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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]

The task of change detection (CD) in optical and SAR images is an ever-evolving and demanding subject within the realm of remote sensing (RS). It holds great significance to identify the target areas by using complementary information between the two. Due to the distinct imaging mechanisms employed by optical and SAR sensors, effectively and accurately identifying changing regions can be challenging. To this end, a novel heterogeneous RS images CD network (Twin-Depthwise Separable Convolution Connect Network, TDSCCNet) is proposed in this paper. Image domain transformation is a front-end task, while the back-end employs a single-branch bilayer depthwise separable convolutionconnected encoder-decoder to accomplish CD work. Specifically, first, the cycle-consistent adversarial network (CycleGAN) serves to integrate the optical and SAR visual domains, and a consistent feature expression is obtained. Second, the single-branch encoder structure of bilayer depthwise separable convolution is employed to realize change feature extraction. Finally, the multiscale connected decoder reconstructed by change map is utilized to reconstruct the original images and solve the local discontinuities and holes in the binary change map. Multiscale loss is designated for optimizing the global and local effects to alleviate the class imbalance problem. It was tested on four representative datasets from Gloucester, Shuguang Village, Italy and WV-3 datasets with an overall accuracy of 97.36%, 97.01%, 97.62%, and 98.01% respectively. By comparing the existing methods, experimental results confirmed the effectiveness of the proposed method. [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]

The field of (practical) data-driven approaches that utilize the vibration signature of target systems for developing mathematical models (for computational purposes, control, or anomaly detection for structural health monitoring) is still an active research area, despite the fact that several powerful system identification techniques have been developed in the system dynamics field to analyze such measurements. However, there is still a paucity of comprehensive experimental studies that investigate the range of validity of such identification techniques, particularly those applicable to realistic situations encountered in the structural engineering field, with the focus on detecting, quantifying, locating, and classifying observed changes, especially when there are significant inherent nonlinearities in the reference (undamaged) complex target structure, and where there are unavoidable sources of errors and uncertainties in the measurements and the attendant data analysis procedures. The research team constructed a well-instrumented, reconfigurable test apparatus (resembling a tall building) that allows the introduction of quantifiable levels of composite changes at various locations, orientations, and types of linear and/or nonlinear changes, with the aim of investigating a subset of the aforementioned challenges facing researchers who are interested in assessing the utility of some practical system identification approaches. The primary focus of the identification approach is on a decomposition procedure that is ideally suited for certain types of structures that possess some topological features that can be exploited to enhance the detectability of small changes. A companion paper provides a detailed description of the testbed features and its instrumentation. The present paper focuses on the analysis of some of the very extensive data sets that were created to study the usefulness of some practical dimensionless probabilistic measures that not only provide normalized change indices but also simultaneously attach a confidence level to each of these indices. [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]

Growing intracranial aneurysms pose a high risk of rupture, making the detection and quantification of the growth crucial for timely treatment strategy adoption. In this paper we propose a computer-assisted approach based on the extraction of IA shapes from associated baseline and follow-up angiographic scans and non-rigid morphing of the two shapes. From the obtained shape deformations we computed four novel features, including differential volume (dV), surface area (dSA), aneurysm-size normalized median deformation path length (dMPL), and integral of cumulative deformation distances (dICDD). An experienced neuroradiologist manually extracted the IA shape models from the baseline and follow-up MRAs and, by utilizing size change and visual assessments, classified each aneurysm into stable with morphology changes, stable or growing. We investigated the classification performance and found that three of the novel and one cross-sectional feature exhibited significantly different mean values (p-value < 0.05 ; Tukey's HSD test) between the stable and growing IA groups, while the mean dICDD was significantly different between all the three groups. The cross-sectional features has sensitivity to growing IAs in range 0.05–0.86, while novel features had generally higher sensitivity in range 0.81–0.90, making them promising candidates as surrogate follow-up imaging-based biomarkers for IA growth detection. These findings may offer valuable information for clinical management of patients with IAs based on follow-up imaging. [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]

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]

The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN). However, considering the locality of convolutional operation, CNN unable to master the interplay between global and distant semantic information. Some researches has employ Vision Transformer as a backbone in remote sensing field. Inspired by these researches, in this paper, we propose a network named Siam-Swin-Unet, which is a Siamesed pure Transformer with U-shape construction for remote sensing image change detection. Swin Transformer is a hierarchical vision transformer with shifted windows that can extract global feature. To learn local and global semantic feature information, the dual-time image are fed into Siam-Swin-Unet which is composed of Swin Transformer, Unet Siamesenet and two feature fusion module. Considered the Unet and Siamesenet are effective for change detection, We applied it to the model. The feature fusion module is designed for fusion of dual-time image features, and is efficient and low-compute confirmed by our experiments. Our network achieved 94.67 F1 on the CDD dataset (season varying). [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]

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]

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) 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 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]

Despite the ongoing advancements in deep learning, challenges persist in the domain of change detection in remote sensing imagery. Objects with intricate structures and features may exhibit different shapes or appearances at different times or spatial locations. While most models aim to improve the performance of change detection tasks, these enhancements may lead to significantly increased computational efficiency. In this paper, we propose a global context enhancement network. Firstly, we use ResNet18 to extract dual-temporal features, which are then represented as concise semantic labels by an image semantic extractor. Subsequently, we process these semantic labels through a contextual transformer encoder to generate more refined remote sensing semantic labels enriched with abundant contextual information. The refined semantic labels are integrated with the original features and processed through a Transformer decoder to generate enhanced dual-temporal feature maps. Finally, through the processing of the classification head, we obtain pixel-level predictive images. Extensive experiments conducted on two public change detection datasets yielded impressive results, achieving an F1 score of 89.95% on the WHU-CD dataset and 95.16% on the SVCD dataset. When compared to state-of-the-art change detection models, our approach not only achieves significant performance gains but also maintains relatively high computational efficiency. Our method excels in capturing relevant features and relationships within input data, thereby enhancing the model's ability to represent relationships between different features. This results in a significant performance improvement without adding to the computational complexity. [ABSTRACT FROM AUTHOR]

The importance of trees in every environment is crucial given the fact that they make some provisional and supportive services such as food inform of fruits, shelter, shade, aesthetics, as well as carbon sequestration among others. Hence the objective of this paper is to use Normalized Difference Vegetation Index (NDVI) techniques to determine quantitatively the changes in forest and trees outside forest from 1990-2021 in Adamawa central. The result revealed that changes occurred in trees inside forest, between 2013 and 2021; Rocks and baresurface increased by17.49%. Shrubs and grassland increased by 34.13% while healthy forest reduced to - 48.38% within the same period. However, result of TOF shows similar trend between 2013 and 2021 in the study area. Rocks and bare surface increases to 58.15%, shrubs and grassland reduced to -37.46%, while dense forest decreased to -4.39%. The results revealed further a decline in the concentration of both trees inside forest and trees outside forest in the area. The study recommended a change of policy to mitigate the negative impact of degradation through various forest rehabilitation mechanisms such as afforestation and reforestation programs in the study area. [ABSTRACT FROM AUTHOR]

Effectively utilizing the complementary characteristics between optical and SAR remote sensing images to accurately identify change information is of great practical significance. Direct pixel comparisons are challenging since they come from sensors with different imaging mechanisms. Therefore, in this paper, a novel domain transformation that incorporates channel interaction and a bridging-enhanced (CIBENet) heterogeneous change detection network is proposed, where twin-depthwise separable fused gated channel transformation is the convolutional block designed for channel information interaction. And samples without changed semantic information are used to train the deep transformation model, which effectively solves the interference of semantic information on NiceGAN. A different perspective is provided for the heterogeneous change detection task. The backend change detection network takes UNet++ with twin-depthwise separable convolution as the baseline, introduces the gated channel transformation and bridging-enhanced decoder, and models the feature relationship between channels to strengthen the channel information interaction while suppressing the expression of nonchanged information. In addition, the bridging-enhanced decoder can efficiently solve localized holes and discontinuities in binary maps by bridging identical pixels. CIBENet is supervised and experimented on three heterogeneous change detection datasets, Gloucester, Shuguang, and Italy, also compared with the classical unsupervised methods CGAN, SCCN, INLPG and advanced supervised methods DTCDN, DACDT. The network model proposed in this paper significantly improved F1 and recall, and the overall accuracies were 96.60 %, 98.23 %, and 96.29 %, respectively. The experiments validated the reliability of the proposed model.

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]

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]

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]

Multimodal change detection (MCD) combines multiple remote sensing data sources to realize surface change monitoring, which is essential for disaster evaluation and environmental monitoring. However, due to the 'incomparable' features in multimodal data, traditional change detection methods for unimodal (homogenous) data no longer apply. To address this issue, this paper proposes a novel MCD method with global structure graph mapping (GSGM) which extracts the 'comparable' structural features between multimodal datasets and constructs a global structure graph (GSG) to express the structure information for each of the multi-temporal images, which are then cross-mapped to the other data domain. The change intensity (CI) is determined by measuring the change of GSGs after mapping and the differences between GSGs and mapped GSGs. The forward and backward CI maps (CIMs) are then fused with the latent low-rank representation method (LLRR), and the change map (CM) is obtained by threshold segmentation. Experiments on five multimodal and four unimodal datasets demonstrate the effectiveness and robustness of the proposed method (source code is made available at https://github.com/ rshante0426/GSGM). [ABSTRACT FROM AUTHOR]

In the context of Earth observation, change detection boils down to comparing images acquired at different times by sensors of possibly different spatial and/or spectral resolutions or different modalities (e.g., optical or radar). Even when considering only optical images, this task has proven to be challenging as soon as the sensors differ by their spatial and/or spectral resolutions. This paper proposes a novel unsupervised change detection method dedicated to images acquired by such so-called heterogeneous optical sensors. It capitalizes on recent advances which formulate the change detection task into a robust fusion framework. Adopting this formulation, the work reported in this paper shows that any off-the-shelf network trained beforehand to fuse optical images of different spatial and/or spectral resolutions can be easily complemented with a network of the same architecture and embedded into an adversarial framework to perform change detection. A comparison with state-of-the-art change detection methods demonstrates the versatility and the effectiveness of the proposed approach. • Detecting changes between images acquired by heterogeneous sensors is formulated as a robust fusion problem. • The method is able to handle multi-band images with possibly different spatial and/or spectral resolutions. • The method is unsupervised, i.e., it does not need a train set composed of image pairs with associated binary change maps. • Any pretrained deep network designed to fuse heterogeneous images can be reused to perform change detection. • When compared to conventional approaches, the strategy appears to be more flexible while competing favorably. [ABSTRACT FROM AUTHOR]

To enhance the accuracy of agricultural area classification and enable remote sensing monitoring of agricultural regions, this paper investigates classification models and their application in change detection within rural areas, proposing the MC&N-PSPNet (CBAM into MobileNetV2 and NAM into PSPNet) network model. Initially, the HRSCD (High Resolution Semantic Change Detection) dataset labels undergo binary redrawing. Subsequently, to efficiently extract image features, the original PSPNet (Pyramid Scene Parsing Network) backbone network, ResNet50 (Residual Network-50), is substituted with the MobileNetV2 (Inverted Residuals and Linear Bottlenecks) model. Furthermore, to enhance the model’s training efficiency and classification accuracy, the NAM (Normalization-Based Attention Module) attention mechanism is introduced into the improved PSPNet model to obtain the categories of land cover changes in remote sensing images before and after the designated periods. Finally, the final change detection results are obtained by performing a different operation on the classification results for different periods. Through experimental analysis, this paper demonstrates the proposed method’s superior capability in segmenting agricultural areas, which is crucial for effective agricultural area change detection. The model achieves commendable performance metrics, including overall accuracy, Kappa value, MIoU, and MPA values of 95.03%, 88.15%, 93.55%, and 88.90%, respectively, surpassing other models. Moreover, the model exhibits robust performance in final change detection, achieving an overall accuracy and Kappa value of 93.24% and 92.29%, respectively. The results of this study show that the MC&N-PSPNet model has significant advantages in the detection of changes in agricultural zones, which provides a scientific basis and technical support for agricultural resource management and policy formulation.

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.

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.

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.

Ecocultural restoration involves the reciprocal repair of ecosystems and revitalization of cultural practices to enhance their mutual resilience to natural and anthropogenic disturbances and climate change stressors. Resilient ecocultural systems are adapted to retain structure and function in the face of disturbances that remain within historical ranges of severity. To assist in ecocultural restoration and management, understanding how a system has historically responded to different types of disturbances is therefore invaluable in understanding how social-ecological resilience can be maintained in the face of future stressors and disturbances. However, records of disturbances and ecocultural responses can be limited for certain landscapes and human communities. In this methods paper, we demonstrate a mixed-method process for integrating oral history, field-based knowledge, archival information, and historical and contemporary aerial images to gain insight into the changes on the Klamath River in Northern California from the 1940s through 2020. We georegistered historical imagery, quantified changes between land cover classes, and contextualized these classifications with qualitative assessments of changes in larger surrounding areas. By synthesizing these data sources with field measurements, mining and other land survey maps, timber management plans, fire and flood histories, and interviews with members of the Karuk Tribe, we were able to reconstruct the land use and land cover change histories at five sites. We noted that recovery of canopy cover from fire and logging practices was faster than for flood, which was faster than recovery from mining, consistent with the relative severity of likely soil disturbance. By combining different sources of information with complementary strengths, we were able to provide managers with site-specific information on recovery from different types of disturbance. Though this approach was labor-intensive, with emerging tools for supervised classification of high-resolution imagery, mixed-method analytical historical ecology could be applied more broadly, supporting ecocultural restoration on a larger scale. [Display omitted] • Reconstructing landscape histories helps understand ecosystem response to disturbance. • Combining qualitative and quantitative analysis helps reconstruct land-use. • Synthesizing many historical data sources improves ecocultural restoration planning. • Manual classification could improve with machine learning 'human-in-the-loop' tools. [ABSTRACT FROM AUTHOR]

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL's UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire "burn" detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as "burned" compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance. [ABSTRACT FROM AUTHOR]

With the development of satellite technology, the importance of multi-temporal remote sensing (RS) image change detection (CD) in urban planning, environmental monitoring, and other fields is increasingly prominent. Deep learning techniques enable a profound exploration of the intrinsic features within hyperspectral (HS) data, leading to substantial enhancements in CD accuracy while addressing several challenges posed by traditional methodologies. However, existing convolutional neural network (CNN)-based CD approaches frequently encounter issues during the feature extraction process, such as the loss of detailed information due to downsampling, which hampers a model's ability to accurately capture complex spectral features. Additionally, these methods often neglect the integration of multi-scale information, resulting in suboptimal local feature extraction and, consequently, diminished model performance. To address these limitations, we propose a multi-scale fusion network (MsFNet) which leverages dynamic spectral features for effective multi-temporal HS-CD. Our approach incorporates a dynamic convolution module with spectral attention, which adaptively modulates the receptive field size according to the spectral characteristics of different bands. This flexibility enhances the model's capacity to focus on critical bands, thereby improving its ability to identify and differentiate changes across spectral dimensions. Furthermore, we develop a multi-scale feature fusion module which extracts and integrates features from deep feature maps, enriching local information and augmenting the model's sensitivity to local variations. Experimental evaluations conducted on three real-world HS-CD datasets demonstrate that the proposed MsFNet significantly outperforms contemporary advanced CD methods in terms of both efficacy and performance. [ABSTRACT FROM AUTHOR]

Background Subtraction (BgS) is a widely researched technique to develop online Change Detection algorithms for static video cameras. Many BgS methods have employed the unsupervised, adaptive approach of Gaussian Mixture Model (GMM) to produce decent backgrounds, but they lack proper consideration of scene semantics to produce better foregrounds. On the other hand, with considerable computational expenses, BgS with Deep Neural Networks (DNN) is able to produce accurate background and foreground segments. In our research, we blend both approaches for the best. First, we formulated a network called Convolutional Density Approximation (CDA) for direct density estimation of background models. Then, we propose a self-supervised training strategy for CDA to adaptively capture high-frequency color distributions for the corresponding backgrounds. Finally, we show that background models can indeed assist foreground extraction by an efficient Neural Motion Subtraction (NeMos) network. Our experiments verify competitive results in the balance between effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Change detection aims to identify the difference between dual-temporal images and has garnered considerable attention over the past decade. Recently, deep learning methods have shown robust feature extraction capabilities and have achieved improved detection results; however, they exhibit limitations in preserving clear boundaries for the identified regions, which is attributed to the inadequate contextual information aggregation capabilities of feature extraction, and fail to adequately constrain the delineation of boundaries. To address this issue, a novel dual-branch feature interaction backbone network integrating the CNN and Transformer architectures to extract pixel-level change information was developed. With our method, contextual feature aggregation can be achieved by using a cross-layer feature fusion module, and a dual-branch upsampling module is employed to incorporate both spatial and channel information, enhancing the precision of the identified change areas. In addition, a boundary constraint is incorporated, leveraging an MLP module to consolidate fragmented edge information, which increases the boundary constraints within the change areas and minimizes boundary blurring effectively. Quantitative and qualitative experiments were conducted on three benchmarks, including LEVIR-CD, WHU Building, and the xBD natural disaster dataset. The comprehensive results show the superiority of the proposed method compared with previous approaches. [ABSTRACT FROM AUTHOR]

The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in the Three Gorges Reservoir area in China. Using the continuous change detection and classification (CCDC) algorithm, sample data from 2023 were successfully migrated to 2018–2022, achieving high migration accuracy ( R 2 = 0.8303, RMSE = 4.64). Based on migrated samples, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) algorithms classified forest tree species with overall accuracies above 70% and Kappa coefficients above 0.6. XGB. They outperformed other algorithms, with classification accuracy of over 80% and Kappa above 0.75 in almost all years. The final map indicates stable distribution from 2018 to 2023, with eucalyptus covering over 40% of the forest area, followed by horsetail pine, fir, cypress, and wetland pine. [ABSTRACT FROM AUTHOR]

Change detection (CD) in high-resolution remote sensing imagery remains challenging due to the complex nature of objects and varying spectral characteristics across different times and locations. Convolutional neural networks (CNNs) have shown promising performance in CD tasks by extracting meaningful semantic features. However, traditional 2D-CNNs may struggle to accurately integrate deep features from multi-temporal images, limiting their ability to improve CD accuracy. This study proposes a Multi-level Feature Cross-Fusion (MFCF) network with 3D-CNNs for remote sensing image change detection. The network aims to effectively extract and fuse deep features from multi-temporal images to identify surface changes. To bridge the semantic gap between high-level and low-level features, a MFCF module is introduced. A channel attention mechanism (CAM) is also integrated to enhance model performance, interpretability, and generalization capabilities. The proposed methodology is validated on the LEVIR construction dataset (LEVIR-CD). The experimental results demonstrate superior performance compared to the current state-of-the-art in evaluation metrics including recall, F1 score, and IOU. The MFCF network, which combines 3D-CNNs and a CAM, effectively utilizes multi-temporal information and deep feature fusion, resulting in precise and reliable change detection in remote sensing imagery. This study significantly contributes to the advancement of change detection methods, facilitating more efficient management and decision making across various domains such as urban planning, natural resource management, and environmental monitoring. [ABSTRACT FROM AUTHOR]

The European Copernicus Land Monitoring Service (CLMS) has been producing datasets on imperviousness every 3 years since 2006. However, for 2018, the input for the production of the imperviousness dataset was switched from mixed inputs to the Sentinel constellation. While this led to an improvement in the spatial detail from 20 m to 10 m, this also resulted in a break in the time series as the 2018 update was not comparable to the previous reference years. In addition, the European CLMS has been producing a new dataset from 2018 onward entitled CLC+ Backbone, which also includes a sealed area thematic class. When comparing both datasets with sampled reference data, it appears that the imperviousness dataset substantially underestimates sealed areas at the European level. However, the CLC+ dataset is only available from 2018 and currently does not include any change layer. To address these issues, a harmonized continental soil sealing combined dataset for Europe was produced for the entire observation period. This new dataset has been validated to be the best current dataset for monitoring soil sealing as a direct input for European policies with an estimated total sealed area of 175,664 km2 over Europe and an increase in sealed areas of 1297 km2 or 0.7% between 2015 and 2018, which is comparable to previous time periods. Finally, recommendations for future updates and the validation of imperviousness degree geospatial products are given. [ABSTRACT FROM AUTHOR]

Land cover change represents one of the most significant global transformations, which has profound impacts on ecosystems, biological diversity, and the ongoing climate crisis. In this study, our objective was to analyse land cover transformation in the Valencian Community over the last four decades. Utilising Landsat 5, 8, and 9 summer images, a Random Forest algorithm renowned for its ability to handle large datasets and complex variables, was employed to produce land cover classifications consisting of five categories: 'Urban Areas', 'Dense Vegetation', 'Sparse Vegetation', 'Water Bodies', and Other'. The results were validated through in situ measurements comparing with pre-existing products and utilising a confusion matrix. Over the study period, the urban area practically doubled, increasing from approximately 482 to 940 square kilometres. This expansion was concentrated mainly in the proximity of the already existing urban zone and occurred primarily between 1985 and 1990. The Dense and Sparse Vegetation classes exhibit substantial fluctuations over the years, displaying a subtle trend towards a decrease in their cumulative value. Water bodies and Other classes do not show substantial changes over the years. The Random Forest algorithm showed a high Overall Accuracy (OA) of 95% and Kappa values of 93%, showing good agreement with field measurements (88% OA), ESA World Cover (80% OA), and the Copernicus Global Land Service Land Cover Map (73% OA), confirming the effectiveness of this methodology in generating land cover classifications. [ABSTRACT FROM AUTHOR]

Credit evaluation has always been an important part of the financial field. The existing credit evaluation methods have difficulty in solving the problems of redundant data features and imbalanced samples. In response to the above issues, an ensemble model combining an advanced feature selection algorithm and an optimized loss function is proposed, which can be applied in the field of credit evaluation and improve the risk management ability of financial institutions. Firstly, the Boruta algorithm is embedded for feature selection, which can effectively reduce the data dimension and noise and improve the model's capacity for generalization by automatically identifying and screening out features that are highly correlated with target variables. Then, the GHM loss function is incorporated into the XGBoost model to tackle the issue of skewed sample distribution, which is common in classification, and further improve the classification and prediction performance of the model. The comparative experiments on four large datasets demonstrate that the proposed method is superior to the existing mainstream methods and can effectively extract features and handle the problem of imbalanced samples. [ABSTRACT FROM AUTHOR]

Abstract Growing intracranial aneurysms pose a high risk of rupture, making the detection and quantification of the growth crucial for timely treatment strategy adoption. In this paper we propose a computer-assisted approach based on the extraction of IA shapes from associated baseline and follow-up angiographic scans and non-rigid morphing of the two shapes. From the obtained shape deformations we computed four novel features, including differential volume (dV), surface area (dSA), aneurysm-size normalized median deformation path length (dMPL), and integral of cumulative deformation distances (dICDD). An experienced neuroradiologist manually extracted the IA shape models from the baseline and follow-up MRAs and, by utilizing size change and visual assessments, classified each aneurysm into stable with morphology changes, stable or growing. We investigated the classification performance and found that three of the novel and one cross-sectional feature exhibited significantly different mean values (p-value $$< 0.05$$ < 0.05 ; Tukey’s HSD test) between the stable and growing IA groups, while the mean dICDD was significantly different between all the three groups. The cross-sectional features has sensitivity to growing IAs in range 0.05–0.86, while novel features had generally higher sensitivity in range 0.81–0.90, making them promising candidates as surrogate follow-up imaging-based biomarkers for IA growth detection. These findings may offer valuable information for clinical management of patients with IAs based on follow-up imaging.

• A change detection method is proposed with heterogeneous remote sensing images. • It takes advantage of Cycle-Consistent Adversarial Networks for data mining. • Copula mixture is constructed to model the dependence structure of the images. • Experiments on real datasets validate the superiority of the proposed method. In this paper, we consider the problem of change detection (CD) with two heterogeneous remote sensing (RS) images. For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction. However, the difference map derived from subtraction is susceptible to image translation errors, in which case the changed area and the unchanged area are less distinguishable. To overcome the above shortcoming, we propose a new unsupervised copula mixture and CycleGAN-based CD method (COMIC), which combines the advantages of copula mixtures on statistical modeling and the advantages of CycleGANs on data mining. In COMIC, the pre-event image is first translated from its original modality to the post-event image modality. After that, by constructing a copula mixture, the joint distribution of the features from the heterogeneous images can be learnt according to quantitive analysis of the dependence structure based on the translated image and the original pre-event image, which are of the same modality and contain totally the same objects. Then, we model the CD problem as a binary hypothesis testing problem and derive its test statistics based on the constructed copula mixture. Finally, the difference map can be obtained from the test statistics and the binary change map (BCM) is generated by K-means clustering. We perform experiments on real RS datasets, which demonstrate the superiority of COMIC over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]