LRT, the large variation in the areas of the superpixels makes the likelihood functions very complex and estimating the parameters difficult. We further simplify the calculation of the LRT statistic and apply it to built-up area extraction. Compared to the state-of-the-art built-up area extraction methods, our approach provides the best results with overall accuracy values of 91.41%, 92.71%, 93.67% and 93.91% for the four studied areas respectively. In addition, the computational complexity of our method is assessed, and the run time (seconds) of the proposed method in the four study cases is 11.14, 6.32, 5.31, and 4.88, respectively, superior to the values of 66.12, 36.26, 33.65, and 29.67 for DRT, respectively. Code and datasets: https://github.com/SunXJ7/Sentinel-1-Datasets-for-Built-up-Area-Extraction. [ABSTRACT FROM AUTHOR]

High-definition (HD) maps are becoming increasingly important for autonomous vehicles and advanced driver assistance systems (ADAS), as they provide detailed and accurate information about the road environment. Vision localization using HD maps can improve accuracy, but maps can become outdated. Image perception can be useful for HD map change detection. However, achieving robust and high-precision alignment between images and HD maps is challenging in varying environmental conditions. In addition, evaluating alignment in the absence of ground truth data is inconvenient. This article proposes a trajectory interpolation-based method for reconstructing lane markings from images to detect road changes when compared with the HD grid map. Images and HD maps are registered using a distance transform and cross-entropy-based optimization. The article also proposes a metric based on intersection over union for evaluating alignment accuracy. Experiments were conducted using a third-party-collected campus dataset and the Argoverse2 open-source dataset to demonstrate the effectiveness of the proposed methods in detecting road changes and achieving high-precision image and HD map fusion. [ABSTRACT FROM AUTHOR]

Discriminating salient moving objects against complex, cluttered backgrounds, with occlusions and challenging environmental conditions like weather and illumination, is essential for stateful scene perception in autonomous systems. We propose a novel deep architecture, named DeepFTSG, for robust moving object detection that incorporates single and multi-stream multi-channel USE-Net trellis asymmetric encoders extending U-Net with squeeze and excitation (SE) blocks and a single shared decoder network for fusing multiple motion and appearance cues. DeepFTSG is a deep learning based approach that builds upon our previous hand-engineered flux tensor split Gaussian (FTSG) change detection video analysis algorithm which won the CDNet CVPR Change Detection Workshop challenge competition. DeepFTSG generalizes much better than top-performing motion detection deep networks, such as the scene-dependent ensemble-based FgSegNet_v2, while using an order of magnitude fewer weights. Short-term motion and longer-term change cues are estimated using general-purpose unsupervised methods—flux tensor and multi-modal background subtraction, respectively. DeepFTSG was evaluated using the CDnet-2014 change detection challenge dataset, the largest change detection video sequence benchmark with 12.3 billion labeled pixels, and had an overall F-measure of 97%. We also evaluated the cross-dataset generalization capability of DeepFTSG trained solely on CDnet-2014 short video segments and then evaluated on unseen SBI-2015, LASIESTA and LaSOT benchmark videos. On the unseen SBI-2015 dataset, DeepFTSG had an F-measure accuracy of 87%, more than 30% higher compared to the top-performing deep network FgSegNet_v2 and outperforms the recently proposed KimHa method by 17%. On the unseen LASIESTA, DeepFTSG had an F-measure of 88% and outperformed the best recent deep learning method BSUV-Net2.0 by 3%. On the unseen LaSOT with axis-aligned bounding box ground-truth, network segmentation masks were converted to bounding boxes for evaluation, DeepFTSG had an F-Measure of 55%, outperforming KimHa method by 14% and FgSegNet_v2 by almost 1.5%. When a customized single DeepFTSG model is trained in a scene-dependent manner for comparison with state-of-the-art approaches, then DeepFTSG performs significantly better, reaching an F-Measure of 97% on SBI-2015 (+ 10%) and 99% on LASIESTA (+ 11%). The source code, pre-trained weights, and video demo for DeepFTSG are available at https://github.com/CIVA-Lab/DeepFTSG. [ABSTRACT FROM AUTHOR]

Ghana's coastline has been facing erosion and sedimentation phenomena for several decades, resulting in a serious threat to life and property considering that major urban settlements are located on the coast. In this region, there has been a lack of emphasis on comprehensive, large-scale investigations into coastal changes: prior research has predominantly centered on site-specific assessments. These studies have revealed alarming erosion rates, with reports indicating that nearly ten meters are lost annually. The use of high-resolution remotely sensed data can be a consistent support in regions where physical or economic obstacles interfere with collecting in situ information. In particular, the use of continuous all-weather SAR data may facilitate the evaluation of erosion and sedimentation phenomena in coastal areas. In this paper, we apply SAR data over a time period between 2017 and 2021. Sentinel-1 data are pre-processed using the Google Earth Engine platform, and a dedicated algorithm is then applied to identify and quantify erosion and sedimentation processes. Optical images are used as a reference for detecting the location of two areas where consistent sedimentation and erosion phenomena occurred in the considered four years. The results demonstrate that SAR backscattering variations over time offer a reliable method for monitoring coastal changes. This approach enables the identification of the type of phenomena occurring - sedimentation or erosion -, and allows for the quantification of their intensity and dimensions over time. The method can be worldwide applied once the appropriate thresholds are evaluated and help in predictive studies and environmental planning. [ABSTRACT FROM AUTHOR]

Urbanisation is the process whereby cities are transformed into large sprawling areas. Urbanisation combined with a continuous increase in population makes food security crucial for sustainable development. Urbanisation poses a threat to agricultural land use within built-up and peri-urban areas. It has resulted in the rapid disappearance and/or total change of agricultural farmland in urban and peri-urban areas. To monitor the changes in agricultural farmland, an understanding of changes in the urban landscape is becoming increasingly important. In this study, multi-temporal Landsat imagery were used to analyse the impact of urbanisation on urban agriculture in the city of Niamey. Changes in the urban landscape were determined using the support vector machine (machine learning) algorithm. Results of this study showed a decrease in land with crops from 3428 ha to 648 ha and an increase in built-up areas from 1352 ha to 11,596 ha between 1975 and 2020. Urbanisation and population growth are the main drivers of urban landscape change in Niamey. There was also a decrease in bare land, rock and vegetation classes, while a small increase in rice and water body classes, comparing the 1975 and 2020 values. This study demonstrates the importance of remote sensing in showing the implications of urbanisation on urban agriculture. These results can assist city planners and resource managers in decision-making and adoption of sustainable mitigation measures which are crucial for urban development. [ABSTRACT FROM AUTHOR]

Spatiotemporal data are common in practice. Such data often have complicated structures that are difficult to describe by parametric statistical models. Thus, it is often challenging to analyze spatiotemporal data effectively since most existing statistical methods and software packages in the literature are based on parametric modeling and cannot handle certain applications properly. This paper introduces the new R package SpTe2M, which was developed for implementing some recent nonparametric methods for modeling and monitoring spatiotemporal data. This package provides analytic tools for modeling spatiotemporal data nonparametrically and for monitoring dynamic spatial processes sequentially over time. It can be used for different applications, including disease surveillance and environmental monitoring. The use of the package is demonstrated using the Florida influenza-like illness data observed during 2012–2014 and the PM2.5 concentration data in China collected during 2014–2016. [ABSTRACT FROM AUTHOR]

The environment is characterised by subtle and major mutations that cause changes in land use/land cover. Analysis of its dynamics and identification of vulnerable areas are critical to maintaining ecosystem services. The aim of this research is to quantify and qualify land cover dynamics over a 30-year period. It will also highlight forest degradation from a supervised classification of Landsat satellite imagery (L5 TM1987, L7 ETM+ 2000, and L8 OLI/TIRS 2019). The dynamics of land use/land cover were investigated by a maximum likelihood approach using geographic information system (GIS) and remote sensing (RS). Six major land use and land cover (LULC) types were mapped (build-up, agriculture, forest, clearing, matorral and olive cultivation). The classification reports made it possible to assess a reduction in forest cover (from 14 470.11 ha to 5 203.26 ha) and an increase in buildings (from 6 033.69 ha to 9 515.61 ha), and agricultural land (from 9 517.59 ha to 12 338.19 ha). The results were validated by a kappa coefficient of 0.93, 0.91, and 0.96, which showed that the model had successfully predicted LULC changes. We anticipate that the results will provide a basis for decision-making as well as a starting point for further in-depth studies in sustainable management and development of natural resources in the study region. [ABSTRACT FROM AUTHOR]

The Madaba Governorate, as the second-largest wheat producer in Jordan, holds a crucial position in safeguarding regional food security. Its evolving landscape, marked by changes in land use, presents environmental and socio-economic challenges that necessitate sustainable urban planning and land management practices. This study delves into the intricate relationship between the conversion of agricultural lands into urban areas and the concurrent rise in population within the Madaba Governorate. Utilizing a Markov model, this research employs land use and land cover (LULC) data from 1994, 2004, and 2015 to project future changes in 2025 and 2035 with an impressive 80% accuracy (kappa coefficient). The findings reveal a projected 6% increase in urban areas over the next decade and a notable 11.81% decline in rural lands, signifying a substantial urbanization trend. In response to these population-driven LULC dynamics, there is an urgent need for the implementation of sustainable land use planning and management solutions. Given the constraints of limited water resources in the region, this study also places emphasis on water resource management. Recommendations include measures such as restricting urban sprawl, preserving agricultural lands, managing population growth, and implementing water conservation strategies. These insights provide invaluable information for stakeholders in the Madaba Governorate, including policymakers and land use planners, fostering a comprehensive understanding of the complex interplay between regional water resources, population expansion, and land use changes. [ABSTRACT FROM AUTHOR]

Remote sensing is one of the most important methods for analysing the multitemporal changes over a certain period. As a cost-effective way, remote sensing allows the long-term analysis of agricultural land by collecting satellite imagery from different satellite missions. Landsat is one of the longest-running world missions which offers a moderate-resolution earth observation dataset. Land surface mapping and monitoring are generally performed by incorporating classification and change detection models. In this work, a deep learning-based change detection (DCD) algorithm has been proposed to detect long-term agricultural changes using the Landsat series datasets (i.e., Landsat-7, Landsat-8, and Landsat-9) during the period 2012 to 2023. The proposed algorithm extracts the features from satellite data according to their spectral and geographic characteristics and identifies seasonal variability. The DCD integrates the deep learning-based (Environment for visualizing images) ENVI Net-5 classification model and posterior probability-based post-classification comparison-based change detection model (PCD). The DCD is capable of providing seasonal variations accurately with distinct Landsat series dataset and promises to use higher resolution dataset with accurate results. The experimental result concludes that vegetation has decreased from 2012 to 2023, while build-up land has increased up to 88.22% (2012–2023) for Landsat-7 and Landsat-8 datasets. On the other side, degraded area includes water (3.20–0.05%) and fallow land (1–0.59%). This study allows the identification of crop growth, crop yield prediction, precision farming, and crop mapping. [ABSTRACT FROM AUTHOR]

The development of standardised data acquisition strategies and analytical workflows is crucial to quantify gully changes. In this study, we explore synergies between unoccupied aircraft vehicles (UAV) and satellite remote sensing in order to classify gully morphodynamics. Using Time Series Forest (TSF) and the Sentinel‐1 radar backscatter coefficient (σo), gully scenarios can be classified into four categories: gully topographical change, no change outside gully, no change inside gully, and non‐topographical change. In addition, a Random Forest (RF) classification is performed employing individual features obtained from elevation models and temporally aggregated datasets. Training data are generated from multitemporal UAV‐borne photogrammetric point clouds through a manual segmentation of different gullies in Namibia. This information is transferred from point clouds (sub‐m) to satellite imagery (10 m) generating training data at Sentinel‐1 pixel level. Results indicate that the TSF (on the σo Vertical‐Vertical polarisation) and RF (on temporally aggregated features) perform best when training and testing areas are located within the same geographical extent. Both approaches yield similar Total Accuracy (TA ≈ 79%–80%) and Cohen's Kappa value (Kappa ≈ 0.7), but TSF achieves superior Producer Accuracy (PA = 78.5%) and User Accuracy (UA = 84.6%) for the gully topographical change class. Additionally, the utilisation of TSF in Vertical‐Vertical polarisation is the most effective method if the testing and training areas are in different geographical locations, allowing gully identification with TA > 80% and Kappa = 0.49. However, this method presents limitations to precisely delineate the change types, as dynamics are rain‐driven and therefore are geographically related. In summary, by combining the complementary benefits of UAV‐based and satellite‐based solutions, this study opens a line of research for the study and classification of surface land dynamics and geomorphological feature extraction in regional extents. [ABSTRACT FROM AUTHOR]

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Change detection is a common and actual problem in the field of remote sensing. The classical approaches using raw pixel information are very sensitive to noise. In this study we propose the usage of additional semantic information for change detection. We use the semantic segmentation methods like geospatial Segment Anything Model and encoder based U-Net to evaluate the predictions and tracing the semantic information as well as raw information in change detection. Later the multidimensional time series data is used via the Vector Autoregression model to predict the future changes in the landscape. The observations which fall out of the prediction interval are considered as the changes in the landscape. The proposed method is evaluated on the dataset of the random locations across the Baltic region. The research is accompanied by the data and reproducible code at Github repository. [ABSTRACT FROM AUTHOR]

In order to clarify the long-term changes in mangroves in the Beibu Gulf of Guangxi and the carbon storage changes after the invasion of Spartina alterniflora (S. alterniflora) in the Dandou Sea area, the Continuous Change Detection and Classification (CCDC) algorithm combined with feature indices was first used to track the changes. Subsequently, the random forest algorithm was applied to classify each change segment, and then sampling was conducted based on the distribution of S. alterniflora in different invasion years. The results showed that the Kappa coefficient of the classification result of the latest change segment was 0.78. The rapid expansion of S. alterniflora, aquaculture pond construction, and land reclamation activities have led to changes in mangroves, causing a decrease in the area of the mangrove region. A total of 814.57 hectares of mangroves has been converted into other land-cover types, with most pixels undergoing one to two changes, and many of these changes were expected to continue until 2022. An analysis of the distribution characteristics and influencing factors of soil organic carbon (SOC) and soil organic carbon storage (SOCS) at different invasion stages revealed that SOC and SOCS were mainly influenced by soil bulk density, soil moisture content, and electrical conductivity. It was found that S. alterniflora had higher SOC content compared to the mudflats. With the increase in invasion years, S. alterniflora continuously increased the SOC and SOCS content in coastal wetlands. [ABSTRACT FROM AUTHOR]

Deforestation is a major concern for preserving the biodiversity of the entire globe. During the last few years, machine learning and deep learning methods have been employed for mapping deforestation. There is still scope for ample improvement in these methods as they are prone to errors and can give inaccurate results because of over or under-segmentation. This paper uses deep convolutional neural network-based semantic segmentation to process multispectral satellite images to monitor forest cover changes in Tadoba-Andhari National Park during the period 2000–2022. The proposed approach uses the U-Net architecture with extended inputs which gives more accuracy as compared to U-Net with only image input. Landsat images along with vegetation indices have been used as training data. The proposed method requires less time to train the model and is also cost-efficient in terms of computing requirements. The performance of the proposed method was compared with state-of-the-art methods where the proposed method outperformed the other models with an F1-score of 0.90 and an accuracy of 84.83%. When compared with U-Net trained with Landsat images only, it was observed that the U-Net model trained with extended input was able to achieve better results. [ABSTRACT FROM AUTHOR]

Destructive disasters can cause enormous harm and losses, and due to a lack of information for a certain period of time after the disaster, it is extremely detrimental to understanding the disaster situation and disaster relief. In order to obtain timely collapse and damage information of buildings, roads, bridges, reservoirs and other important ground objects in disaster areas, a method for automatically identifying and annotating the differences in remote sensing images before and after disasters has been proposed. Firstly, Gaussian noise was removed from time-series remote sensing images using block matching 3D (BM3D) method, and then image registration was performed using scale invariant feature transform (SIFT) method. The edge information of important ground objects in different areas was obtained by using Wv_Canny edge detection method for difference images. Finally, the important ground objects that have changed were identified and annotated. The experimental results of remote sensing image were compared according to the change area of the building, the correct rate is 78% ~ 79%, the false detection rate is 21% ~ 22%, and there is no missed detection rate. Simulation experiment and actual remote sensing image processing show that the proposed method can effectively identify and mark the difference areas of important ground objects such as buildings, which is conducive to timely understanding and rescue of destructive ground objects after disaster. [ABSTRACT FROM AUTHOR]

Change detection in remote sensing enables identifying alterations in surface characteristics over time, underpinning diverse applications. However, conventional pixel-based algorithms encounter constraints in terms of accuracy when applied to medium- and high-resolution remote sensing images. Although object-oriented methods offer a step forward, they frequently grapple with missing small objects or handling complex features effectively. To bridge these gaps, this paper proposes an unsupervised object-oriented change detection approach empowered by hierarchical multi-scale segmentation for generating binary ecosystem change maps. This approach meticulously segments images into optimal sizes and leverages multidimensional features to adapt the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) algorithm for GaoFen WFV data. We rigorously evaluated its performance in the Yellow River Source Region, a critical ecosystem conservation zone. The results unveil three key strengths: (1) the approach achieved excellent object-level change detection results, making it particularly suited for identifying changes in subtle features; (2) while simply increasing object features did not lead to a linear accuracy gain, optimized feature space construction effectively mitigated dimensionality issues; and (3) the scalability of our approach is underscored by its success in mapping the entire Yellow River Source Region, achieving an overall accuracy of 90.09% and F-score of 0.8844. Furthermore, our analysis reveals that from 2015 to 2022, changed ecosystems comprised approximately 1.42% of the total area, providing valuable insights into regional ecosystem dynamics. [ABSTRACT FROM AUTHOR]

Accurately monitoring the assembly sequence of mechanical parts is significant for reducing assembly process errors and improving assembly accuracy. To monitor the newly assembled parts of mechanical assemblies from multiple views, this paper proposes a multi-view change detection network based on feature fusion and feature refinement with depthwise separable convolution (DWFR Net) for mechanical assembly images. Based on the BIT (Bitemporal Image Transformer) network, the DWFR Net adds a feature fusion structure to the convolutional neural network module to reduce the loss of shallow features due to the increase in network depth. Then, a feature refinement module is designed to refine the features extracted by the convolutional neural network and make the Q of the Transformer decoder more accurate. The depthwise separable convolution with a convolution kernel of 5 × 5 is used in the feature fusion structure and feature refinement module. It reduces the number of parameters added by the large convolution kernel while increasing the receptive field. Finally, the DWFR Net is evaluated on a mechanical assembly image change detection dataset and a public dataset LEVIR-CD. The experimental results show that the F1 of the DWFR Net can reach 95.60% on the mechanical assembly image change detection dataset and 90.12% on the LEVIR-CD dataset, and the latter is 0.81% higher than that of the BIT network. [ABSTRACT FROM AUTHOR]

India's metropolitan cities have been growing rapidly for many years. To keep geographical information accurate and current, it is essential to update GIS maps. Traditionally, experts have analysed new data sources and made necessary adjustments to the maps manually. Such manual monitoring is a laborious test both economically and in terms of workforce. Geographical data are transformed into digital maps by GIS mapping, making it simple to spot patterns, trends and linkages. Extraction of humanmade objects, such as roads, water bodies and buildings, from remotely sensed imageries holds significance in various urban applications, including urban land-use and land-cover assessment, geographical database updates and change detection. Cartosat-3 data can provide detailed information about buildings and their changes over time. Additionally, GIS maps are manually updated by rasterizing vector data. The suggested system consists of ResNet and U-Net architecture as its core. The bi-temporal images are initially coregistered to completely align 2020 and 2022 satellite images with respect to the coordinates. Buildings are then segmented using U-Net with ResNet as the backbone, and the resultant segments are converted from raster to vector format. The suggested model has been tested and trained using the Chandigarh dataset, which resulted in an accuracy of 95%. [ABSTRACT FROM AUTHOR]

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF's disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts' effort saved could be over 95%. The regional model performance evaluation showed that SAFER's performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data. [ABSTRACT FROM AUTHOR]

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR's performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring. [ABSTRACT FROM AUTHOR]

Change detection (CD) stands out as a pivotal yet challenging task in the interpretation of remote sensing images. Significant developments have been witnessed, particularly with the rapid advancements in deep learning techniques. Nevertheless, challenges such as incomplete detection targets and unsmooth boundaries remain as most CD methods suffer from ineffective feature fusion. Therefore, this paper presents a multi-scale gated fusion network (MSGFNet) to improve the accuracy of CD results. To effectively extract bi-temporal features, the EfficientNetB4 model based on a Siamese network is employed. Subsequently, we propose a multi-scale gated fusion module (MSGFM) that comprises a multi-scale progressive fusion (MSPF) unit and a gated weight adaptive fusion (GWAF) unit, aimed at fusing bi-temporal multi-scale features to maintain boundary details and detect completely changed targets. Finally, we use the simple yet efficient UNet structure to recover the feature maps and predict results. To demonstrate the effectiveness of the MSGFNet, the LEVIR-CD, WHU-CD, and SYSU-CD datasets were utilized, and the MSGFNet achieved F1 scores of 90.86%, 92.46%, and 80.39% on the three datasets, respectively. Furthermore, the low computational costs and small model size have validated the superior performance of the MSGFNet. [ABSTRACT FROM AUTHOR]

Change detection (CD) in remote sensing imagery has found broad applications in ecosystem service assessment, disaster evaluation, urban planning, land utilization, etc. In this paper, we propose a novel graph model-based method for synthetic aperture radar (SAR) image CD. To mitigate the influence of speckle noise on SAR image CD, we opt for comparing the structures of multi-temporal images instead of the conventional approach of directly comparing pixel values, which is more robust to the speckle noise. Specifically, we first segment the multi-temporal images into square patches at multiple scales and construct multi-scale K-nearest neighbor (KNN) graphs for each image, and then develop an effective graph fusion strategy, facilitating the exploitation of multi-scale information within SAR images, which offers an enhanced representation of the complex relationships among features in the images. Second, we accomplish the interaction of spatio-temporal-radiometric information between graph models through graph mapping, which can efficiently uncover the connections between multi-temporal images, leading to a more precise extraction of changes between the images. Finally, we use the Markov random field (MRF) based segmentation method to obtain the binary change map. Through extensive experimentation on real datasets, we demonstrate the remarkable superiority of our methodologies by comparing with some current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Land use change is a global issue, and different regions of Bangladesh are experiencing land use changes at different scales. The Sundarbans is one of the largest mangrove forests that provide resources to the local communities, act as a sink for CO2, and protect the coastal zone from natural disasters. Sundarbans is also being affected by land use change and deteriorating rapidly, posing a threat to mangroves and wildlife. This is mainly driven by the unplanned expansion of open land, deforestation, and filling up the water bodies. Evaluating the shoreline food web and the interaction of water and land necessitates quantitatively assessing shoreline movement patterns over time. This study aimed to understand the spatial–temporal changes in shoreline and vegetation from 1981 to 2021 considering the mangrove cover along the Sundarbans in Bangladesh. To fulfill the objectives, the Digital Shoreline Analysis System was used to measure the shoreline change, and the vegetation changes were assessed by using the Normalized Difference Vegetation Index (NDVI) using Landsat satellite data. The analysis of NDVI revealed that the mangrove stress level is increasing rapidly. In the last 20 years, the high-level vegetation has decreased by 5.01%, the moderate level by 9.61% & the low level by 6.99%. Moreover, the shoreline change analysis found that from 1980 to 1990 the erosion was 143.95 sq. km and the accretion was 110.9 sq. km. The findings of this study may help policymakers make informed decisions, and improve their plans for the sustainable development of the region. [ABSTRACT FROM AUTHOR]

The population pressure and rapid urbanization in the Southern Punjab of Pakistan have led to massive landuse transformations in the recent years, especially in the Multan and Bahawalpur district. These frequent landuse alterations disrupt the crop yield and indirectly affect the livelihoods and economy at the national level. With an aim to assess the agricultural shifts in cotton landscapes of Multan and Bahawalpur districts over two decades, the advanced classification techniques of Machine Learning Algorithms (MLA), Random Forest (RF) was used. One year composite of monthly time series images were taken along with Normalized Difference Vegetation Index in Google Earth Engine (GEE) for the selected years (2000, 2005, 2010, 2015, and 2020). The Landuse/Landcover classes extracted through satellite imagery included cropland, orchards, settlement, water, and other land for both districts, while forest class was extracted for Bahawalpur district only. The results indicated that the use of MLA, RF classifier highlights the variations in cotton landscapes with an accuracy greater than 85%. It was observed that the cropland and other lands in Multan reduced by 3.03% and 20.33%, respectively, while the classes such as orchard, wetland, and settlement significantly increased by 66.1%, 40.3%, and 75.5%, respectively. Similarly, in Bahawalpur an overall increase was observed in croplands, forests, water bodies, and settlements over the two decades. It can be confidently concluded that GEE has become a viable alternative to conventional methodologies for remote sensing applications while providing a comparatively higher degree of confidence in results. [ABSTRACT FROM AUTHOR]

Objective: This study investigated the effect of the spatial disorientation (SD) events on an attentive blank stare in the cockpit scene and demonstrated how much the flight task and visual delayed discrimination task were competing for the pilots' attention. Background: SD in flight is the leading cause of human error-related aircraft accidents in the military, general and commercial aviation, and has been an unsolved problem since the inception of flight. In-flight safety research, visually scanning cockpit instruments, and detecting changes are critical countermeasures against SD. Method: Thirty male military pilots were performing a dual task involving piloting a flight simulator and visual change detection, while eye movements were obtained using an eye tracker. Results: Pilots made more flight errors and spent less time gazing at the area of change in SD-conflict than in non-conflict flights. The vestibular origin SD-conflict led not only to deteriorated piloting and visual scanning but also to problems coordinating overt and covert attention, resulting in lower noticeability of visual changes despite gazing at them. Conclusion: Our study shows that looking at a given area in space is not a sufficient condition for effective covert attention allocation and the correct response to a visual stimulus. It seems to be important to make pilots aware of this during SD training. Application: To reduce change blindness, some strategies, such as reducing the number of secondary tasks is extremely valuable. Particular efforts should also be focused on improving the design of the aircraft cockpit by increasing the conspicuousness of critical information. [ABSTRACT FROM AUTHOR]

Remote sensing change detection (RSCD), which aims to identify surface changes from bitemporal images, is significant for many applications, such as environmental protection and disaster monitoring. In the last decade, driven by the wave of artificial intelligence, many change detection methods based on deep learning emerged and have achieved essential breakthroughs. However, these methods pay more attention to visual representation learning while ignoring the potential of multimodal data. Recently, the foundation vision-language model, i.e. CLIP, has provided a new paradigm for multimodal AI, demonstrating impressive performance on downstream tasks. Following this trend, in this study, we introduce ChangeCLIP, a novel framework that leverages robust semantic information from image-text pairs, specifically tailored for Remote Sensing Change Detection (RSCD). Specifically, we reconstruct the original CLIP to extract bitemporal features and propose a novel differential features compensation module to capture the detailed semantic changes between them. Besides, we proposed a vision-language-driven decoder by combining the results of image-text encoding with the visual features of the decoding stage, thereby enhancing the image semantics. The proposed ChangeCLIP achieved state-of-the-art IoU on 5 well-known change detection datasets, LEVIR-CD (85.20%), LEVIR-CD+ (75.63%), WHUCD (90.15%), CDD (95.87%) and SYSU-CD (71.41%). The code and the pretrained models of ChangeCLIP will be publicly available on https://github.com/dyzy41/ChangeCLIP. [ABSTRACT FROM AUTHOR]

Climate change in northern latitudes is increasing the vulnerability of peatlands and the riparian transition zones between peatlands and upland forests (referred to as ecotones) to greater frequency of wildland fires. We examined early post‐fire vegetation regeneration following the 2011 Utikuma complex fire (central Alberta, Canada). This study examined 779 peatlands and adjacent ecotones, covering an area of ~182 km2. Based on the known regional fire history, peatlands that burned in 2011 were stratified into either long return interval (LRI) fire regimes of >80 years (i.e., no recorded prior fire history) or short fire return interval (SRI) of 55 years (i.e., within the boundary of a documented severe fire in 1956). Data from six multitemporal airborne lidar surveys were used to quantify trajectories of vegetation change for 8 years prior to and 8 years following the 2011 fire. To date, no studies have quantified the impacts of post‐fire regeneration following short versus long return interval fires across this broad range of peatlands with variable environmental and post‐fire successional trajectories. We found that SRI peatlands demonstrated more rapid vascular and shrub growth rates, especially in peatland centers, than LRI peatlands. Bogs and fens burned in 1956, and with little vascular vegetation (classified as "open peatlands") prior to the 2011 fire, experienced the greatest changes. These peatlands tended to transition to vascular/shrub forms following the SRI fire, while open LRI peatlands were not significantly different from pre‐fire conditions. The results of this study suggest the emergence of a positive feedback, where areas experiencing SRI fires in southern boreal peatlands are expected to transition to forested vegetation forms. Along fen edges and within bog centers, SRI fires are expected to reduce local peatland groundwater moisture‐holding capacity and promote favorable conditions for increased fire frequency and severity in the future. [ABSTRACT FROM AUTHOR]

A key source of information for many decision support systems is identifying land use and land cover (LULC) based on remote sensing data. Land conservation, sustainable development, and water resource management all benefit from the knowledge obtained from detecting changes in land use and land cover. The present study aims to investigate the multi-decadal coastal change detection for Ras El-Hekma and El-Dabaa area along the Mediterranean coast of Egypt, a multi-sectoral development area. Besides, the superiority of the area is highly dependent on its proximity to three development projects: the tourism and urban growth pole at Ras El-Hekma, the beachfront Alamain New Mega City, and the Nuclear Power Plant at El Dabaa. This study utilized multi-spectral Landsat satellite images covering 1990, 2010, and 2020 to perceive the post-classification change detection analysis of the land use and land cover changes (LULCC) over 30 years. The results of the supervised classification from 1990 to 2020 showed a 47.33 km2 (4.13%) expansion of the agricultural land area, whereas the bare soil land area shrunk to 73.13 km2 (6.24%). On the other hand, the built-up activities in the area launched in 2010 and escalated to 20.51 km2(1.77%) in 2020. The change in land use reveals the shift in the economic growth pattern in the last decade toward tourism and urban development. Meanwhile, it indicates that no conflict has yet arisen regarding the land use between the expanded socioeconomic main sectors (i.e., agriculture, and tourism). Therefore, the best practices of land use management and active participation of the stakeholders and the local community should be enhanced to achieve sustainability and avoid future conflicts. An area-specific plan including resource conservation measures and the provision of livelihood alternatives should be formulated within the National Integrated Coastal Zone Management (ICZM) plan with the participation of the main stakeholders and beneficiaries. The findings of the present work may be considered useful for sustainable management and supportive to the decision-making process for the sustainable development of this area. [ABSTRACT FROM AUTHOR]

With the powerful discriminative capabilities of convolutional neural networks, change detection has achieved significant success. However, current methods either ignore the spatiotemporal dependencies between dual-temporal images or suffer from decreased accuracy due to registration errors. Addressing these challenges, this paper proposes a method for remote sensing image change detection based on the cross-mixing attention network. To minimize the impact of registration errors on change detection results, a feature alignment module (FAM) is specifically developed in this study. The FAM performs spatial transformations on dual-temporal feature maps, achieving the precise spatial alignment of feature pairs and reducing false positive rates in change detection. Additionally, to fully exploit the spatiotemporal relationships between dual-temporal images, a cross-mixing attention module (CMAM) is utilized to extract global channel information, enhancing feature selection capabilities. Furthermore, attentional maps are created to guide the up-sampling process, optimizing feature information. Comprehensive experiments conducted on the LEVIR-CD and SYSU-CD change detection datasets demonstrate that the proposed model achieves F1 scores of 91.06% and 81.88%, respectively, outperforming other comparative models. In conclusion, the proposed model maintains good performance on two datasets and, thus, has good applicability in various change detection tasks. [ABSTRACT FROM AUTHOR]

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Change Detection (CD) provides a research basis for environmental monitoring, urban expansion and reconstruction as well as disaster assessment, by identifying the changes of ground objects in different time periods. Traditional CD focused on the Binary Change Detection (BCD), focusing solely on the change and no-change regions. Due to the dynamic progress of earth observation satellite techniques, the spatial resolution of remote sensing images continues to increase, Multi-class Change Detection (MCD) which can reflect more detailed land change has become a hot research direction in the field of CD. Although many scholars have reviewed change detection at present, most of the work still focuses on BCD. This paper focuses on the recent progress in MCD, which includes five major aspects: challenges, datasets, methods, applications and future research direction. Specifically, the background of MCD is first introduced. Then, the major difficulties and challenges in MCD are discussed and delineated. The benchmark datasets for MCD are described, and the available open datasets are listed. Moreover, MCD is further divided into three categories and the specific techniques are described, respectively. Subsequently, the common applications of MCD are described. Finally, the relevant literature in the main journals of remote sensing in the past five years are analyzed and the development and future research direction of MCD are discussed. This review will help researchers understand this field and provide a reference for the subsequent development of MCD. Our collections of MCD benchmark datasets are available at: [ABSTRACT FROM AUTHOR]

The world we live in is dynamic by nature. Frequently, the environment changes in ways we cannot predict. In machine learning, the phenomenon that occurs when a model has its prediction effectiveness degraded due to unforeseen changes is known as concept drift. Applications of smart video surveillance tend to suffer from concept drift due to changes in illumination, weather, and scene structure. This work differs from previous ones as it brings focus to the problem of concept drift from a surveillance video perspective which presents additional challenges compared to other sources of data, such as high dimensionality, spatial and temporal relations between data, and real-time constraints. The approaches and algorithms used to cope with concept drift are compared and discussed. We also present datasets and metrics used to evaluate the effectiveness of the algorithms. As contributions, we present a new classification of concept drift adaptation methods, delineate the characteristics and limitations of techniques that deal with concept drift, and analyze practical aspects, such as real-time processing and memory constraints. Moreover, we conclude that informed concept drift adaptation methods have been employed 90% less than continuous adaptation ones. Research directions include using established concept drift detection techniques applied to surveillance video data, exploring datasets for concept drift in surveillance, strategies to deal with the high dimensionality and volume of surveillance video data when adapting existing models, and the creation of frameworks to manage drift adaptation while applying computer vision tasks. [ABSTRACT FROM AUTHOR]

Recognizing the change over the earth's surface in the context of remote sensing identities needs to find the differences between the images. The manual execution of this task will be erroneous and incompetent to scale. To automate this process, we propose a novel framework that uses a customized Siamese-based neural network pipeline (CSNN) to detect the changes over the earth's surface. Based on the spatiotemporal analysis, the proposed work leverage's the ancient divide and conquers strategy. The image is divided into sub-image, and the feature maps are extracted using convolution layers that detect the fine-grained changes which occur at the sub-image level. The proposed methodology uses the LEVIR-CD dataset, which has 637 images. The performance of the proposed change detection method increases, as the input image size gets reduced, this is experimentally proved by decreasing the original image size into sub-images of size 2 x 2. The best performance achieved at 2 x 2 sized sub-image is 94.15%, 87.0%, 92.0%, 89.40% for accuracy, precision, recall, and F1-score respectively, which outperforms the results of the baseline algorithm. Further, the proposed framework performs well on different terrains, with varying amounts and types of changes in the satellite image. [ABSTRACT FROM AUTHOR]

Marine communities are facing both natural disturbances and anthropogenic stressors. Bioconstructor species are endangered by multiple large-scale and local pressures and the early identification of impacts and damages is a primary goal for preserving coral reefs. Taking advantage of the recent development in underwater photogrammetry, the use of photogrammetry and fluorimetry was coupled to design, test and validate in laboratory a multi-sensor measuring system that could be potentially exploited in open water by SCUBA divers for assessing the health status of corals and detecting relevant biometric parameters with high accuracy and resolution. The approach was tested with fragments of the endemic coral Cladocora caespitosa, the sole zooxanthellate scleractinian reef-builder in the Mediterranean. The most significant results contributing to the scientific advancement of knowledge were: 1) the development of a cost-effective, flexible and easy-to-use approach based on emerging technologies; 2) the achievement of a sub-centimetric resolution for measuring relevant biometric parameters (polyp counting, colony surface areas and volumes); 3) set up of a reliable and repeatable strategy for multitemporal analyses capable of quantifying changes in coral morphology with subcentimeter accuracy; 4) detect changes in coral health status at a fine scale and under natural lighting through autofluorescence analysis. The novelty of the present research lies in the coupling of emerging techniques that could be applied to a wide range of 3D morphometrics, different habitats and species, thus paving the way to innovative opportunities in ecological research and more effective results than traditional in-situ measurements. Moreover, the possibility to easily modify the developed system to be installed on an underwater remotely operated vehicle further highlights the possible concrete impact of the research for ecological monitoring and protection purposes. [ABSTRACT FROM AUTHOR]

Natural gas pipelines represent a critical infrastructure for most countries and thus their safety is of paramount importance. To report potential risks along pipelines, several steps are taken such as manual inspection and helicopter flights; however, these solutions are expensive and the flights are environmentally unfriendly. Deep learning has demonstrated considerable potential in handling a number of tasks in recent years as models rely on huge datasets to learn a specific task. With the increasing number of satellites orbiting the Earth, remote sensing data have become widely available, thus paving the way for automated pipeline monitoring via deep learning. This can result in effective risk detection, thereby reducing monitoring costs while being more precise and accurate. A major hindrance here is the low resolution of images obtained from the satellites, which makes it difficult to detect smaller changes. To this end, we propose to use transformers trained with low-resolution images in a change detection setting to detect pipeline risks. We collect PlanetScope satellite imagery (3 m resolution) that captures certain risks associated with the pipelines and present how we collected the data. Furthermore, we compare various state-of-the-art models, among which ChangeFormer, a transformer architecture for change detection, achieves the best performance with a 70% F1 score. As part of our evaluation, we discuss the specific performance requirements in pipeline monitoring and show how the model's predictions can be shifted accordingly during training. [ABSTRACT FROM AUTHOR]

The explosive rate of population growth demands a revision of existing protective measures to address water scarcity that urges water body monitoring models to be developed using satellite image-based change detection approaches. And the main objective of this proposed work was to investigate the spectral indices for spatial object extraction in satellite images with respect to water body extraction. The Landsat 8 coastal/aerosol band (0.433–0.453 μm) is still an unexplored band with spectral signatures that favor water extraction, and it has been used in the proposed deep blue normalized difference water index (DBNDWI). The multi-temporal Landsat 8 data products of three lakes with distinct geographical importance from India and Iran are chosen for assessment. Widely used spectral indices for water body extraction such as the normalized difference water index (NDWI), modified normalized difference water index, and automated water extraction index are the benchmark results used for comparative assessment. Along with these conventional water indices, the most recent water indices weighted normalized difference water index (WNDWI) and Wavelet-based normalized difference water index (WAWI) are also compared for extended validation. The use of standard image quality analysis and statistical histogram distance measures justifies the significance of coastal band in extracting water bodies. The experimental results tabulated show that proposed DBNDWI outperforms the most recent WNDWI and WAWI with higher accuracy, even in moderate-resolution images. [ABSTRACT FROM AUTHOR]

Studying the landscape change depending on human activities and associating this change with activity is important for making the appropriate planning decisions. In this study, it was aimed to determine how an activity that seriously damages to the environment, such as a thermal power plant, causes a change in CORINE Land Cover/Land Uses (LC/LU) classes between 1990 and 2018. In this context, it has all the units of the Yeniköy-Kemerköy Thermal power plant within the lower basin boundary, which has been selected as the study area and is considered an ecological boundary. The landscape types of the study area, which consists of a total of two micro-catchments, a sub-basin and a separate basin, were determined by the 2018 CORINE LC/LU classes and Forest Stand types. As a result of the change detection carried out with the ArcGIS 10.6.1 program, supported by Geographic Information Systems (GIS), the change and transformation between LC/LU classes were examined. Diversity indices were calculated by considering the number of units and area sizes of the determined landscape types. The effect of such measurements on landscape management has been discussed by mathematically demonstrating how this change affects landscape diversity through diversity indices. According to the calculated Shannon and Simpson Diversity Indices, the diversity of the area is high both in the whole area and in the micro-catchment's basis. It has been discussed in the results section of the study that this high diversity is not a diversity that includes ecological richness in the expected sense and that it is a result of fragmentation caused by the activity. [ABSTRACT FROM AUTHOR]

Change detection from heterogeneous satellite and aerial images plays a progressively important role in many fields, including disaster assessment, urban construction, and land use monitoring. Currently, researchers have mainly devoted their attention to change detection using homologous image pairs and achieved many remarkable results. It is sometimes necessary to use heterogeneous images for change detection in practical scenarios due to missing images, emergency situations, and cloud and fog occlusion. However, heterogeneous change detection still faces great challenges, especially using satellite and aerial images. The main challenges in satellite and aerial image change detection are related to the resolution gap and blurred edge. Previous studies used interpolation or shallow feature alignment before traditional homologous change detection methods, which ignored the high-level feature interaction and edge information. Therefore, we propose a new heterogeneous change detection model based on multimodal transformers combined with edge guidance. In order to alleviate the resolution gap between satellite and aerial images, we design an improved spatially aligned transformer (SP-T) with a sub-pixel module to align the satellite features to the same size of the aerial ones supervised by a token loss. Moreover, we introduce an edge detection branch to guide change features using the object edge with an auxiliary edge-change loss. Finally, we conduct considerable experiments to verify the effectiveness and superiority of our proposed model (EGMT-CD) on a new satellite–aerial heterogeneous change dataset, named SACD. The experiments show that our method (EGMT-CD) outperforms many previously superior change detection methods and fully demonstrates its potential in heterogeneous change detection from satellite–aerial images. [ABSTRACT FROM AUTHOR]

Research on high-temporal-resolution rock slope monitoring has tended to focus on scenarios where spatial resolution is also high. Accordingly, there is a lack of understanding of the implications for rock slope monitoring results in cases with high temporal resolution but low spatial resolution, which is the focus of this study. This study uses automatically captured photos taken at a daily frequency by five fixed-base cameras in conjunction with multi-epoch Structure-from-Motion (SfM) photogrammetric processing techniques to evaluate changes in a rock slope in Majes, Arequipa, Peru. The results of the monitoring campaign demonstrate that there are potential issues with the common notion that higher frequency change detection is always superior. For lower spatial resolutions or when only large changes are of concern, using a high-frequency monitoring method may cause small volume changes that eventually aggrade into larger areas of change to be missed, whereas most of the total volume change would be captured with lower-frequency monitoring intervals. In this study, daily change detection and volume calculation resulted in a cumulative rockfall volume of 4300 m3 over about 14 months, while change detection and volume calculation between dates at the start and end of the 14-month period resulted in a total rockfall volume of 12,300 m3. High-frequency monitoring is still the most accurate approach for evaluating slope evolution from a rockfall frequency and size distribution perspective, and it allows for the detection of short accelerations and pre-failure deformations, but longer-term comparison intervals may be required in cases where spatial resolution is low relative to temporal resolution to more accurately reflect the total volume change of a given rock slope over a long period of time. [ABSTRACT FROM AUTHOR]

Touch is unique among the sensory modalities in that our tactile receptors are spread across the body surface and continuously receive different inputs at the same time. These inputs vary in type, properties, relevance according to current goals, and, of course, location on the body. Sometimes, they must be integrated, and other times set apart and distinguished. Here, we investigate how simultaneous stimulation to different body sites affects tactile cognition. Specifically, we characterized the impact of irrelevant tactile sensations on tactile change detection. To this end, we embedded detection targets amidst ongoing performance, akin to the conditions encountered in everyday life, where we are constantly confronted with new events within ongoing stimuli. In the set of experiments presented here, participants detected a brief intensity change (.04 s) within an ongoing vibrotactile stimulus (1.6 s) that was always presented in a constantly attended location. The intensity change (i.e., the detection target) varied parametrically, from hardly detectable to easily detectable. In half of the trials, irrelevant ongoing stimulation was simultaneously presented to a site across the body midline, but participants were instructed to ignore it. In line with previous bimanual studies employing brief onset targets, we document robust interference on performance due to the irrelevant stimulation at each of the measured body sites (homologous and nonhomologous fingers, and the contralateral ankle). After describing this basic phenomenon, we further examine the conditions under which such interference occurs in three additional tasks. In each task, we honed in on a different aspect of the stimulation protocol (e.g., hand distance, the strength of the irrelevant stimulation, the detection target itself) in order to better understand the principles governing the observed interference effects. Our findings suggest a minimal role for exogenous attentional capture in producing the observed interference effects (Exp. 2), and a principled distribution of attentional resources or sensory integration between body sides (Exps. 3, 4). In our last study (Exp. 4), we presented bilateral tactile targets of varying intensities to both the relevant and irrelevant stimulation sites. We then characterized the degree to which the irrelevant stimulation is also processed. Our results—that participants' perception of target intensity is always proportional to the combined bilateral signal—suggest that both body sites are equally weighed and processed despite clear instructions to attend only the target site. In light of this observation and participants' inability to use selection processes to guide their perception, we propose that bilateral tactile inputs are automatically combined, quite possibly early in the hierarchy of somatosensory processing. [ABSTRACT FROM AUTHOR]

The expected color of an object influences how it is perceived. For example, a banana in a greyscale photo may appear slightly yellow because bananas are expected to be yellow. This phenomenon is known as the memory color effect (MCE), and the objects with a memory color are called "color-diagnostic." The MCE is theorized to be a top-down influence of color knowledge on visual perception. However, its validity has been questioned because most evidence for the MCE is based on subjective reports. Here a change detection task is used as an objective measure of the effect and the results show that change detection differs for color-diagnostic objects. Specifically, it was predicted and found that unnaturally colored color-diagnostic objects (e.g., a blue banana) would attract attention and thus be discovered more quickly and accurately. In the experiment, two arrays alternated with the target present in one array and absent in the other while all other objects remained unchanged. Participants had to find the target as quickly and accurately as possible. In the experimental condition, the targets were color-diagnostic objects (e.g., a banana) presented in either their natural (yellow) or an unnatural (blue) color. In the control condition, non-color-diagnostic objects (e.g., a mug) were presented with the same colors as the color-diagnostic objects. Unnaturally colored color-diagnostic objects were found more quickly, which suggests that the MCE is a top-down, preattentive process that can influence a nonsubjective visual perceptual task such as change detection. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to identify changes in the vegetation cover of the geopark M’goun region of Morocco, as a sample area to track the Moroccan vegetation status, and the crucial factors that influenced its evolution between 1984 and 2021. The NDVI (Normalized Difference Vegetation Index) method has been used, combined with supervised classification manipulated with the Google Earth Engine and through statistical analysis using R, together with field trips and Google Earth records for supervised classification. The percentage of vegetation density, made up of oak groves, Matorrals, red juniper, and thuja, in addition to a highly biodiverse cover, displays a declining trend. Mediumcover vegetation decreased by 29.5%, dense vegetation decreased considerably by 70.9%, and low-cover vegetation saw a minimal decline of 0.02%. As a result, dense and medium vegetation are being replaced by bare lands or poor-quality cover formed by sparse plants and solitary trees. In addition, precipitation increased slightly, showing an irregular trend, with average minimum and maximum temperatures rising by +1.7°C and +1.4°C, respectively. Furthermore, the population increased by 84.47%. Statistical analysis showed that the most important parameters affecting medium and dense vegetation cover are temperatures (Tmin and Tmax) and population density, as evidenced by the strong correlation between them. In contrast, sparse vegetation cover showed less correlation due to its heterogeneity. However, precipitation played a minimal role in vegetation evolution. Change detection maps revealed a significant negative impact on vegetation cover. This degradation was localized in the southern part of the area of study, where, according to the demographic density map, the majority of the population resides. Deforestation continues due to the lifestyle and economic activities of the local population. These factors, combined with climate change, are exerting large-scale pressure on the forest throughout the geopark. [ABSTRACT FROM AUTHOR]

With the rapid expansion of urbanization, it is imperative to monitor builtup areas changes to promote the sustainable development of cities, aligning with the goals of Sustainable Development Goal 11(SDG 11). Remote sensing big data is valuable for automatically mapping these changes. Bi-temporal very high-resolution (Bi-VHR) optical images have been widely utilized for fine-grained change detection (CD). However, the significant spatiotemporal inconsistency due to imaging conditions and seasonal variations poses challenges for VHR optical CD. Unlike optical images, synthetic aperture radar (SAR) images are unaffected by atmospheric interference and provide robust spatiotemporal features as a supplement. Previous CD algorithms with SAR overlooked the exploration of long-term features of time series. In this study, we propose a novel CD framework combining long-term SAR with Bi-VHR images. It incorporates a spatial-frequency learning module to enhance SAR temporal features and a multisource feature fusion module to adaptively fuse the heterogeneous features. The experiments are conducted on OS-BCD dataset, which is the first dataset specifically designed for this task. The results demonstrate that our proposal outperforms advanced CD methods with F1 score, IoU and OA of 64.99%, 48.13%, and 95.11%, validating the efficacy of our proposal in accurately detecting changes in built-up areas. [ABSTRACT FROM AUTHOR]