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The National Forestland "One Map" applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in "One Map", taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for "One Map" (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in "One Map" and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data. [ABSTRACT FROM AUTHOR]

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task. [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]

As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the widespread applications of change detection technology in urban change analysis, environmental monitoring, agricultural surveillance, disaster detection, and other domains, the task of change detection has become one of the primary applications of Earth orbit satellite remote sensing data. However, the analysis of dual-temporal change detection (CD) remains a challenge in high-resolution optical remote sensing images due to the complexities in remote sensing images, such as intricate textures, seasonal variations in imaging time, climatic differences, and significant differences in the sizes of various objects. In this paper, we propose a novel U-shaped architecture for change detection. In the encoding stage, a multi-branch feature extraction module is employed by combining CNN and transformer networks to enhance the network's perception capability for objects of varying sizes. Furthermore, a multi-branch aggregation module is utilized to aggregate features from different branches, providing the network with global attention while preserving detailed information. For dual-temporal features, we introduce a spatiotemporal discrepancy perception module to model the context of dual-temporal images. Particularly noteworthy is the construction of channel attention and token attention modules based on the transformer attention mechanism to facilitate information interaction between multi-level features, thereby enhancing the network's contextual awareness. The effectiveness of the proposed network is validated on three public datasets, demonstrating its superior performance over other state-of-the-art methods through qualitative and quantitative experiments. [ABSTRACT FROM AUTHOR]

Heterogeneous image change detection is a very practical and challenging task because the data in the original image have a large distribution difference and the labeled samples of the remote sensing image are usually very few. In this study, we focus on solving the issue of comparing heterogeneous images without supervision. This paper first designs a self-paced multi-scale joint feature mapper (SMJFM) for the mapping of heterogeneous data to similar feature spaces for comparison and incorporates a self-paced learning strategy to weaken the mapper's capture of non-consistent information. Then, the difference information in the output of the mapper is evaluated from two perspectives, namely noise robustness and detail preservation effectiveness; then, the change detection problem is modeled as a multi-objective optimization problem. We decompose this multi-objective optimization problem into several scalar optimization subproblems with different weights, and use particle swarm optimization to optimize these subproblems. Finally, the robust evaluation strategy is used to fuse the multi-scale change information to obtain a high-precision binary change map. Compared with previous methods, the proposed SMJFM framework has the following three main advantages: First, the unsupervised design alleviates the dilemma of few labels in remote sensing images. Secondly, the introduction of self-paced learning enhances SMJFM's capture of the unchanged region mapping relationship between heterogeneous images. Finally, the multi-scale change information fusion strategy enhances the robustness of the framework to outliers in the original data. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection. [ABSTRACT FROM AUTHOR]

The development of remote sensing technology has redefined the approaches to the Earth's surface monitoring. The Copernicus Programme promoted by the European Space Agency (ESA) and the European Union (EU), through the launch of the Synthetic Aperture Radar (SAR) Sentinel-1 and the multispectral Sentinel-2 satellites, has provided a valuable contribution to monitoring the Earth's surface. There are several review articles on the land use/land cover (LULC) matter using Sentinel images, but it lacks a methodical and extensive review in the specific field of land consumption monitoring, concerning the application of SAR images, in particular Sentinel-1 images. In this paper, we explored the potential of Sentinel-1 images to estimate land consumption using mathematical modeling, focusing on innovative approaches. Therefore, this research was structured into three principal steps: (1) searching for appropriate studies, (2) collecting information required from each paper, and (3) discussing and comparing the accuracy of the existing methods to evaluate land consumption and their applied conditions using Sentinel-1 Images. Current research has demonstrated that Sentinel-1 data has the potential for land consumption monitoring around the world, as shown by most of the studies reviewed: the most promising approaches are presented and analyzed. [ABSTRACT FROM AUTHOR]

Change detection is crucial for evaluating land use, land cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity of image content, poses challenges for traditional change detection algorithms in terms of accuracy and applicability. The recent emergence of deep learning methods has led to substantial progress in the field of change detection. However, existing frameworks often involve the simplistic integration of bi-temporal features in specific areas, lacking the fusion of temporal information and semantic details in the images. In this paper, we propose an attention-guided multi-scale fusion network (AMFNet), which effectively integrates bi-temporal image features and diverse semantics at both the encoding and decoding stages. AMFNet utilizes a unique attention-guided mechanism to dynamically adjust feature fusion, enhancing adaptability and accuracy in change detection tasks. Our method intelligently incorporates temporal information into the deep learning model, considering the temporal dependency inherent in these tasks. We decode based on an interactive feature map, which improves the model's understanding of evolving patterns over time. Additionally, we introduce multi-level supervised training to facilitate the learning of fused features across multiple scales. In comparison with different algorithms, our proposed method achieves F1 values of 0.9079, 0.8225, and 0.8809 in the LEVIR-CD, GZ-CD, and SYSU-CD datasets, respectively. Our model outperforms the SOTA model, SAGNet, by 0.69% in terms of F1 and 1.15% in terms of IoU on the LEVIR-CD dataset, by 2.8% in terms of F1 and 1.79% in terms of IoU on the GZ-CD dataset, and by 0.54% in terms of F1 and 0.38% in terms of IoU on the SYSU-CD dataset. The method proposed in this study can be applied to various complex scenarios, establishing a change detection method with strong model generalization capabilities. [ABSTRACT FROM AUTHOR]

Cultivated land plays a fundamental role in the sustainable development of the world. Monitoring the non-agricultural changes is important for the development of land-use policies. A bitemporal image transformer (BIT) can achieve high accuracy for change detection (CD) tasks and also become a key scientific tool to support decision-making. Because of the diversity of high-resolution RSIs in series, the complexity of agricultural types, and the irregularity of hierarchical semantics in different types of changes, the accuracy of non-agricultural CD is far below the need for the management of the land and for resource planning. In this paper, we proposed a novel non-agricultural CD method to improve the accuracy of machine processing. First, multi-resource surveying data are collected to produce a well-tagged dataset with cultivated land and non-agricultural changes. Secondly, a hierarchical semantic aggregation mechanism and attention module (HSAA) bitemporal image transformer method named HSAA-CD is performed for non-agricultural CD in cultivated land. The proposed HSAA-CD added a hierarchical semantic aggregation mechanism for clustering the input data for U-Net as the backbone network and an attention module to improve the feature edge. Experiments were performed on the open-source LEVIR-CD and WHU Building-CD datasets as well as on the self-built RSI dataset. The F1-score, intersection over union (IoU), and overall accuracy (OA) of these three datasets were 88.56%, 84.29%, and 68.50%; 79.84%, 73.41%, and 59.29%; and 98.83%, 98.39%, and 93.56%, respectively. The results indicated that the proposed HSAA-CD method outperformed the BIT and some other state-of-the-art methods and proved to be suitable accuracy for non-agricultural CD in cultivated land. [ABSTRACT FROM AUTHOR]

Change detection is widely used in the field of building monitoring. In recent years, the progress of remote sensing image technology has provided high-resolution data. However, unlike other tasks, change detection focuses on the difference between dual-input images, so the interaction between bi-temporal features is crucial. However, the existing methods have not fully tapped the potential of multi-scale bi-temporal features to interact layer by layer. Therefore, this paper proposes a multi-scale feature interaction network (MFINet). The network realizes the information interaction of multi-temporal images by inserting a bi-temporal feature interaction layer (BFIL) between backbone networks at the same level, guides the attention to focus on the difference region, and suppresses the interference. At the same time, a double temporal feature fusion layer (BFFL) is used at the end of the coding layer to extract subtle difference features. By introducing the transformer decoding layer and improving the recovery effect of the feature size, the ability of the network to accurately capture the details and contour information of the building is further improved. The F1 of our model on the public dataset LEVIR-CD reaches 90.12%, which shows better accuracy and generalization performance than many state-of-the-art change detection models. [ABSTRACT FROM AUTHOR]

Natural hazards are considered to have a strong link with climate change and human activities. With the rapid advancements in remote sensing technology, real-time monitoring and high-resolution remote-sensing images have become increasingly available, which provide precise details about the Earth's surface and enable prompt updates to support risk identification and management. This paper proposes a new network framework with Transformer architecture and a Residual network for detecting the changes in high-resolution remote-sensing images. The proposed model is trained using remote-sensing images from Shandong and Anhui Provinces of China in 2021 and 2022 while one district in 2023 is used to test the prediction accuracy. The performance of the proposed model is evaluated by using five matrices and further compared to both convention-based and attention-based models. The results demonstrated that the proposed structure integrates the great capability of conventional neural networks for image feature extraction with the ability to obtain global context from the attention mechanism, resulting in significant improvements in balancing positive sample identification while avoiding false positives in complex image change detection. Additionally, a toolkit supporting image preprocessing is developed for practical applications. [ABSTRACT FROM AUTHOR]

This paper introduces an AI-based approach to detect human-made objects and changes in these on land parcels. To this end, we used binary image classification performed by a convolutional neural network. Binary classification requires the selection of a decision boundary, and we provided a deterministic method for this selection. Furthermore, we varied different parameters to improve the performance of our approach, leading to a true positive rate of 91.3% and a true negative rate of 63.0%. A specific application of our work supports the administration of agricultural land parcels eligible for subsidiaries. As a result of our findings, authorities could reduce the effort involved in the detection of human made changes by approximately 50%. [ABSTRACT FROM AUTHOR]

Change detection in remote sensing imagery is vital for Earth monitoring but faces challenges such as background complexity and pseudo-changes. Effective interaction between bitemporal images is crucial for accurate change information extraction. This paper presents a multistage interaction network designed for effective change detection, incorporating interaction at the image, feature, and decision levels. At the image level, change information is directly extracted from intensity changes, mitigating potential change information loss during feature extraction. Instead of separately extracting features from bitemporal images, the feature-level interaction jointly extracts features from bitemporal images. By enhancing relevance to spatial variant information and shared semantic channels, the network excels in overcoming background complexity and pseudo-changes. The decision-level interaction combines image-level and feature-level interactions, producing multiscale feature differences for precise change prediction. Extensive experiments demonstrate the superior performance of our method compared to existing approaches, establishing it as a robust solution for remote sensing image change detection. [ABSTRACT FROM AUTHOR]

Remote sensing images change detection technology has become a popular tool for monitoring the change type, area, and distribution of land cover, including cultivated land, forest land, photovoltaic, roads, and buildings. However, traditional methods which rely on pre-annotation and on-site verification are time-consuming and challenging to meet timeliness requirements. With the emergence of artificial intelligence, this paper proposes an automatic change detection model and a crowdsourcing collaborative framework. The framework uses human-in-the-loop technology and an active learning approach to transform the manual interpretation method into a human-machine collaborative intelligent interpretation method. This low-cost and high-efficiency framework aims to solve the problem of weak model generalization caused by the lack of annotated data in change detection. The proposed framework can effectively incorporate expert domain knowledge and reduce the cost of data annotation while improving model performance. To ensure data quality, a crowdsourcing quality control model is constructed to evaluate the annotation qualification of the annotators and check their annotation results. Furthermore, a prototype of automatic detection and crowdsourcing collaborative annotation management platform is developed, which integrates annotation, crowdsourcing quality control, and change detection applications. The proposed framework and platform can help natural resource departments monitor land cover changes efficiently and effectively. [ABSTRACT FROM AUTHOR]

This paper presents a real-time object-based 3D change detection method that is built around the concept of semantic object maps. The algorithm is able to maintain an object-oriented metric-semantic map of the environment and can detect object-level changes between consecutive patrol routes. The proposed 3D change detection method exploits the capabilities of the novel ZED 2 stereo camera, which integrates stereo vision and artificial intelligence (AI) to enable the development of spatial AI applications. To design the change detection algorithm and set its parameters, an extensive evaluation of the ZED 2 camera was carried out with respect to depth accuracy and consistency, visual tracking and relocalization accuracy and object detection performance. The outcomes of these findings are reported in the paper. Moreover, the utility of the proposed object-based 3D change detection is shown in real-world indoor and outdoor experiments. [ABSTRACT FROM AUTHOR]

Change detection (CD) is an important remote sensing (RS) data analysis technology. Existing remote sensing change detection (RS-CD) technologies cannot fully consider situations where pixels between bitemporal images do not correspond well on a one-to-one basis due to factors such as seasonal changes and lighting conditions. Existing networks construct two identical feature extraction branches through convolution, which share weights. The two branches work independently and do not merge until the feature mapping is sent to the decoder head. This results in a lack of feature information interaction between the two images. So, directing attention to the change area is of research interest. In complex backgrounds, the loss of edge details is very important. Therefore, this paper proposes a new CD algorithm that extracts multi-scale feature information through the backbone network in the coding stage. According to the task characteristics of CD, two submodules (the Feature Interaction Module and Detail Feature Guidance Module) are designed to make the feature information between the bitemporal RS images fully interact. Thus, the edge details are restored to the greatest extent while fully paying attention to the change areas. Finally, in the decoding stage, the feature information of different levels is fully used for fusion and decoding operations. We build a new CD dataset to further verify and test the model's performance. The generalization and robustness of the model are further verified by using two open datasets. However, due to the relatively simple construction of the model, it cannot handle the task of multi-classification CD well. Therefore, further research on multi-classification CD algorithms is recommended. Moreover, due to the high production cost of CD datasets and the difficulty in obtaining them in practical tasks, future research will look into semi-supervised or unsupervised related CD algorithms. [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]

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]

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]

The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain. [ABSTRACT FROM AUTHOR]

Change detection is a crucial task in remote sensing that finds broad application in land resource planning, forest resource monitoring, natural disaster monitoring, and evaluation. In this paper, we propose a change detection model for cross-domain recognition, which we call CrossCDNet. Our model significantly improves the modeling ability of the change detection on one dataset and demonstrates good generalization on another dataset without any additional operations. To achieve this, we employ a Siamese neural network for change detection and design an IBNM (Instance Normalization and Batch Normalization Module) that utilizes instance normalization and batch normalization in order to serve as the encoder backbone in the Siamese neural network. The IBNM extracts feature maps for each layer, and the Siamese neural network fuses the feature maps of the two branches using a unique operation. Finally, a simple MLP decoder is used for end-to-end change detection. We train our model on the LEVIR-CD dataset and achieve competitive performance on the test set. In cross-domain dataset testing, CrossCDNet outperforms all the other compared models. Specifically, our model achieves an F1-score of 91.69% on the LEVIR-CD dataset and an F1-score of 77.09% on the WHU-CD dataset, where the training set was LEVIR-CD. [ABSTRACT FROM AUTHOR]

Detecting and monitoring changes in open-pit mines is crucial for efficient mining operations. Indeed, these changes comprise a broad spectrum of activities that can often lead to significant environmental impacts such as surface damage, air pollution, soil erosion, and ecosystem degradation. Conventional optical sensors face limitations due to cloud cover, hindering accurate observation of the mining area. To overcome this challenge, synthetic aperture radar (SAR) images have emerged as a powerful solution, due to their unique ability to penetrate clouds and provide a clear view of the ground. The open-pit mine change detection task presents significant challenges, justifying the need for a model trained for this specific task. First, different mining areas frequently include various features, resulting in a diverse range of land cover types within a single scene. This heterogeneity complicates the detection and distinction of changes within open-pit mines. Second, pseudo changes, e.g., equipment movements or humidity fluctuations, which show statistically reliable reflectivity changes, lead to false positives, as they do not directly correspond to the actual changes of interest, i.e., blasting, collapsing, or waste pile operations. In this paper, to the best of our knowledge, we present the first deep learning model in the literature that can accurately detect changes within open-pit mines using SAR images (TerraSAR-X). We showcase the fundamental role of data augmentations and a coherence layer as a critical component in enhancing the model's performance, which initially relied solely on amplitude information. In addition, we demonstrate how, in the presence of a few labels, a pseudo-labeling pipeline can improve the model robustness, without degrading the performance by introducing misclassification points related to pseudo changes. The F1-Score results show that our deep learning approach is a reliable and effective method for SAR change detection in the open-pit mining sector. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) image change detection aims to detect changes with images of the same area acquired at different times. It has wide applications in environmental monitoring, urban planning and resource management. Traditional change detection methods for spaceborne SAR time-series images typically adopt a pairwise comparison strategy to obtain multi-temporal change information. However, this kind of method has the problem of losing the overall change information, which is time consuming. To address this problem, this paper proposes a new change detection algorithm for spaceborne SAR time-series data based on SAR-SIFT-Logarithm Background Subtraction. This algorithm combines the SAR-SIFT image registration technology with Logarithm Background Subtraction. The method first preprocesses the input time-series data with steps like noise reducing and radiometric calibration. Then, the images will be coregistered by the SAR-SIFT step to avoid mismatches-induced detection performance degradation. Next, the parts that remained unchanged throughout the time period are modeled with a median filter to obtain the static background. The change information is then obtained via the subtraction of background and CFAR detection and clustering. The proposed algorithm is validated using the Sentinel-1 GRD and PAZ-1 time-series dataset. Experimental results demonstrate that the proposed method effectively detects the overall change information and reduces processing time compared to traditional pairwise comparison methods. [ABSTRACT FROM AUTHOR]

Remote sensing change detection involves detecting pixels that have changed from a bi-temporal image of the same location. Current mainstream change detection models use encoder-decoder structures as well as Siamese networks. However, there are still some challenges with this: (1) Existing change feature fusion approaches do not take into account the symmetry of change features, which leads to information loss; (2) The encoder is independent of the change detection task, and feature extraction is performed separately for dual-time images, which leads to underutilization of the encoder parameters; (3) There are problems of unbalanced positive and negative samples and bad edge region detection. To solve the above problems, a mutual feature-aware network (MFNet) is proposed in this paper. Three modules are proposed for the purpose: (1) A symmetric change feature fusion module (SCFM), which uses double-branch feature selection without losing feature information and focuses explicitly on focal spatial regions based on cosine similarity to introduce strong a priori information; (2) A mutual feature-aware module (MFAM), which introduces change features in advance at the encoder stage and uses a cross-type attention mechanism for long-range dependence modeling; (3) A loss function for edge regions. After detailed experiments, the F1 scores of MFNet on SYSU-CD and LEVIR-CD were 83.11% and 91.52%, respectively, outperforming several advanced algorithms, demonstrating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing image change detection technology compares and analyzes bi-temporal or multitemporal high-resolution remote sensing images to determine the change areas. It plays an important role in land cover/use monitoring, natural disaster monitoring, illegal building investigation, military target strike effect analysis, and land and resource investigation. The change detection of high-resolution remote sensing images has developed rapidly from data accumulation to algorithm models because of the rapid development of technologies such as deep learning and earth observation in recent years. However, the current deep learning-based change detection methods are strongly dependent on large sample data, and the training model has insufficient cross-domain generalization ability. As a result, a prior semantic information-guided change detection framework (PSI-CD), which alleviates the change detection model's dependence on datasets by making full use of prior semantic information, is proposed in this paper. The proposed method mainly includes two parts: one is a prior semantic information generation network that uses the semantic segmentation dataset to extract robust and reliable prior semantic information; the other is the prior semantic information guided change detection network that makes full use of prior semantic information to reduce the sample size of the change detection. To verify the effectiveness of the proposed method, we produced pixel-level semantic labels for the bi-temporal images of the public change detection dataset (LEVIR-CD). Then, we performed extensive experiments on the WHU and LEVIR-CD datasets, including comparisons with existing methods, experiments with different amounts of data, and ablation study, to show the effectiveness of the proposed method. Compared with other existing methods, our method has the highest IoU for all training samples and different amounts of training samples on WHU and LEVIR-CD, reaching a maximum of 83.25% and 83.80%, respectively. [ABSTRACT FROM AUTHOR]

To meet the demands of natural resource monitoring, land development supervision, and other applications for high-precision and high-frequency information extraction from constructed land change, this paper focused on automatic feature extraction and data processing optimization methods for newly constructed bare land based on remote sensing images. A generalized deep convolutional neural network change detection model framework integrating multi-scale information was developed for the automatic extraction of change information. To resolve the problems in the automatic extraction of new bare land parcels, such as mis-extractions and parcel fragmentation, a proximity evaluation model that integrates the confidence-based semantic distance and spatial distance between parcels and their overlapping area is proposed to perform parcel aggregation. Additionally, we propose a complete set of optimized processing techniques from pixel pre-processing to vector post-processing. The results demonstrated that the aggregation method developed in this study is more targeted and effective than ArcGIS for the automatically extracted land change parcels. Additionally, compared with the initial parcels, the total number of optimized parcels decreased by more than 50% and the false detection rate decreased by approximately 30%. These results indicate that this method can markedly reduce the overall data volume and false detection rate of automatically extracted parcels through post-processing under certain conditions of the model and samples and provide technical support for applying the results of automatic feature extraction in engineering practices. [ABSTRACT FROM AUTHOR]

In recent years, video research has dealt with high-frame-rate (HFR) content. Even though low or standard frame rates (SFR) that correspond to values less than 60 frames per second (fps) are still covered. Temporal conversions are applied accompanied with video compression and, thus, it is of importance to observe and detect possible effects of typical compressed video manipulations over HFR (60 fps+) content. This paper addresses ultra-high-definition HFR content via Hurst index as a measure of long-range dependency (LRD), as well as using Legendre multifractal spectrum, having in mind standard high-efficiency video coding (HEVC) format and temporal resolution recovery (TRR), meaning frame upconversion after temporal filtering of compressed content. LRD and multifractals-based studies using video traces have been performed for characterization of compressed video, and they are mostly presented for advanced video coding (AVC). Moreover, recent studies have shown that it is possible to perform TRR detection for SFR data compressed with standards developed before HEVC. In order to address HEVC HFR data, video traces are analyzed using LRD and multifractals, and a novel TRR detection model is proposed based on a weighted k-nearest neighbors (WkNN) classifier and multifractals. Firstly, HFR video traces are gathered using six constant rate factors (crfs), where Hurst indices and multifractal spectra are calculated. According to TRR and original spectra comparison, a novel detection model is proposed based on new multifractal features. Also, five-fold cross-validation using the proposed TRR detection model gave high-accuracy results of around 98%. The obtained results show the effects on LRD and multifractality and their significance in understanding changes in typical video manipulation. The proposed model can be valuable in video credibility and quality assessments of HFR HEVC compressed content. [ABSTRACT FROM AUTHOR]

After the end of World War II, many military air bases in Greenland were abandoned with all the material left in place. One of these sites was the Bluie East Two military air base. A specific feature of this area is that it contained thousands of old barrels formerly used for fuel storage. In 2019, a rehabilitation of this area began. A few months prior to the rehabilitation, our expedition visited the area and mapped it using an unmanned aerial vehicle (UAV). This made it probably the latest documentation before the start of the sanitation. The aim of such mapping was to estimate the number of barrels in a given location. The second objective was to monitor the progress of the rehabilitation over the years. For this purpose, satellite data were acquired for the years 2019 through 2022. A supervised classification was performed to automatically detect sites with barrel occurrences, which enabled subsequent change detection. We conclude that a total of 33,786 old barrels were located in the investigated area in 2019. However, we suggest this number is a lower estimate of the actual number of barrels due to the factors we mention in our paper. The results further indicate that between the years 2019 and 2022 the barrels were removed from more than half of the area. [ABSTRACT FROM AUTHOR]

Rockfall presents a significant risk to the safety and economy of communities and infrastructure in mountainous regions. The recently-developed Rockfall Activity Index (RAI) utilizes high-resolution terrestrial lidar-derived digital elevation models (DEMs) of rock slopes to categorize a slope face into seven distinct morphological units, or "RAI classes". This paper focuses on a comprehensive study conducted at four sites in Alaska, USA, where a robust lidar-based five-year inventory of 4381 rockfall events was analyzed. The primary objective was to investigate variations in failure characteristics, such as cumulative magnitude–frequency distributions, non-cumulative power–law parameters, average annual failure rates, and average failure depths, among the different RAI classes. The findings demonstrate that the seven RAI classes effectively differentiate the rock slope based on unique mass-wasting characteristics. Furthermore, the research explores spatial and temporal variations in these failure characteristics. Based on the study's outcomes, recommendations are provided for modifying the RAI parameters for each RAI class, specifically the annual failure rate and average failure depth. These modifications aim to enhance the accuracy and effectiveness of rockfall hazard assessments. [ABSTRACT FROM AUTHOR]

Remote sensing change detection (CD) identifies changes in each pixel of certain classes of interest from a set of aligned image pairs. It is challenging to accurately identify natural changes in feature categories due to unstructured and temporal changes. This research proposed an effective bi-temporal remote sensing CD comprising an encoder that could extract multiscale features, a decoder that focused on semantic alignment between temporal features, and a classification head. In the decoder, we constructed a new convolutional attention structure based on pre-generation of depthwise-separable change-salient maps (PDACN) that could reduce the attention of the network on unchanged regions and thus reduce the potential pseudo-variation in the data sources caused by semantic differences in illumination and subtle alignment differences. To demonstrate the effectiveness of the PDA attention structure, we designed a lightweight network structure for encoders under both convolution-based and transformer architectures. The experiments were conducted on a single-building CD dataset (LEVIR-CD) and a more complex multivariate change type dataset (SYSU-CD). The results showed that our PDA attention structure generated more discriminative change variance information while the entire network model obtained the best performance results with the same level of network model parameters in the transformer architecture. For LEVIR-CD, we achieved an intersection over union (IoU) of 0.8492 and an F1 score of 0.9185. For SYSU-CD, we obtained an IoU of 0.7028 and an F1 score of 0.8255. The experimental results showed that the method proposed in this paper was superior to some current state-of-the-art CD methods. [ABSTRACT FROM AUTHOR]

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

Semantic change detection (SCD) is a challenging task in remote sensing, which aims to locate and identify changes between the bi-temporal images, providing detailed "from-to" change information. This information is valuable for various remote sensing applications. Recent studies have shown that multi-task networks, with dual segmentation branches and single change branch, are effective in SCD tasks. However, these networks primarily focus on extracting contextual information and ignore spatial details, resulting in the missed or false detection of small targets and inaccurate boundaries. To address the limitations of the aforementioned methods, this paper proposed a spatial-temporal semantic perception network (STSP-Net) for SCD. It effectively utilizes spatial detail information through the detail-aware path (DAP) and generates spatial-temporal semantic-perception features through combining deep contextual features. Meanwhile, the network enhances the representation of semantic features in spatial and temporal dimensions by leveraging a spatial attention fusion module (SAFM) and a temporal refinement detection module (TRDM). This augmentation results in improved sensitivity to details and adaptive performance balancing between semantic segmentation (SS) and change detection (CD). In addition, by incorporating the invariant consistency loss function (ICLoss), the proposed method constrains the consistency of land cover (LC) categories in invariant regions, thereby improving the accuracy and robustness of SCD. The comparative experimental results on three SCD datasets demonstrate the superiority of the proposed method in SCD. It outperforms other methods in various evaluation metrics, achieving a significant improvement. The Sek improvements of 2.84%, 1.63%, and 0.78% have been observed, respectively. [ABSTRACT FROM AUTHOR]

Change detection plays a crucial role in remote sensing by identifying surface modifications between two sets of temporal remote sensing images. Recent advancements in deep learning techniques have yielded significant achievements in this field. However, there are still some challenges: (1) Existing change feature fusion methods often introduce redundant information. (2) The complexity of network structures leads to a large number of parameters and difficulties in model training. To overcome these challenges, this paper proposes a Multi-Scale Feature Subtraction Fusion Network (MFSF-Net). It comprises two primary modules: the Multi-scale Feature Subtraction Fusion (MFSF) module and the Feature Deep Supervision (FDS) module. MFSF enhances change features and reduces redundant pseudo-change features. FDS provides additional supervision on different scales of change features in the decoder, improving the training efficiency performance of the network. Additionally, to address the problem of imbalanced samples, the Dice loss strategy is introduced as a means to mitigate this issue. Through comprehensive experiments, MFSF-Net achieves an F1 score of 91.15% and 95.64% on LEVIR-CD and CDD benchmark datasets, respectively, outperforming six state-of-the-art algorithms. Moreover, it attains an improved balance between model complexity and performance, showcasing the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Woody vegetation landscape features, such as hedges, tree patches, and riparian vegetation, are important elements of landscape and biotic diversity. For the reason that biodiversity loss is one of the major ecological problems in the EU, it is necessary to establish efficient workflows for the registration and monitoring of woody vegetation landscape features. In the paper, we propose and evaluate a methodology for automated detection of changes in woody vegetation landscape features from a digital orthophoto (DOP). We demonstrate its ability to capture most of the actual changes in the field and thereby provide valuable support for more efficient maintenance of landscape feature layers, which is important for the shaping of future environmental policies. While the most reliable source for vegetation cover mapping is a combination of LiDAR and high-resolution imagery, it can be prohibitively expensive for continuous updates. The DOP from cyclic aerial photography presents an alternative source of up-to-date information for tracking woody vegetation landscape features in-between LiDAR recordings. The proposed methodology uses a segmentation neural network, which is trained with the latest DOP against the last known ground truth as the target. The output is a layer of detected changes, which are validated by the user before being used to update the woody vegetation landscape feature layer. The methodology was tested using the data of a typical traditional Central European cultural landscape, Goričko, in north-eastern Slovenia. The achieved F 1 of per-pixel segmentation was 83.5% and 77.1% for two- and five-year differences between the LiDAR-based reference and the DOP, respectively. The validation of the proposed changes at a minimum area threshold of 100 m2 and a minimum area percentage threshold of 20% showed that the model achieved recall close to 90%. [ABSTRACT FROM AUTHOR]

Damage identification soon after a large-magnitude earthquake is a major problem for early disaster response activities. The faster the damaged areas are identified, the higher the survival chances of inhabitants. Current methods for damage identification are based on the application of artificial intelligence techniques using remote sensing data. Such methods require a large amount of high-quality labeled data for calibration and/or fine-tuning processes, which are expensive in the aftermath of large-scale disasters. In this paper, we propose a novel semi-supervised classification approach for identifying urban changes induced by an earthquake between images recorded at different times. We integrate information from a small set of labeled data with information from ground motion and fragility functions computed on large unlabeled data. A relevant consideration is that ground motion and fragility functions can be computed in real time. The urban changes induced by the 2023 Turkey earthquake sequence are reported as an evaluation of the proposed method. The method was applied to the interferometric coherence computed from C-band synthetic aperture radar images from Sentinel-1. We use only 39 samples labeled as changed and 9000 unlabeled samples. The results show that our method is able to identify changes between images associated with the effects of an earthquake with an accuracy of about 81%. We conclude that the proposed method can rapidly identify affected areas in the aftermath of a large-magnitude earthquake. [ABSTRACT FROM AUTHOR]

The mainstream methods for change detection in synthetic-aperture radar (SAR) images use difference images to define the initial change regions. However, methods can suffer from semantic collapse, which makes it difficult to determine semantic information about the changes. In this paper, we proposed a hierarchical fusion SAR image change-detection model based on hierarchical fusion conditional random field (HF-CRF). This model introduces multimodal difference images and constructs the fusion energy potential function using dynamic convolutional neural networks and sliding window entropy information. By using an iterative convergence process, the proposed method was able to accurately detect the change-detection regions. We designed a dynamic region convolutional semantic segmentation network with a two-branch structure (D-DRUNet) to accomplish feature fusion and the segmentation of multimodal difference images. The proposed network adopts a dual encoder–single decoder structure where the baseline is the UNet network that utilizes dynamic convolution kernels. D-DRUNet extracts multimodal difference features and completes semantic-level fusion. The Sobel operator is introduced to strengthen the multimodal difference-image boundary information and construct the dynamic fusion pairwise potential function, based on local boundary entropy. Finally, the final change result is stabilized by iterative convergence of the CRF energy potential function. Experimental results demonstrate that the proposed method outperforms existing methods in terms of the overall number of detection errors, and reduces the occurrence of false positives. [ABSTRACT FROM AUTHOR]

Change detection based on remote sensing images plays an important role in the field of remote sensing analysis, and it has been widely used in many areas, such as resources monitoring, urban planning, disaster assessment, etc. In recent years, it has aroused widespread interest due to the explosive development of artificial intelligence (AI) technology, and change detection algorithms based on deep learning frameworks have made it possible to detect more delicate changes (such as the alteration of small buildings) with the help of huge amounts of remote sensing data, especially high-resolution (HR) data. Although there are many methods, we still lack a deep review of the recent progress concerning the latest deep learning methods in change detection. To this end, the main purpose of this paper is to provide a review of the available deep learning-based change detection algorithms using HR remote sensing images. The paper first describes the change detection framework and classifies the methods from the perspective of the deep network architectures adopted. Then, we review the latest progress in the application of deep learning in various granularity structures for change detection. Further, the paper provides a summary of HR datasets derived from different sensors, along with information related to change detection, for the potential use of researchers. Simultaneously, representative evaluation metrics for this task are investigated. Finally, a conclusion of the challenges for change detection using HR remote sensing images, which must be dealt with in order to improve the model's performance, is presented. In addition, we put forward promising directions for future research in this area. [ABSTRACT FROM AUTHOR]

In recent years, Africa has seen much construction of large-scale hydrological infrastructures in the arid and semi-arid regions of numerous countries. This paper aims to quantify the effects of this form of hydrological infrastructure, especially the Upper Atbara and Setit Dam Complex (UASDC) in Eastern Sudan, on the land use/cover (LUC) and socioeconomic domains. This paper attempts to advance our understanding of this phenomenon by using multiple approaches. A framework using the integration of 3S technologies and a logical approach for quantifying the significance of the results to society has been developed. The method used Landsat5 TM in 2002, Sentinel2A in 2018, and statistical data to create the LUC map. The final map included seven classes; the overall accuracy of changes in LUC patterns was 94.9% in 2002 and 93% in the results reveal that significant changes occurred in terms of LUC, having a considerable effect on socio-economic development. The results were analyzed with the logical approach for overall objectives, where 85% represents S1, 3.3% represents S2, and 11.7% represents S3, respectively. This study provides an insight into further investigations of the dam's effect on climate and groundwater, and offers a new perspective on land use prediction, simulation, and environmental sustainability. [ABSTRACT FROM AUTHOR]

Change detection (CD), as a special remote-sensing (RS) segmentation task, faces challenges, including alignment errors and illumination variation, dense small targets, and large background intraclass variance in very high-resolution (VHR) remote-sensing images. Recent methods have avoided the misjudgment caused by illumination variation and alignment errors by increasing the ability of global modeling, but the latter two problems have still not been fully addressed. In this paper, we propose a new CD model called SFCD, which increases the feature extraction capability for small targets by introducing a shifted-window (Swin) transformer. We designed a foreground-aware fusion module to use attention gates to trim low-level feature responses, enabling increased attention to the changed region compared to the background when recovering the changed region, thus reducing background interference. We evaluated our model on two CD datasets, LEVIR-CD and CDD, and obtained F1 scores of 91.78 and 97.87, respectively. The experimental results and visual interpretation show that our model outperforms several previous CD models. In addition, we adjusted the parameters and structure of the standard model to develop a lightweight version that achieves an accuracy beyond most models with only 1.55 M parameters, further validating the effectiveness of our design. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of detecting and learning anomalies in high-dimensional data-streams in real-time. Following a data-driven approach, we propose an online and multivariate anomaly detection method that is suitable for the timely and accurate detection of anomalies. We propose our method for both semi-supervised and supervised settings. By combining the semi-supervised and supervised algorithms, we present a self-supervised online learning algorithm in which the semi-supervised algorithm trains the supervised algorithm to improve its detection performance over time. The methods are comprehensively analyzed in terms of computational complexity, asymptotic optimality, and false alarm rate. The performances of the proposed algorithms are also evaluated using real-world cybersecurity datasets, that show a significant improvement over the state-of-the-art results. [ABSTRACT FROM AUTHOR]

The loss of archaeological heritage continues today, because of both natural disasters and human-made actions. Alarmingly, a significant amount of the destruction is perpetrated by looters and illegal excavations. This problem is not a new one. However, it has increased exponentially in recent years, especially in countries which witnessed internal turmoil (i.e., the Arab Spring) but also throughout Europe. Local authorities struggle to provide adequate controls because of a lack of human resources, budget constraints or technological know-how. This paper describes a multimodal documentation and monitoring workflow applied to an archaeological site for which, due to the sensitivity of the topic, no specific details can be publicly disclosed. The techniques used include UAV aerial surveys, image-based modelling, change detection, relief visualization and GIS mapping. Thanks to the analysis of the multitemporal datasets, it was possible to assess the extension and spatial progression of illegal excavation over a two-year period. [ABSTRACT FROM AUTHOR]

In an era of rapid technological improvements, state-of-the-art methodologies and tools dedicated to protecting and promoting our cultural heritage should be developed and extensively employed in the contemporary built environment and lifestyle. At the same time, sustainability principles underline the importance of the continuous use of historic or vernacular buildings as part of the building stock of our society. Adopting a holistic, integrated, multi-disciplinary strategy can link technological innovation with the conservation and restoration of heritage buildings. This paper presents the ongoing research and results of the application of Machine Learning methods for the remote monitoring of the built environment of the historic cluster in Cypriot cities. This study is part of an integrated, multi-scale, and multi-disciplinary study of heritage buildings, with the end goal of creating an online HBIM platform for urban monitoring. [ABSTRACT FROM AUTHOR]

In the field of remote sens., change detection is an important monitoring technology. However, effectively extracting the change feature is still a challenge, especially with an unsupervised method. To solve this problem, we proposed an unsupervised transformer boundary autoencoder network (UTBANet) in this paper. UTBANet consists of a transformer structure and spectral attention in the encoder part. In addition to reconstructing hyperspectral images, UTBANet also adds a decoder branch for reconstructing edge information. The designed encoder module is used to extract features. First, the transformer structure is used for extracting the global features. Then, spectral attention can find important feature maps and reduce feature redundancy. Furthermore, UTBANet reconstructs the hyperspectral image and boundary information simultaneously through two decoders, which can improve the ability of the encoder to extract edge features. Our experiments demonstrate that the proposed structure significantly improves the performance of change detection. Moreover, comparative experiments show that our method is superior to most existing unsupervised methods. [ABSTRACT FROM AUTHOR]

In traditional image processing, the Fourier transform is often used to transform an image from the spatial domain to the frequency domain, and frequency filters are designed from the perspective of the frequency domain to sharpen or blur the image. In the field of remote sensing change detection, deep learning is beginning to become a mainstream tool. However, deep learning can still refer to traditional methodological ideas. In this paper, we designed a new convolutional neural network (MFGFNet) in which multiple global filters (GFs) are used to capture more information in the frequency domain, thus sharpening the image boundaries and better preserving the edge information of the change region. In addition, in MFGFNet, we use CNNs to extract multi-scale images to enhance the effects and to better focus on information about changes in different sizes (multi-scale combination module). The multiple pairs of enhancements are fused by the difference method and then convolved and concatenated several times to obtain a better difference fusion effect (feature fusion module). In our experiments, the IOUs of our network for the LEVIR-CD, SYSU, and CDD datasets are 0.8322, 0.6780, and 0.9101, respectively, outperforming the state-of-the-art model and providing a new perspective on change detection. [ABSTRACT FROM AUTHOR]

Change detection (CD) is an important research topic in remote sensing, which has been applied in many fields. In the paper, we focus on the post-processing of difference images (DIs), i.e., how to further improve the quality of a DI after the initial DI is obtained. The importance of DIs for CD problems cannot be overstated, however few methods have been investigated so far for re-processing DIs after their acquisition. In order to improve the DI quality, we propose a global and local graph-based DI-enhancement method (GLGDE) specifically for CD problems; this is a plug-and-play method that can be applied to both homogeneous and heterogeneous CD. GLGDE first segments the multi-temporal images and DIs into superpixels with the same boundaries and then constructs two graphs for the DI with superpixels as vertices: one is the global feature graph that characterizes the association between the similarity relationships of connected vertices in the multi-temporal images and their changing states in a DI, the other is the local spatial graph that exploits the change information and contextual information of the DI. Based on these two graphs, a DI-enhancement model is built, which constrains the enhanced DI to be smooth on both graphs. Therefore, the proposed GLGDE can not only smooth the DI but also correct the it. By solving the minimization model, we can obtain an improved DI. The experimental results and comparisons on different CD tasks with six real datasets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the advent of the digital computer, time series analysis has gained wide attention and is being applied to many fields of science. This paper reviews many traditional and recent techniques for time series analysis and change detection, including spectral and wavelet analyses with their advantages and weaknesses. First, Fourier and least-squares-based spectral analysis methods and spectral leakage attenuation methods are reviewed. Second, several time-frequency decomposition methods are described in detail. Third, several change or breakpoints detection methods are briefly reviewed. Finally, some of the applications of the methods in various fields, such as geodesy, geophysics, remote sensing, astronomy, hydrology, finance, and medicine, are listed in a table. The main focus of this paper is reviewing the most recent methods for analyzing non-stationary time series that may not be sampled at equally spaced time intervals without the need for any interpolation prior to the analysis. Understanding the methods presented herein is worthwhile to further develop and apply them for unraveling our universe. [ABSTRACT FROM AUTHOR]

Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small satellite missions usually focus on specific monitoring and observation tasks. Considering that multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, bandwidth constraint is an important challenge in data transmission. That issue is addressed mainly by source and channel coding techniques aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis on the satellite to decide which information is effectively useful before coding and transmitting. The images are tiled and classified using a set of detection algorithms after defining the least relevant content for general remote sensing applications. The methodology makes use of the red-band, green-band, blue-band, and near-infrared-band measurements to perform the classification of the content by managing a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. Experiments for a set of typical scenarios of summer and winter days in Stockholm, Sweden, were conducted, and the results show that non-important content can be identified and discarded without compromising the predefined useful information for water and dry-land regions. For the evaluated images, only 22.3% of the information would need to be transmitted to the ground station to ensure the acquisition of all the important content, which illustrates the merits of the proposed method. Furthermore, the embedded platform's constraints regarding processing time were analyzed by running the detection algorithms on Unibap's iX10-100 space cloud platform. [ABSTRACT FROM AUTHOR]