International journal of applied earth observation and geoinformation : ITC journal最新文献_第6页

Identification of shrinkage patterns in Japan’s four major metropolitan areas based on nighttime light and population data

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104391

Hao Zheng, Runsen Zhang

Urban shrinkage has become a critical global issue, influencing the sustainable development of cities across social, economic, and environmental dimensions. In Japan, which is characterized by an aging population and low birth rate, this phenomenon has now extended to metropolitan areas, presenting new challenges for urban sustainability. Although many studies have been conducted regarding urban decline in rural regions, the shrinkage dynamics within Japan’s major cities are poorly understood. Addressing this knowledge gap is crucial for devising targeted urban-planning strategies that ensure the long-term viability of urban areas. Here, we integrated Suomi National Polar-orbiting Partnership–Visible Infrared Imager Radiometer Suite nighttime light data with WorldPop population data to examine the patterns of urban shrinkage from 2012 to 2020 in Japan’s four largest metropolitan areas: Tokyo, Osaka, Nagoya, and Fukuoka. Using Theil–Sen median trend analysis and K-means clustering, we developed a method to quantify both shrinking and growing areas within these regions. It was found that Tokyo exhibited the highest urban vitality, with minimal shrinkage, whereas Nagoya and Osaka faced greater declines. Fukuoka displayed a distinct east–west spatial pattern of urban shrinkage. This study introduces the “triple V” theory, which evaluates urban vitality through the lenses of robustness and activity levels. Our analysis highlights the spatial complexities of urban shrinkage, emphasizing the importance of region-specific urban planning. By providing new insights obtained from a data-driven analysis, we offer a framework for policymakers to promote sustainable urban development in the face of demographic and spatial challenges.

{"title":"Identification of shrinkage patterns in Japan’s four major metropolitan areas based on nighttime light and population data","authors":"Hao Zheng, Runsen Zhang","doi":"10.1016/j.jag.2025.104391","DOIUrl":"10.1016/j.jag.2025.104391","url":null,"abstract":"<div><div>Urban shrinkage has become a critical global issue, influencing the sustainable development of cities across social, economic, and environmental dimensions. In Japan, which is characterized by an aging population and low birth rate, this phenomenon has now extended to metropolitan areas, presenting new challenges for urban sustainability. Although many studies have been conducted regarding urban decline in rural regions, the shrinkage dynamics within Japan’s major cities are poorly understood. Addressing this knowledge gap is crucial for devising targeted urban-planning strategies that ensure the long-term viability of urban areas. Here, we integrated Suomi National Polar-orbiting Partnership–Visible Infrared Imager Radiometer Suite nighttime light data with WorldPop population data to examine the patterns of urban shrinkage from 2012 to 2020 in Japan’s four largest metropolitan areas: Tokyo, Osaka, Nagoya, and Fukuoka. Using Theil–Sen median trend analysis and K-means clustering, we developed a method to quantify both shrinking and growing areas within these regions. It was found that Tokyo exhibited the highest urban vitality, with minimal shrinkage, whereas Nagoya and Osaka faced greater declines. Fukuoka displayed a distinct east–west spatial pattern of urban shrinkage. This study introduces the “triple V” theory, which evaluates urban vitality through the lenses of robustness and activity levels. Our analysis highlights the spatial complexities of urban shrinkage, emphasizing the importance of region-specific urban planning. By providing new insights obtained from a data-driven analysis, we offer a framework for policymakers to promote sustainable urban development in the face of demographic and spatial challenges.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104391"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137371","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}

引用次数: 0

OptiViewNeRF: Optimizing 3D reconstruction via batch view selection and scene uncertainty in Neural Radiance Fields OptiViewNeRF：在神经辐射场中通过批量视图选择和场景不确定性优化3D重建

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 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.

在摆姿势图像数量有限的情况下，选择最合适的视点对于准确的神经辐射场（NeRF）建模至关重要。当前的视图选择方法通常依赖于启发式方法或计算密集型方法。为了应对这些挑战，我们引入了一个新的框架，OptiViewNeRF，它利用场景的不确定性来指导视图选择过程。首先，在初步NeRF模型的基础上，建立了整个场景的不确定性估计模型。然后，该模型使用批处理视图选择策略通知新感知视点的选择，从而允许在单个迭代中完成整个过程。通过选择提供信息数据的视点，该方法改进了新的视图合成结果，并准确地重建了3D场景。在两个选定的数据集上的实验结果表明，该方法有效地识别了信息视角，与基线和当前的方法相比，产生了更准确的场景重建。

{"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}

引用次数: 0

3D-UMamba: 3D U-Net with state space model for semantic segmentation of multi-source LiDAR point clouds

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 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}

引用次数: 0

A spatiotemporal framework to assess the bio-geomorphic interplay of saltmarsh vegetation and tidal emergence (Western Scheldt estuary) 盐沼植被与潮汐相互作用的时空框架研究（西谢尔德河河口）

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 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.

海平面的变化将对河口的水动力、形态和生态发展产生重要影响。然而，在河口尺度上，地貌与植被的相互作用尚不清楚。为了更好地理解这一过程，我们以荷兰西舍尔德河河口为例，揭示了1993-2016年期间海蚀期变化与植被动态之间的联系。研究发现，西斯海尔德河潮滩坡度陡，潮间带高程增加，潮间带高程增加，潮间带低洼边缘高程减少，潮间带高程减少。我们发现，较长的重现时间与增加的植物多样性和覆盖有关。此外，我们还检测了四种丰富的植物物种对地貌变化的独特时空响应模式。研究表明，在大河口尺度上，地貌变化与植物群落的丰富度和覆盖度是耦合的，相对海平面的潜在变化可以引起植物群落的结构改变。它还强调评估局部相对海平面变化的潜在影响的重要性，同时考虑到自然过程的所有方面以及直接和间接的人类影响。我们的研究提供了一个框架，以空间明确的方式评估生物地貌过程。

{"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}

引用次数: 0

Deep learning change detection techniques for optical remote sensing imagery: Status, perspectives and challenges 光学遥感图像的深度学习变化检测技术：现状、展望和挑战

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 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.

变化检测（Change detection， CD）旨在对相同地理区域不同日期的图像进行比较分析，从而揭示地球表面的时空变化规律。随着高分辨率对地观测工程的实施，对地综合观测系统不断发展完善。大量多模态、多角度、多分辨率遥感数据的积累，极大地丰富了遥感数据的来源。其中，高分辨率光学遥感影像包含了丰富的空间细节信息，使得对细粒度场景的解读成为可能，极大地拓展了CD的应用广度和深度。传统的光学遥感CD方法一般步骤繁琐，自动化程度较低。而基于人工智能（AI）的CD方法具有强大的特征提取和非线性建模能力，具有传统方法无法比拟的优势。本文系统地综述了光学遥感图像的数据集、理论和方法。从算法粒度的角度全面分析了基于深度学习范式的基于ai的CD算法。进一步深入分析了典型算法的性能。最后，我们总结了人工智能时代CD算法面临的挑战和趋势，旨在为相关研究人员提供重要的指导和见解。

{"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}

引用次数: 0

Spatiotemporal-grained quantitative assessment of construction-induced deformation along the MTR in Hong Kong using MT-InSAR and iterative STL-based subsidence ratio analysis 利用MT-InSAR和基于迭代stl的沉降比分析对香港地铁沿线施工引起的变形进行时空粒度定量评估

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 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.

多时相合成孔径雷达干涉测量技术（MT-InSAR）在地铁沿线的地面变形和结构稳定性监测方面具有独特的优势。然而，InSAR可以获得大量复杂的变形点，数百万甚至更多，这给自动识别时间序列中的变形热点带来了挑战。本文提出了一种定量评估mt - insar衍生变形结果的新方法。首先采用黄土（STL）方法进行迭代季节趋势分解，确定从位移时间序列中分离季节分量的最优周期。然后，提出了一种带滚动窗的绝对差值检测器，以量化时间序列内的沉降比，并使变形热点更加明显。为了验证该方法的有效性，采用了2015年6月至2023年11月在香港地下铁路（MTR）上拍摄的468幅Sentinel-1A上升图像。结果表明：99.2%的区域相对稳定，位移速度在−2 mm/年~ 2 mm/年之间；除局部热点地区表现出短期或长期沉降趋势外，84%的区域沉降率保持在0.3以下；研究结果表明，在九龙半岛和港岛线沿线多条地铁线路的交汇处发现了多个变形热点。除了常规MT-InSAR的位移速度外，总体沉降比和年沉降比已被证明是定量评估施工引起变形的有用指标。

{"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}

引用次数: 0

A tree crown edge-aware clipping algorithm for airborne LiDAR point clouds

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 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}

引用次数: 0

HUTDNet: A joint unmixing and target detection network for underwater hyperspectral imagery HUTDNet：一种水下高光谱图像解混与目标检测联合网络

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 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.

水下高光谱目标探测技术在增强海上军事力量中具有举足轻重的价值。然而，水体的吸收和散射特性导致了水下高光谱图像中不可避免的混合像元问题。为了解决这一问题，提出了一种水下高光谱解混和目标检测联合网络，称为HUTDNet，该网络利用材料类型和丰度信息进行下游语义任务。具体而言，设计了一个非线性水下解混网络来提取纯水下端元及其相关丰度信息，这对后续的目标探测任务至关重要。该网络还提取了水下虚拟端元及其丰度值，以重建更真实的水下HSI。然后，丰度加权模块通过计算先验目标光谱与估计的水下纯端元之间的光谱距离来确定丰度加权因子，生成加权丰度图。最后，由于丰度图和端元表征能力的固有局限性，检测网络从输入的水下HSI中提取关键光谱特征图。这些特征图作为补充术语，与原始的和加权的丰度图融合在一起。随后，使用卷积和全连接层提取更深的特征并生成目标检测图。在真实数据集和合成数据集上的实验表明，与其他最新方法相比，本文提出的方法具有优越的性能和效率。

{"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}

引用次数: 0

Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 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}

引用次数: 0

Combined gated graph convolution neural networks with multi-modal geospatial data for forest type classification

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104372

Huiqing Pei , Toshiaki Owari , Satoshi Tsuyuki , Takuya Hiroshima , Danfeng Hong

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}

引用次数: 0