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Urban public safety management relies heavily on video surveillance systems, which provide crucial visual data for resolving a wide range of incidents and controlling unlawful activities. Traditional methods for target detection predominantly employ a two-stage approach, focusing on precision in identifying objects such as pedestrians and vehicles. These objects, typically sparse in large-scale, lower-quality surveillance footage, induce considerable redundant computation during the initial processing stage. This redundancy constrains real-time detection capabilities and escalates processing costs. Furthermore, transmitting raw images and videos laden with superfluous information to centralized back-end systems significantly burdens network communications and fails to capitalize on the computational resources available at diverse surveillance nodes. This study introduces DiffRank, a novel preprocessing method for fixed-angle video imagery in urban surveillance. The method strategically generates candidate regions during preprocessing, thereby reducing redundant object detection and improving the efficiency of the detection algorithm. Drawing upon change detection principles, a background feature learning approach utilizing shallow features has been developed. This approach prioritizes learning the characteristics of fixed-area backgrounds over direct background identification. As a result, alterations in ROI are efficiently discerned using computationally efficient shallow features, markedly accelerating the generation of proposed Regions of Interest (ROIs) and diminishing the computational demands for subsequent object detection and classification. Comparative analysis on various public and private datasets illustrates that DiffRank, while maintaining high accuracy, substantially outperforms existing baselines in terms of speed, particularly with larger image sizes (e.g., an improvement exceeding 300 % at 1920×1080 resolution). Moreover, the method demonstrates enhanced robustness compared to baseline methods, efficiently disregarding static targets like mannequins in display windows. The advancements in candidate area preprocessing enable a balanced approach between detection accuracy and overall detection speed, making the algorithm highly applicable for real-time on-site analysis in edge computing scenarios and cloud-edge collaborative computing environments. [ABSTRACT FROM AUTHOR]

Change detection in high-resolution Earth observation is a fundamental Earth vision task to understand the subtle temporal dynamics of Earth's surface, significantly promoted by generic vision technologies in recent years. Vision Transformer is a powerful component to learning spatiotemporal representation but with enormous computation complexity, especially for high-resolution images. Besides, there is still lacking principles in designing macro architectures integrating these advanced vision components for various change detection tasks. In this paper, we present a deep probabilistic change model (DPCM) to provide a unified, interpretable, modular probabilistic change process modeling to address multiple change detection tasks, including binary change detection, one-to-many semantic change detection, and many-to-many semantic change detection. DPCM describes any complex change process as a probabilistic graphical model to provide theoretical evidence for macro architecture design and generic change detection task modeling. We refer to this probabilistic graphical model as the probabilistic change model (PCM), where DPCM is the PCM parameterized by deep neural networks. For parameterization, the PCM is factorized into many easy-to-solve distributions based on task-specific assumptions, and then we can use deep neural modules to parameterize these distributions to solve the change detection problem uniformly. In this way, DPCM has both theoretical macro architecture from PCM and strong representation capability of deep networks. We also present the sparse change Transformer for better parameterization. Inspired by domain knowledge, i.e., the sparsity of change and the local correlation of change, the sparse change Transformer computes self-attention within change regions to model spatiotemporal correlations, which has a quadratic computational complexity of the change region size but independent of image size, significantly reducing computation overhead for high-resolution image change detection. We refer to this instance of DPCM with sparse change Transformer as ChangeSparse to demonstrate their effectiveness. The experiments confirm ChangeSparse's superiority in speed and accuracy for multiple real-world application scenarios, such as disaster response and urban development monitoring. The code is available at https://github.com/Z-Zheng/pytorch-change-models. More resources can be found in http://rsidea.whu.edu.cn/resource_sharing.htm. [ABSTRACT FROM AUTHOR]

In remote sensing (RS) image change detection (CD) task, many existing CD methods focus more on how to improve accuracy, but they usually have more parameters, higher computational costs, and heavier memory usage. Designing lightweight and performance-sustainable CD model that is more compatible with real-world applications is an urgent problem to be solved. Therefore, we propose a lightweight change detection network, called as robust feature aggregation network (RFANet). To improve representative capability of weaker features extracted from lightweight backbone, a feature reinforcement module (FRM) is proposed. FRM allows current level feature to densely interact and fuse with other level features, thus accomplishing the complementarity of fine-grained details and semantic information. Considering massive objects with rich correlations in RS images, we design semantic split-aggregation module (SSAM) to better capture global semantic information of changed objects. Besides, we present a lightweight decoder containing channel interaction module (CIM), which allows multi-level refined difference features to emphasize changed areas and suppress background and pseudo-changes. Extensive experiments carried out on four challenging RS image CD datasets illustrate that RFANet achieves competitive performance with fewer parameters and lower computational costs. The source code is available at https://github.com/Youzhihui/RFANet. [ABSTRACT FROM AUTHOR]

In remote sensing, change detection based on deep learning shows promising performance. However, collecting multi-temporal paired images for training a change detection model is costly. To solve this problem, unsupervised change detection methodologies have been proposed, but their performance is still low. In this article, we introduce the Unsupervised Change Detection Based on Image Reconstruction Loss (CDRL) and CDRL with Segment Anything (CDRL-SA). Requiring only single-temporal unlabelled images for training, CDRL uses the original and a photometrically transformed image as an unchanged pair, input to a bi-temporal transformer-based network for reconstructing the original image. During inference, changed pairs result in significant reconstruction loss, highlighting change areas. To capture finer details, we change the structure of CDRL to a transformer-based model and introduce the CutSwap method for effective training. Furthermore, this output is fused with the results of a recently proposed Segment Anything (SA) model to improve the final output. We assessed the performance of CDRL and CDRL-SA using the LEVIR change detection dataset and CLCD dataset, and the method achieved competitive results of 88.9 and 91.6 ACC for the respective datasets, demonstrating its effectiveness in unsupervised change detection tasks. [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]

Having in mind applications in statistics and machine learning such as individualized care monitoring, or watermark detection in large language models, we consider the following general setting: When monitoring a sequence of observations, Xt$$ {X}_t $$, there may be additional information, Zt$$ {Z}_t $$, on the environment which should be used to design the monitoring procedure. This additional information can be incorporated by applying threshold functions c(Zt)$$ c\left({Z}_t\right) $$ to the standardized measurements to adapt the detector to the environment. For the case of categorical data encoding of discrete‐valued environmental information we study several classes of level α$$ \alpha $$ threshold functions including a proportional one which favors rare events among imbalanced classes. For the latter rule asymptotic theory is developed for independent and identically distributed and dependent learning samples including data from new discrete autoregressive moving average model (NDARMA) series and Hidden Markov Models. Further, we propose two‐stage designs which allow to distribute in a controlled way the α$$ \alpha $$ budget over an a priori partition of the sample space of Zt$$ {Z}_t $$. The approach is illustrated by a real medical data set. [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]

Contemporary post-industrial urban areas face opposing transformation trends: on one hand, abandonment or underutilization, and its replacement by new constructions and uses, on the other hand, the revaluation of the historical fabric and the implementation of initiatives to rehabilitate this legacy as industrial heritage. This study aimed to understand the factors that influence trends, and simulate land-use scenarios. A methodology based on three phases is proposed: digitization, exploratory spatial data analysis and simulation. Using the former textile district of Bellavista in Tomé (Chile), this study created and used historical land-use maps from 1970, 1992 and 2019. Meanwhile the main change observed from 1970 to 1992 was a 59.4% reduction in Historical Informal Open Spaces. The major change from 1992 to 2019 was the Historical Informal Open Space loss trend continuing; 65% of the land dedicated to this use changed to new usages. Consequently, the influence of two morphological factors and three urban management instruments on land-use changes between 1992 and 2019 was studied. The projection to 2030 showed a continued trend of expansion of new housing uses over historic urban green spaces and industrial areas on the waterfront, although restrained by the preservation of the central areas of historic housing and the textile factory. [ABSTRACT FROM AUTHOR]

Change detection using SAR data has been an active topic in various applications. Because conventional change detection identifies signal changes in single-pol radar observations, they cannot separately detect different kinds of change associated with different ground parameters. In this study, we investigated the comprehensive use of dual-pol parameters and proposed a novel dual-pol-based change detection framework utilizing different dual-pol scatter-type indicators. To optimize the exploitation of dual-pol change information, we presented a two-step processing strategy that divides the multiclass change detection process into a binary detection step that identifies the presence of changes and the classification step that distinguishes the types of change. In the detection stage, each dual-pol parameter was considered as an independent information source. Assuming potential conflict between dual-pol parameters, a disjunctive combination of detection results from different dual-pol parameters was applied to obtain the final detection result. In the classification step, an unsupervised change classification strategy was proposed based on the change direction and magnitude of the dual-pol parameters within the change class. Experimental results exhibited significantly improved detectability across a wide change spectrum compared with previous dual-pol-based change detection approaches. They also demonstrated the possibility of distinguishing different semantic changes without in situ ground data. [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]

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 Tigris and Euphrates Basin (TEB) encompasses a wide area, and due to its different geographical and political conditions, each environmental factor in different conditions has different effects on the process of surface water changes. Accordingly, in this research, we aim to evaluate the trend of surface water changes in this basin in 2001-2021 by using the time series of 16 different parameters and the products available on the Google Earth Engine (GEE) platform. Based on the findings, the general trend of surface water changes is increasing, and the water area has reached 8605.9 km2 in 2001 to 10021.8 km2 in 2021. Nevertheless, the spatial-temporal changes of water have been different because the extent of lakes and wetlands in the southern areas of the basin has decreased drastically. On the contrary, it has increased upstream of the basin due to the expansion of various dams and channels. In addition, our findings indicated a high correlation between climatic variables, especially evapotranspiration, and temperature, with temporal changes in water in the region. Thus, the impact of global climate changes on the hydrology and environment of the basin highlights the importance and high sensitivity of the major lakes in the region, such as Razazeh, Tharthar, Hamrin, and Habbaniyah, to climate changes. The present research results may be used to assess surface water in other regions and provide valuable information for the planning and management of global surface water resources. [ABSTRACT FROM AUTHOR]

Image change detection is an active research topic in the field of remote sensing, as it allows monitoring environmental changes that occur on temporal and spatial scales. However, most of the existing change detection methods suffer from a lack of adaptability to different image types and lack of large-scale validation. In this study, we propose an automatic change detection method, called "CD-ResUNet," based on multi-spectral NDVI imagery. It is an end-to-end deep learning method based on the fusion of two complementary deep learning networks: UNet and residual networks (ResNet). Extensive experiments have been conducted on low-resolution as well as high-resolution datasets using four represented geographical areas, which are Colombia, California, Brazil, and Duluth, each containing 145,161 patches, and the Change Detection Dataset containing 16,000 patches. For all the investigated regions, the proposed method outperforms many relevant state-of-the-art methods with an accuracy up to 99.5% and an F1-score of 99.40%. [ABSTRACT FROM AUTHOR]

The large-scale removal of trees from forests to make way for human activities is known as deforestation, given that it may result in soil erosion, natural habitat deterioration, biodiversity loss, and water cycle disturbance, this is a major environmental problem. As a source of food, clean water, oxygen, and medicines as well as an essential component of the hydrological cycle—they supply water to the atmosphere through transpiration—forests are a contributing factor to climate change and global warming. In addition to decreasing soil fertility and rainfall, deforestation increases the likelihood of floods and droughts and has a major effect on global warming. Deforestation monitoring is an important input for forest management that helps to prepare an action plan, but monitoring is still a challenging task. Hence, there is a need for an accurate deforestation mechanism to monitor those areas that have been converted from forest to non-forest areas. Therefore, in this paper a deep learning-based forest monitoring approach has been proposed, which is implemented in two steps: (i) a machine learning-based classification technique has been applied to the Sentinel-2 images to classify the forest and non-forest areas, and (ii) a deep learning-based change detection technique is proposed to detect the changes occurred during 2017–2022 of the Kukrail forest range situated in India. The performance of the proposed algorithm is assessed by estimating the error measuring parameters like Precision, Recall, and F1 Score, and it is observed that the proposed approach is quite suitable for forest area change monitoring. [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]

In this paper, a novel change detection algorithm is proposed for the remote sensing Synthetic Aperture Radar (SAR) images. Change detection in SAR images is one of the critical tasks in the field of remote sensing as the images contain significant speckle noise and illumination variations. In order to address these issues, a novel change detection algorithm is proposed using Discrete Wavelet Transform (DTW) and Differential Evaluation (DE). At first, the DWT is utilized to remove the speckle noise from the images. Then, a DE optimization-based Support Vector Machine (SVM) algorithm is proposed to detect the changes in SAR images. The proposed method can give comparatively better correct classification values, and lesser false alarm as well as total error values than the recently developed change detection methods. [ABSTRACT FROM AUTHOR]

To select videos to compose a change detection dataset, we can consider the videos' difficulty level. We need to use difficulty maps, which store values representing the pixels' difficulty level, to estimate these levels. The problem is that ground truth is needed to generate a difficulty map, and generating the ground truth requires manual attribution of labels to the pixels of the frames. Identifying the difficulty level of a video before producing its ground truth allows researchers to obtain the difficulty level, select the videos considering this information, and, subsequently, generate ground truths only for the videos with different difficulty levels. Datasets containing videos with different difficulty levels can evaluate an algorithm more adequately. In this research, we developed a method to generate difficulty maps of a video without using its ground truth. Our method uses the videos and the ground truths from the CDNet 2014 dataset to generate difficulty maps to train a pix2pix neural network. The results showed that the trained network could generate difficulty maps similar to those generated by the traditional approach. [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]

Detecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes. [ABSTRACT FROM AUTHOR]

Current practices in traffic sign monitoring heavily rely on manual inspections, a method that is both time-consuming and prone to human error. This leads to inefficiencies in the management and maintenance of these critical roadside assets. The objective of this work is to overcome these limitations by proposing a method for automated change detection in traffic signs using low-density LiDAR data. The proposed solution integrates noise elimination, point cloud restructuring, and cross-scan KD-tree generation, followed by the application of unsupervised machine learning techniques for change identification. The effectiveness of this method was verified by testing across three different highways with varying point cloud resolutions. For robust testing, an algorithm was also designed to simulate a broad range of different damage scenarios in traffic signs of different types, sizes, and placements. Testing in different scenarios along almost 15 km of the road revealed impressive results with accuracy and F1 score metrics ranging from 92% to 100%. Moreover, the algorithm was also extremely efficient with an average runtime of just 115" per km of fully automated unattended processing. The change detection potential of the proposed algorithm extends beyond traffic signs, as it could be adapted for many highway elements, enhancing the efficiency of transportation asset management and highway maintenance programmes. The findings indicate that this approach not only fills a significant gap in the current traffic sign monitoring and asset management practice but also offers a promising, comprehensive solution towards automated, cost-effective, and precise monitoring and maintenance of traffic signs, thus addressing a major challenge in this area. [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]

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]

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]

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]

Great Lakes Basin landscapes are undergoing rapid land cover and land use (LCLU) change. The goal for this study was to identify changes in land cover occurring in the Great Lakes Basin over three time periods to provide insights into historical land cover changes occurring on a bi-national watershed scale. To quantify potential impacts of anthropogenic changes on important yet vulnerable Great Lakes Wetland ecosystems, the historical changes in land cover over time are assessed via remote sensing. The goal is to better understand legacy effects on current conditions, including wetland gain and loss and the impacts of upland ecosystems on wetland health and water quality. Three key time periods with respect to Great Lakes water level changes and coastal wetland plant invasions were mapped using Landsat-derived land cover maps: 1985, 1995, and 2010. To address change between the three time periods of interest, we incorporate both radiometric and categorical change analysis and open-source tools available for assessing time series data including LandTrendr and TimeSync. Results include maps of annual land cover transition from 1985 to 1995 and 1995 to 2010 basin-wide and by ecoregion and an assessment of the magnitude and direction of change by land cover type. Basin-wide validated change results show approximately 776,854 ha of land changed from c.1980–1995 and approximately 998,400 ha of land changed from c.1995–2010. Both time periods displayed large net decreases in both deciduous forest and agricultural land and net increases in suburban cover. Change by ecoregion is reviewed in this study with many of the change types in central plains showing change in and out of agriculture and suburban land covers, the mixed wood plain ecoregion consisted of a mixture of agricultural, suburban, and forestry changes, and all top five change types in the mixed wood shield consisted of various stages of the forestry cycle for both time periods. In comparison with previous LCLU change studies, overall change products showed similar trends. The discussion reviews why, while most changes had accuracies better than 84%, accuracies found for change from urban to other classes and from other classes to agriculture were lower due to unique aspects of change in these classes which are not relevant for most change analyses applications. The study found a consistent loss in the deciduous forest area for much of the time studied, which is shown to influence the aquatic nitrogen implicated in the expansion of the invasive plant Phragmites australis in the Great Lakes Basin. This underscores the importance of LCLU maps, which allow for the quantification of historical land change in the watersheds of the Great Lakes where invasive species are expanding. [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]

A GIS-based approach was used in this study to assess 10 years of land-use change in the lower Shivalik landscape of the Western Himalayas, India. The landscape encompasses two major protected areas which are home to a thriving population of large mammals. The main objective was to identify the changes in the land use pattern, specifically after forming a new tiger reserve in the landscape. Landsat 8 and 4 imagery were used for the time series analysis for the years 2018, 2013, and 2008, respectively. The change was calculated using nine land use classes, with mixed forest showing the highest change value, followed by agriculture, grassland, and scrub. The dense forest area increased immensely after the formation of the new tiger reserve. The results for dense and mixed forest classes also identified less fragmentation in the number of patches during the same time. Even though most of the landscape is within the boundaries of a protected area, the change in land use forms is noticeable. As development activities continue to expand, this region will face increased strain, which will impact the area's natural biodiversity. To protect that, a long-term conservation management effort is required. [ABSTRACT FROM AUTHOR]

Soil productivity refers to the capability of soil to sustain crop production, as determined by its complete range of physical, chemical, and biological attributes. The present study aimed to assess and monitoring the productivity of soils in El-Qaliobia Governorate, Egypt, which encompasses an area of 1022 km2. To determine the major physiographic units in the area, ENVI software 5.3 was used to process the "Landsat Enhanced Thematic Mapper Plus (ETM+) and Landsat Operational Land Imager (OLI) " images and digital elevation model. The study area is located in lower Egypt, north of Cairo, in the Nile Delta region, which was analyzed using multi-temporal Landsat imagery (2005 and 2022) and Digital Elevation Models. Results showed that the vegetation areas were decreased from 70% to 63 % during the period from 2005 to 2022 while the urban areas were increased from 23.9 % to 30.8 %. The main landform units over the area were delineated using Satellite images and the Digital Elevation Model (DEM). Out of twenty-nine soil profiles, thirteen were selected to represent different map units. Morphological descriptions were conducted as well as soil samples for physical and chemical analysis. soil samples were analyzed to classify soil productivity using the Requier Land Productivity Index (RLPI). The spatial analyst function within ArcGIS 10.8 was employed for the purpose of approximating the assessment of the moisture content rating, drainage condition, effective soil depth, texture/structure, soluble salt concentration, organic matter content, Cation exchange capacity, and mineral reserve. The results illustrated that approximately 38.3 % and 23.0 % of the entire expanse is comprised of good classes (II) and average classes (III), while Class IV and V accounts for a mere 35.8% and 2.9 % of the total area in 2005, then Approximately 25.5 % and 12.9 % of the entire expanse is comprised of good classes (II) and average classes (III), while Class IV and V accounts for a mere 35.8 % and 25.8% of the total area in 2022. [ABSTRACT FROM AUTHOR]

The regional ecological environment has become a significant subject of study due to the dynamics of the ecosystem, which is represented by vegetation, under the influence of human activities. The objective of this research is to demonstrate the implementation and effectiveness of the Forest Canopy Density (FCD) model in generating a map that illustrates changes in forest canopy density using multitemporal remote sensing data in Tabunio watershed. The methodology relies on vegetation index, including the Normalized Difference Vegetation Index (NDVI), shadow index (SI), and bare soil index (BI), to generate a composite vegetation index (CVI). FCD uses multitemporal remote sensing data from Landsat TM images from 2005 to 2020, which have been utilized to accomplish multi-source categorization. The findings indicated that the vegetation coverage of the Tabunio watershed presented a predominant pattern of high coverage in the northeastern and eastern regions, whereas most areas of the western region had low coverage; (2) vegetation cover from 2005 to 2020 is dominated by sparse to very dense vegetation cover classes; (3) changes in vegetation cover over two decades are very significant. The expansion of plantation land in 2005 caused a lot of non-vegetated land, which gradually changed in the following year period along with plant growth. At the end of 2020, the percentage of very dense vegetation became increasingly dominant, which was around 42 percent. The results of the study indicate the three biophysical index (NDVI, SI, and BI) used in this model approach were appropriate for precisely discriminating across all canopy density classes, as seen by the overall producer's accuracy of 81.3%. FCD model in multitemporal data can help in the early identification of deforestation or forest degradation activities. Furthermore, the FCD model may have certain constraints, as it requires an understanding of ground conditions to establish threshold values for each class.

Urban public safety management relies heavily on video surveillance systems, which provide crucial visual data for resolving a wide range of incidents and controlling unlawful activities. Traditional methods for target detection predominantly employ a two-stage approach, focusing on precision in identifying objects such as pedestrians and vehicles. These objects, typically sparse in large-scale, lower-quality surveillance footage, induce considerable redundant computation during the initial processing stage. This redundancy constrains real-time detection capabilities and escalates processing costs. Furthermore, transmitting raw images and videos laden with superfluous information to centralized back-end systems significantly burdens network communications and fails to capitalize on the computational resources available at diverse surveillance nodes. This study introduces DiffRank, a novel preprocessing method for fixed-angle video imagery in urban surveillance. The method strategically generates candidate regions during preprocessing, thereby reducing redundant object detection and improving the efficiency of the detection algorithm. Drawing upon change detection principles, a background feature learning approach utilizing shallow features has been developed. This approach prioritizes learning the characteristics of fixed-area backgrounds over direct background identification. As a result, alterations in ROI are efficiently discerned using computationally efficient shallow features, markedly accelerating the generation of proposed Regions of Interest (ROIs) and diminishing the computational demands for subsequent object detection and classification. Comparative analysis on various public and private datasets illustrates that DiffRank, while maintaining high accuracy, substantially outperforms existing baselines in terms of speed, particularly with larger image sizes (e.g., an improvement exceeding 300 % at 1920×1080 resolution). Moreover, the method demonstrates enhanced robustness compared to baseline methods, efficiently disregarding static targets like mannequins in display windows. The advancements in candidate area preprocessing enable a balanced approach between detection accuracy and overall detection speed, making the algorithm highly applicable for real-time on-site analysis in edge computing scenarios and cloud-edge collaborative computing environments.

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.

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.

The ecological quality of a region is significantly influenced by its geographical conditions, which can yield different effects on ecosystems. Nevertheless, the lack of adequate technology has impeded quantitative investigations into these differences. Consequently, there is an increasing demand for effective techniques to quantitatively measure differences in ecological quality resulting from variations in geographical conditions. This study applied the novel Remote Sensing-based Ecological Index (RSEI) concurrently to two distinct provincial-level regions in China, Fujian and Ningxia, to quantitatively detect their ecological differences. These two regions possess contrasting geographical conditions, with Fujian having high forest coverage and abundant rainfall, while Ningxia features low forest coverage and extensive loess plateau and desert terrain. By linking geographical factors with their corresponding ecological responses, we conducted a comprehensive analysis to determine whether the contrasting geographical conditions between the two regions had caused significant disparities in their ecological status. The results indicate that the contrasting geographical conditions have indeed led to marked ecological differences, with Fujian exhibiting excellent ecological status, while Ningxia lags behind due to unfavorable geographical conditions. In terms of RSEI scores, Fujian consistently achieved higher RSEI values (>0.8) in the study years, reaching an excellent ecological level, whereas Ningxia recorded scores lower than 0.45 during the comparable years, corresponding to a poor to moderate ecological level. Regarding the impact of geographical factors on ecological conditions, the positive contributions of greenness and wetness indicators to the ecology in Fujian were significantly greater than those in Ningxia (58% vs. 39%), whereas the contributions of negative indicators, dryness and hotness, were notably higher in Ningxia compared to Fujian (|–61|% vs. |–42|%). The successful concurrent application of RSEI to these two geographically distant regions also demonstrates the robustness of the RSEI technique.