Pub Date : 2025-02-01DOI: 10.1016/j.jag.2024.104306
You Li , Rui Li , Ziwei Li , Renzhong Guo , Shengjun Tang
In situations with a limited number of posed images, choosing the most suitable viewpoints becomes crucial for accurate Neural Radiance Fields (NeRF) modeling. Current approaches for view selection often rely on heuristic methods or are computationally intensive. To address these challenges, we introduce a new framework, OptiViewNeRF, which leverages scene uncertainty to guide the view selection process. Initially, an uncertainty estimation model of the entire scene is developed based on a preliminary NeRF model. This model then informs the selection of new perception viewpoints using a batch view selection strategy, allowing the entire process to be completed in a single iteration. By selecting viewpoints that provide informative data, this approach improves novel view synthesis results and accurately reconstructs 3D scenes. Experimental results on two selected datasets show that the proposed method effectively identifies informative viewpoints, resulting in more accurate scene reconstructions compared to baseline and state-of-the-art methods.
{"title":"OptiViewNeRF: Optimizing 3D reconstruction via batch view selection and scene uncertainty in Neural Radiance Fields","authors":"You Li , Rui Li , Ziwei Li , Renzhong Guo , Shengjun Tang","doi":"10.1016/j.jag.2024.104306","DOIUrl":"10.1016/j.jag.2024.104306","url":null,"abstract":"<div><div>In situations with a limited number of posed images, choosing the most suitable viewpoints becomes crucial for accurate Neural Radiance Fields (NeRF) modeling. Current approaches for view selection often rely on heuristic methods or are computationally intensive. To address these challenges, we introduce a new framework, OptiViewNeRF, which leverages scene uncertainty to guide the view selection process. Initially, an uncertainty estimation model of the entire scene is developed based on a preliminary NeRF model. This model then informs the selection of new perception viewpoints using a batch view selection strategy, allowing the entire process to be completed in a single iteration. By selecting viewpoints that provide informative data, this approach improves novel view synthesis results and accurately reconstructs 3D scenes. Experimental results on two selected datasets show that the proposed method effectively identifies informative viewpoints, resulting in more accurate scene reconstructions compared to baseline and state-of-the-art methods.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104306"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.jag.2025.104401
Dening Lu , Linlin Xu , Jun Zhou , Kyle Gao , Zheng Gong , Dedong Zhang
Segmentation of point clouds is foundational to numerous remote sensing applications. Recently, the development of Transformers has further improved segmentation techniques thanks to their great long-range context modeling capability. However, Transformers have quadratic complexity in inference time and memory, which both limits the input size and poses a strict hardware requirement. This paper presents a novel 3D-UMamba network with linear complexity, which is the earliest to introduce the Selective State Space Model (i.e., Mamba) to multi-source LiDAR point cloud processing. 3D-UMamba integrates Mamba into the classic U-Net architecture, presenting outstanding global context modeling with high efficiency and achieving an effective combination of local and global information. In addition, we propose a simple yet efficient 3D-token serialization approach (Voxel-based Token Serialization, i.e., VTS) for Mamba, where the Bi-Scanning strategy enables the model to collect features from all input points in different directions effectively. The performance of 3D-UMamba on three challenging LiDAR point cloud datasets (airborne MultiSpectral LiDAR (MS-LiDAR), aerial DALES, and vehicle-mounted Toronto-3D) demonstrated its superiority in multi-source LiDAR point cloud semantic segmentation, as well as the strong adaptability of Mamba to different types of LiDAR data, exceeding current state-of-the-art models. Ablation studies demonstrated the higher efficiency and lower memory costs of 3D-UMamba than its Transformer-based counterparts.
{"title":"3D-UMamba: 3D U-Net with state space model for semantic segmentation of multi-source LiDAR point clouds","authors":"Dening Lu , Linlin Xu , Jun Zhou , Kyle Gao , Zheng Gong , Dedong Zhang","doi":"10.1016/j.jag.2025.104401","DOIUrl":"10.1016/j.jag.2025.104401","url":null,"abstract":"<div><div>Segmentation of point clouds is foundational to numerous remote sensing applications. Recently, the development of Transformers has further improved segmentation techniques thanks to their great long-range context modeling capability. However, Transformers have quadratic complexity in inference time and memory, which both limits the input size and poses a strict hardware requirement. This paper presents a novel 3D-UMamba network with linear complexity, which is the earliest to introduce the Selective State Space Model (i.e., Mamba) to multi-source LiDAR point cloud processing. 3D-UMamba integrates Mamba into the classic U-Net architecture, presenting outstanding global context modeling with high efficiency and achieving an effective combination of local and global information. In addition, we propose a simple yet efficient 3D-token serialization approach (Voxel-based Token Serialization, i.e., VTS) for Mamba, where the Bi-Scanning strategy enables the model to collect features from all input points in different directions effectively. The performance of 3D-UMamba on three challenging LiDAR point cloud datasets (airborne MultiSpectral LiDAR (MS-LiDAR), aerial DALES, and vehicle-mounted Toronto-3D) demonstrated its superiority in multi-source LiDAR point cloud semantic segmentation, as well as the strong adaptability of Mamba to different types of LiDAR data, exceeding current state-of-the-art models. Ablation studies demonstrated the higher efficiency and lower memory costs of 3D-UMamba than its Transformer-based counterparts.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104401"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.jag.2024.104337
Jing Feng , Tim J. Grandjean , Johan van de Koppel , Daphne van der Wal
Sea level changes will significantly drive hydrodynamic, morphological, and ecological development of estuaries. However, the interplay of geomorphology and vegetation at estuary scales remains unclear. To better understand this process, we take the Western Scheldt estuary in the Netherlands as an example to reveal the link between changes in emersion duration and vegetation dynamics in the period 1993–2016. We found that tidal flats in the Western Scheldt become steeper—higher intertidal areas increased in elevation and emersion duration, whereas the low-lying edges of tidal flats experienced a decrease in elevation and emersion duration. We found that longer emersion duration was associated with increased plant diversity and cover. Furthermore, we detected the unique spatiotemporal response patterns of four abundant plant species to geomorphological variations. Our study suggests that on a large estuary scale, geomorphological changes are coupled to the richness and cover of plant communities, and that potential changes in relative sea level can induce structural modifications of the plant communities. It also emphasizes the importance of assessing the potential effects of localized relative sea level changes while considering all aspects of natural processes and direct and indirect human influences. Our study provides a framework to assess the bio-geomorphic processes in a spatially explicit way.
{"title":"A spatiotemporal framework to assess the bio-geomorphic interplay of saltmarsh vegetation and tidal emergence (Western Scheldt estuary)","authors":"Jing Feng , Tim J. Grandjean , Johan van de Koppel , Daphne van der Wal","doi":"10.1016/j.jag.2024.104337","DOIUrl":"10.1016/j.jag.2024.104337","url":null,"abstract":"<div><div>Sea level changes will significantly drive hydrodynamic, morphological, and ecological development of estuaries. However, the interplay of geomorphology and vegetation at estuary scales remains unclear. To better understand this process, we take the Western Scheldt estuary in the Netherlands as an example to reveal the link between changes in emersion duration and vegetation dynamics in the period 1993–2016. We found that tidal flats in the Western Scheldt become steeper—higher intertidal areas increased in elevation and emersion duration, whereas the low-lying edges of tidal flats experienced a decrease in elevation and emersion duration. We found that longer emersion duration was associated with increased plant diversity and cover. Furthermore, we detected the unique spatiotemporal response patterns of four abundant plant species to geomorphological variations. Our study suggests that on a large estuary scale, geomorphological changes are coupled to the richness and cover of plant communities, and that potential changes in relative sea level can induce structural modifications of the plant communities. It also emphasizes the importance of assessing the potential effects of localized relative sea level changes while considering all aspects of natural processes and direct and indirect human influences. Our study provides a framework to assess the bio-geomorphic processes in a spatially explicit way.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104337"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.jag.2024.104282
Daifeng Peng , Xuelian Liu , Yongjun Zhang , Haiyan Guan , Yansheng Li , Lorenzo Bruzzone
Change detection (CD) aims to compare and analyze images of identical geographic areas but different dates, whereby revealing spatio-temporal change patterns of Earth’s surface. With the implementation of the High-Resolution Earth Observation Project, an integrated sky-to-ground observation system has been continuously developed and improved. The accumulation of massive multi-modal, multi-angle, and multi-resolution remote sensing data have greatly enriched the CD data sources. Among them, high-resolution optical remote sensing images contain abundant spatial detail information, making it possible to interpret fine-grained scenes and greatly expand the application breadth and depth of CD. Generally, traditional optical remote sensing CD methods are cumbersome in steps and have a low level of automation. In contrast, artificial intelligence (AI) based CD methods possess powerful feature extraction and non-linear modeling capabilities, thereby gaining advantages that traditional methods cannot match. As a result, they have become the mainstream approaches in the field of CD. This review article systematically summarizes the datasets, theories, and methods of CD for optical remote sensing image. It provides a comprehensive analysis of AI-based CD algorithms based on deep learning paradigms from the perspectives of algorithm granularity. In-depth analysis of the performance of typical algorithms are further conducted. Finally, we summarize the challenges and trends of the CD algorithms in the AI era, aiming to provide important guidelines and insights for relevant researchers.
{"title":"Deep learning change detection techniques for optical remote sensing imagery: Status, perspectives and challenges","authors":"Daifeng Peng , Xuelian Liu , Yongjun Zhang , Haiyan Guan , Yansheng Li , Lorenzo Bruzzone","doi":"10.1016/j.jag.2024.104282","DOIUrl":"10.1016/j.jag.2024.104282","url":null,"abstract":"<div><div>Change detection (CD) aims to compare and analyze images of identical geographic areas but different dates, whereby revealing spatio-temporal change patterns of Earth’s surface. With the implementation of the High-Resolution Earth Observation Project, an integrated sky-to-ground observation system has been continuously developed and improved. The accumulation of massive multi-modal, multi-angle, and multi-resolution remote sensing data have greatly enriched the CD data sources. Among them, high-resolution optical remote sensing images contain abundant spatial detail information, making it possible to interpret fine-grained scenes and greatly expand the application breadth and depth of CD. Generally, traditional optical remote sensing CD methods are cumbersome in steps and have a low level of automation. In contrast, artificial intelligence (AI) based CD methods possess powerful feature extraction and non-linear modeling capabilities, thereby gaining advantages that traditional methods cannot match. As a result, they have become the mainstream approaches in the field of CD. This review article systematically summarizes the datasets, theories, and methods of CD for optical remote sensing image. It provides a comprehensive analysis of AI-based CD algorithms based on deep learning paradigms from the perspectives of algorithm granularity. In-depth analysis of the performance of typical algorithms are further conducted. Finally, we summarize the challenges and trends of the CD algorithms in the AI era, aiming to provide important guidelines and insights for relevant researchers.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104282"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.jag.2024.104342
Jiayuan Zhang , Yuhao Liu , Bochen Zhang , Siting Xiong , Chisheng Wang , Songbo Wu , Wu Zhu
Multi-temporal synthetic aperture radar interferometry (MT-InSAR) offers unique advantages in monitoring ground deformation and structural stability along the metro lines. However, a vast number of complex deformation points, millions and even more, can be derived from InSAR making it challenging to identify the deformation hotspot in time series automatically. This paper proposes a novel method for quantitatively assessing the MT-InSAR-derived deformation results. We first introduce an iterative seasonal trend decomposition using loess (STL) method to confirm the optimal period for separating seasonal components from the displacement time series. Then, an absolute differences detector with rolling windows is proposed to quantify the subsidence ratio within the time series and allow deformation hotspots to be more visible. To validate the effectiveness of the proposed method, 468 scenes of Sentinel-1A ascending images from Jun. 2015 to Nov. 2023 over the Hong Kong Mass Transit Railway (MTR) are adopted. The results indicate that 99.2% of areas are relatively stable with the displacement velocity ranging from −2 mm/year to 2 mm/year, and 84% of the study area remained a subsidence ratio below 0.3, except for localized hotspots that exhibited either short or long-term subsidence trends. The findings of this study indicate that multiple deformation hotspots were identified at the intersections of several metro lines in the Kowloon Peninsula and along the Island line. In addition to the displacement velocity from the conventional MT-InSAR, the overall and annual subsidence ratios have been demonstrated to be useful indicators for quantitative assessment of the construction-induced deformation.
{"title":"Spatiotemporal-grained quantitative assessment of construction-induced deformation along the MTR in Hong Kong using MT-InSAR and iterative STL-based subsidence ratio analysis","authors":"Jiayuan Zhang , Yuhao Liu , Bochen Zhang , Siting Xiong , Chisheng Wang , Songbo Wu , Wu Zhu","doi":"10.1016/j.jag.2024.104342","DOIUrl":"10.1016/j.jag.2024.104342","url":null,"abstract":"<div><div>Multi-temporal synthetic aperture radar interferometry (MT-InSAR) offers unique advantages in monitoring ground deformation and structural stability along the metro lines. However, a vast number of complex deformation points, millions and even more, can be derived from InSAR making it challenging to identify the deformation hotspot in time series automatically. This paper proposes a novel method for quantitatively assessing the MT-InSAR-derived deformation results. We first introduce an iterative seasonal trend decomposition using loess (STL) method to confirm the optimal period for separating seasonal components from the displacement time series. Then, an absolute differences detector with rolling windows is proposed to quantify the subsidence ratio within the time series and allow deformation hotspots to be more visible. To validate the effectiveness of the proposed method, 468 scenes of Sentinel-1A ascending images from Jun. 2015 to Nov. 2023 over the Hong Kong Mass Transit Railway (MTR) are adopted. The results indicate that 99.2% of areas are relatively stable with the displacement velocity ranging from −2 mm/year to 2 mm/year, and 84% of the study area remained a subsidence ratio below 0.3, except for localized hotspots that exhibited either short or long-term subsidence trends. The findings of this study indicate that multiple deformation hotspots were identified at the intersections of several metro lines in the Kowloon Peninsula and along the Island line. In addition to the displacement velocity from the conventional MT-InSAR, the overall and annual subsidence ratios have been demonstrated to be useful indicators for quantitative assessment of the construction-induced deformation.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104342"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.jag.2025.104381
Shangshu Cai , Yong Pang
Dividing a forest point cloud dataset into tiles is a common practice in point cloud processing (e.g., individual tree segmentation), aimed at addressing memory constraints and optimizing processing efficiency. Existing methods typically utilize automatic regular clipping (e.g., rectangular clipping), which tends to result in splitting tree crowns along the cutting lines. To preserve the completeness of tree crowns within predefined clipping boundaries (e.g., rectangles), we develop a tree crown edge-aware (E-A) point cloud clipping algorithm, named E-A algorithm. Firstly, the crown edge and distance features are enhanced and quantified using mathematical morphology and nearest neighbor pixel methods. Then, these two features are linearly weighted and integrated for cutting line detection. Finally, the optimal cutting lines are detected by exploring a set of edges with the minimum sum of integrated feature values. E-A algorithm was tested with airborne LiDAR point clouds collected from China’s Saihanba Forest Farm, comparing it against regular clipping methods. The results indicate that E-A algorithm can automatically and effectively emphasize preserving tree crown completeness within predefined clipping boundaries. It reduces crown fragmentation errors by 73.29% on average and maintains an average area difference of 6.42% compared to regular clippings. This algorithm provides a crucial tool for forest point cloud applications.
{"title":"A tree crown edge-aware clipping algorithm for airborne LiDAR point clouds","authors":"Shangshu Cai , Yong Pang","doi":"10.1016/j.jag.2025.104381","DOIUrl":"10.1016/j.jag.2025.104381","url":null,"abstract":"<div><div>Dividing a forest point cloud dataset into tiles is a common practice in point cloud processing (e.g., individual tree segmentation), aimed at addressing memory constraints and optimizing processing efficiency. Existing methods typically utilize automatic regular clipping (e.g., rectangular clipping), which tends to result in splitting tree crowns along the cutting lines. To preserve the completeness of tree crowns within predefined clipping boundaries (e.g., rectangles), we develop a tree crown edge-aware (E-A) point cloud clipping algorithm, named E-A algorithm. Firstly, the crown edge and distance features are enhanced and quantified using mathematical morphology and nearest neighbor pixel methods. Then, these two features are linearly weighted and integrated for cutting line detection. Finally, the optimal cutting lines are detected by exploring a set of edges with the minimum sum of integrated feature values. E-A algorithm was tested with airborne LiDAR point clouds collected from China’s Saihanba Forest Farm, comparing it against regular clipping methods. The results indicate that E-A algorithm can automatically and effectively emphasize preserving tree crown completeness within predefined clipping boundaries. It reduces crown fragmentation errors by 73.29% on average and maintains an average area difference of 6.42% compared to regular clippings. This algorithm provides a crucial tool for forest point cloud applications.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104381"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.jag.2025.104374
Qi Li , Xingyuan Zu , Ming Zhang , Jinghua Li , Yan Feng
Underwater hyperspectral target detection (HTD) technology holds pivotal value in enhancing maritime military power. However, the absorption and scattering properties of the water bodies result in the inevitable issue of mixed pixels in underwater hyperspectral images (HSIs). To address the issue, a joint hyperspectral unmixing and target detection network for underwater HSI is proposed, denoted as HUTDNet, which utilizes the material type and abundance information for downstream semantic tasks. Specifically, a nonlinear underwater unmixing network is designed to extract pure underwater endmembers and their associated abundance information, which is essential in assisting the subsequent target detection task. The network also extracts underwater virtual endmembers and their abundance values to reconstruct a more realistic underwater HSI. Then, the abundance weighting module determines the abundance weighting factor by calculating the spectral distance between a priori target spectra and the estimated underwater pure endmembers, generating a weighted abundance map. Finally, due to the inherent limitations in the characterization capabilities of abundance maps and endmembers, the detection network extracts key spectral feature maps from the input underwater HSI. These feature maps serve as complementary terms, fused with the original and weighted abundance maps. Subsequently, convolutional and fully connected layers are employed to extract deeper features and generate the target detection maps. Experiments on both real and synthetic datasets demonstrate the superior performance and efficiency of the proposed method in this paper compared to other state-of-the-art methods.
{"title":"HUTDNet: A joint unmixing and target detection network for underwater hyperspectral imagery","authors":"Qi Li , Xingyuan Zu , Ming Zhang , Jinghua Li , Yan Feng","doi":"10.1016/j.jag.2025.104374","DOIUrl":"10.1016/j.jag.2025.104374","url":null,"abstract":"<div><div>Underwater hyperspectral target detection (HTD) technology holds pivotal value in enhancing maritime military power. However, the absorption and scattering properties of the water bodies result in the inevitable issue of mixed pixels in underwater hyperspectral images (HSIs). To address the issue, a joint hyperspectral unmixing and target detection network for underwater HSI is proposed, denoted as HUTDNet, which utilizes the material type and abundance information for downstream semantic tasks. Specifically, a nonlinear underwater unmixing network is designed to extract pure underwater endmembers and their associated abundance information, which is essential in assisting the subsequent target detection task. The network also extracts underwater virtual endmembers and their abundance values to reconstruct a more realistic underwater HSI. Then, the abundance weighting module determines the abundance weighting factor by calculating the spectral distance between a priori target spectra and the estimated underwater pure endmembers, generating a weighted abundance map. Finally, due to the inherent limitations in the characterization capabilities of abundance maps and endmembers, the detection network extracts key spectral feature maps from the input underwater HSI. These feature maps serve as complementary terms, fused with the original and weighted abundance maps. Subsequently, convolutional and fully connected layers are employed to extract deeper features and generate the target detection maps. Experiments on both real and synthetic datasets demonstrate the superior performance and efficiency of the proposed method in this paper compared to other state-of-the-art methods.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104374"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.jag.2025.104396
Jianfeng Gao , Qingyan Meng , Linlin Zhang , Xinli Hu , Die Hu , Jiangkang Qian
Influenced by lockdown policies and anomalies in human activities, emergencies such as pandemic significantly altered the urban thermal environment. However, the spatiotemporal heat anomaly changes across and within cities during emergencies and their drivers have not been fully investigated. This study quantified the changes in the urban thermal environment in China before and during the COVID-19 pandemic. Based on z-scores and multiscale geographically weighted regression models, heat anomaly changes and transfer patterns of different land uses in cities with varying degrees of pandemic impact and drivers were estimated. During the entire year, we found that although the pandemic significantly reduced surface urban heat island intensity during 5 % to 35 % of days, it did not change significantly throughout 2020. During the first-level public health emergency response, the land surface temperatures of residential and commercial lands notably affected by the pandemic decreased by −0.195°C and −0.371°C, and the shifting of strong heat anomaly zones in industrial lands increased heat anomaly and no heat anomaly zones by 6.1 % and 1.4 %, respectively. Furthermore, thermal anomalies were highly correlated with changes in biophysical parameters during the pandemic. These findings provide insights and mitigation strategies for the fluctuations in the urban thermal environment caused by emergencies.
{"title":"Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations","authors":"Jianfeng Gao , Qingyan Meng , Linlin Zhang , Xinli Hu , Die Hu , Jiangkang Qian","doi":"10.1016/j.jag.2025.104396","DOIUrl":"10.1016/j.jag.2025.104396","url":null,"abstract":"<div><div>Influenced by lockdown policies and anomalies in human activities, emergencies such as pandemic significantly altered the urban thermal environment. However, the spatiotemporal heat anomaly changes across and within cities during emergencies and their drivers have not been fully investigated. This study quantified the changes in the urban thermal environment in China before and during the COVID-19 pandemic. Based on z-scores and multiscale geographically weighted regression models, heat anomaly changes and transfer patterns of different land uses in cities with varying degrees of pandemic impact and drivers were estimated. During the entire year, we found that although the pandemic significantly reduced surface urban heat island intensity during 5 % to 35 % of days, it did not change significantly throughout 2020. During the first-level public health emergency response, the land surface temperatures of residential and commercial lands notably affected by the pandemic decreased by −0.195°C and −0.371°C, and the shifting of strong heat anomaly zones in industrial lands increased heat anomaly and no heat anomaly zones by 6.1 % and 1.4 %, respectively. Furthermore, thermal anomalies were highly correlated with changes in biophysical parameters during the pandemic. These findings provide insights and mitigation strategies for the fluctuations in the urban thermal environment caused by emergencies.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104396"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Forest type classification is essential for the monitoring and management of forests, with significant implications for environmental protection and the mitigation of climate change. However, challenges such as multiscale variations, heterogeneous boundaries, mountainous terrain, and unbalanced data sets hinder progress. This study aims to improve forest type classification through three approaches: (1) Multimodal geospatial data fusion; (2) transfer learning using the ImageNet 22K dataset to improve accuracy and address class imbalances; (3) a novel Gated Graph Convolution Neural Network (GGCN). Experiments were conducted at two study sites with varying tree species, management strategies, and climates. The results indicated that very high-resolution aerial photographs outperform open-source Sentinel-1 and Sentinel-2 datasets. The fusion of the original remote sensing bands with the Enhanced Vegetation Index (EVI) feature demonstrates the best composition across all datasets. This approach, which combines the original Sentinel-1 and Sentinel-2 bands with the EVI, significantly improves the performance of open-source remote sensing data sets. It provides a cost-effective alternative to expensive high-resolution images, which is particularly beneficial for rural areas and global applications. Furthermore, utilizing ImageNet 22K transfer learning improved accuracy in addressing class imbalances. The GGCN effectively preserved multiscale and spatial features at both study sites. In general, this integrated approach shows promising potential for achieving high precision in large-scale forest type classification.
{"title":"Combined gated graph convolution neural networks with multi-modal geospatial data for forest type classification","authors":"Huiqing Pei , Toshiaki Owari , Satoshi Tsuyuki , Takuya Hiroshima , Danfeng Hong","doi":"10.1016/j.jag.2025.104372","DOIUrl":"10.1016/j.jag.2025.104372","url":null,"abstract":"<div><div>Forest type classification is essential for the monitoring and management of forests, with significant implications for environmental protection and the mitigation of climate change. However, challenges such as multiscale variations, heterogeneous boundaries, mountainous terrain, and unbalanced data sets hinder progress. This study aims to improve forest type classification through three approaches: (1) Multimodal geospatial data fusion; (2) transfer learning using the ImageNet 22K dataset to improve accuracy and address class imbalances; (3) a novel Gated Graph Convolution Neural Network (GGCN). Experiments were conducted at two study sites with varying tree species, management strategies, and climates. The results indicated that very high-resolution aerial photographs outperform open-source Sentinel-1 and Sentinel-2 datasets. The fusion of the original remote sensing bands with the Enhanced Vegetation Index (EVI) feature demonstrates the best composition across all datasets. This approach, which combines the original Sentinel-1 and Sentinel-2 bands with the EVI, significantly improves the performance of open-source remote sensing data sets. It provides a cost-effective alternative to expensive high-resolution images, which is particularly beneficial for rural areas and global applications. Furthermore, utilizing ImageNet 22K transfer learning improved accuracy in addressing class imbalances. The GGCN effectively preserved multiscale and spatial features at both study sites. In general, this integrated approach shows promising potential for achieving high precision in large-scale forest type classification.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104372"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.jag.2024.104334
Muhammad Fahad Baqa , Linlin Lu , Huadong Guo , Xiaoning Song , Seyed Kazem Alavipanah , Syed Nawaz-ul-Huda , Qingting Li , Fang Chen
Due to the compounding impacts of urbanization and climate change-induced warming, urban inhabitants face increasing risks of thermal health issues. The use of high-resolution maps that categorize intra-urban thermal environment and Local Climate Zones (LCZ) could enhance the understanding of the correlation between heat-related health risks and microclimates. In this study, a fine-scale heat risk assessment framework was applied in an arid megacity, Karachi, Pakistan. Following Crichton’s Risk Triangle framework, heat health risks were mapped by considering hazard-exposure-vulnerability components at the census ward level. The heat hazard was mapped using SDGSAT-1 thermal infrared data at a 30 m spatial resolution during summer season. Factors contributing most to heat vulnerability were identified as the availability of electricity facilities, bathroom facilities, and housing density, with contribution rates of 47.51 %, 21.86 %, and 8.07 %, respectively. Heat risks were considerably higher for built types (0.16) compared to natural LCZ types (0.07), with 65 % of LCZ 2, 3, 6, and 7 (compact mid-rise, compact low-rise, open low-rise, and lightweight low-rise areas) identified as high-risk areas. To mitigate heat risks, green space should be planned in LCZ2 and LCZ3 characterized by dense population and compact buildings arrangement, and public cooling facilities and infrastructure should be improved in LCZ7 featured with squatter and slum settlements. Urban planners may consider restricting the growth of these areas in newly-developed regions, including encroachments and unplanned settlements, to prevent further exacerbation of heat stress. This study offers a valuable guide for assessing and alleviating heat risks at the community level, thereby promoting the development of heat resilient urban areas.
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