International Journal of Digital Earth最新文献

Combined subspace low rank learning and nonlocal low rank estimation for spectral super-resolution of multispectral remote sensing images 子空间低秩学习与非局部低秩估计相结合的多光谱遥感影像光谱超分辨率研究

1区地球科学

International Journal of Digital Earth Pub Date : 2025-10-06 DOI: 10.1080/17538947.2025.2556237

Jun Zhou, Weiwei Sun, Xiangchao Meng, Gang Yang, Binjie Chen, Kai Ren

{"title":"Combined subspace low rank learning and nonlocal low rank estimation for spectral super-resolution of multispectral remote sensing images","authors":"Jun Zhou, Weiwei Sun, Xiangchao Meng, Gang Yang, Binjie Chen, Kai Ren","doi":"10.1080/17538947.2025.2556237","DOIUrl":"https://doi.org/10.1080/17538947.2025.2556237","url":null,"abstract":"Spectral Super-Resolution (SR) of Multispectral Images (MSI) enhances spectral resolution in MSI's non-overlapping regions by utilizing the overlapping regions with Hyperspectral Images (HSI). This technique effectively addresses the trade-off between spectral resolution and spatial coverage in remote sensing imagery, attracting significant research attention. However, traditional spectral SR methods based on sparse representation and deep learning primarily exploit local similarities in HSI, neglecting prevalent non-local spatial patterns, thus limiting reconstruction performance. In this paper, we propose a Combined Subspace Low Rank Learning and NonLocal Low Rank Estimation (CSLNLE) method for MSI spectral SR, capturing both global spectral correlations and nonlocal spatial-spectral self-similarity. The CSLNLE method decomposes the target HSI into a low-rank dictionary of subspaces and associated coefficient matrices. Through low-rank learning, we derive dictionaries from overlapping HSI-MSI regions and estimate coefficients for non-overlapping MSI regions using a nonlocal tensor multi-rank prior. Specifically, images are partitioned into patches, clustered by similarity, and coefficient matrices are estimated via low-tensor multi-rank decomposition within each group. Spectral SR experiments on simulated and real datasets using seven current benchmark methods validate the superiority of our proposed method.","PeriodicalId":54962,"journal":{"name":"International Journal of Digital Earth","volume":"18 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147879029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Comprehensive assessment and analysis of urban resilience based on fusion modeling–taking guilin as an example 基于融合模型的城市韧性综合评价与分析——以桂林市为例

1区地球科学

International Journal of Digital Earth Pub Date : 2025-07-31 DOI: 10.1080/17538947.2025.2538211

Liduo Dou, Lanwei Zhu, Yuxin He, Mingquan Wu, Yifei Guo

{"title":"Comprehensive assessment and analysis of urban resilience based on fusion modeling–taking guilin as an example","authors":"Liduo Dou, Lanwei Zhu, Yuxin He, Mingquan Wu, Yifei Guo","doi":"10.1080/17538947.2025.2538211","DOIUrl":"https://doi.org/10.1080/17538947.2025.2538211","url":null,"abstract":"Dramatic global climate change has led to an increase in multiple and uncertain risks to urban safety and sustainable development, and cities urgently need to enhance their comprehensive response capacity. Urban resilience emphasizes the symbiosis with risks, which can improve the risk management effectiveness of urban systems in complex and uncertain environments, and meets the social needs of disaster normalization. Guilin has long been plagued by frequent floods and droughts, which have caused significant economic losses, and exacerbated the city's development load. This study constructs an urban resilience indicator system from five dimensions: economic, social, ecological, engineering and disaster, and uses the CRITIC-ωTOPSIS method to solve the problem of weight distribution of indicators, highlight the influence of indicator weights on the urban toughness scores over the years, and quantitatively assess the urban toughness of Guilin from 2000 to 2023, revealing its spatial–temporal differentiation characteristics. The results show that:(1) During the period 2000-2023, Guilin's urban resilience as a whole shows a fluctuating ‘V-shaped’ development.(2) Guilin City demonstrates clear evolutionary resilience, with its four area types undergoing leapfrogging and transformation across both time and space. This study offers key theoretical and practical guidance for Guilin's sustainable development.","PeriodicalId":54962,"journal":{"name":"International Journal of Digital Earth","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147877285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud data 基于无人机RGB图像和LiDAR点云数据的露天矿植被类型精细识别

1区地球科学

International Journal of Digital Earth Pub Date : 2025-06-04 DOI: 10.1080/17538947.2025.2515269

Suling He, Yuncheng Deng, Jinliang Wang, Mengjia Luo

{"title":"Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud data","authors":"Suling He, Yuncheng Deng, Jinliang Wang, Mengjia Luo","doi":"10.1080/17538947.2025.2515269","DOIUrl":"https://doi.org/10.1080/17538947.2025.2515269","url":null,"abstract":"Accurate vegetation type information is essential for the scientific assessment of ecological restoration in open pit mining areas. However, many existing mine-mapping studies tend to classify all vegetation into a single class, overlooking inter-species differentiation and thereby limiting the precision of ecological evaluations. To address this issue, this study proposed a multi-scale hierarchical classification (MSHC) method for fine-scale mapping of vegetation types in open phosphate mining areas by combining unmanned aerial vehicle (UAV) RGB remote sensing imagery and light detection and ranging (LiDAR) data with the support of object-oriented methods. The results showed that (1) The all features (AF) scheme achieved an overall accuracy (OA) of 97.04% and a Kappa coefficient of 0.98. (2) The proposed MSHC method reached an OA of 97.69% and a Kappa of 0.97. (3) The vegetation in the study area was predominantly grassland (approximately 29.57% of the total area), with Alnus nepalensis being the dominant tree species (16.07% of the area). Overall, this study provides a detailed classification of vegetation types, offering a valuable dataset for ecological monitoring and assessment. The proposed method also presents a transferable and effective approach for the fine-scale vegetation mapping of other open-pit mining environments.","PeriodicalId":54962,"journal":{"name":"International Journal of Digital Earth","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147899366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

MLFA-Net: multi-level feature-aggregated network for semantic change detection in remote sensing images MLFA-Net：用于遥感图像语义变化检测的多级特征聚合网络

IF 5.1 1区地球科学

International Journal of Digital Earth Pub Date : 2024-09-09 DOI: 10.1080/17538947.2024.2398070

Qing Ding, Zhenfeng Shao, Xiao Huang, Fengyan Wang, Mingchang Wang

引用次数: 0

An improved bridge reference network for 3-D SAR tomography based on regional growing strategy 基于区域增长策略的三维合成孔径雷达断层成像改进型桥梁参考网络

IF 5.1 1区地球科学

International Journal of Digital Earth Pub Date : 2024-09-09 DOI: 10.1080/17538947.2024.2398072

Youjun Wang, Zhen Dong, Xiantao Wang, Anxi Yu, Yifei Ji, Xing Peng

引用次数: 0

MAL-YOLO: a lightweight algorithm for target detection in side-scan sonar images based on multi-scale feature fusion and attention mechanism MAL-YOLO：基于多尺度特征融合和注意力机制的侧扫声纳图像目标检测轻量级算法

IF 5.1 1区地球科学

International Journal of Digital Earth Pub Date : 2024-09-04 DOI: 10.1080/17538947.2024.2398050

Yu Cao, Xiaodong Cui, Mingyi Gan, Yaxue Wang, Fanlin Yang, Yi Huang

引用次数: 0

Assessing water use efficiency reactivity to meteorological, hydrological, and agricultural droughts on the Mongolian Plateau 评估蒙古高原用水效率对气象、水文和农业干旱的反应能力

IF 5.1 1区地球科学

International Journal of Digital Earth Pub Date : 2024-09-04 DOI: 10.1080/17538947.2024.2398056

Li Mei, Siqin Tong, Shan Yin, Yuhai Bao, Yongfang Wang, Enliang Guo, Fei Li, Xiaojun Huang, Tuya Alateng, Dongwei Liu, Lumen Chao, Jinyuan Ren

引用次数: 0

PM2.5 estimation and its relationship with NO2 and SO2 in China from 2016 to 2020 2016-2020年中国PM2.5估算及其与二氧化氮和二氧化硫的关系

IF 5.1 1区地球科学

International Journal of Digital Earth Pub Date : 2024-09-02 DOI: 10.1080/17538947.2024.2398055

Huangyuan Tan, Yumin Chen, Feiyue Mao, John P. Wilson, Tengfei Zhang, Xiana Cui, Zhanghui Li

引用次数: 0

Integrating trajectory data and demographic characteristics: a trajectory semantic model for predicting travel flow and conducting interaction analysis 整合轨迹数据和人口特征：用于预测旅行流量和进行互动分析的轨迹语义模型

IF 5.1 1区地球科学

International Journal of Digital Earth Pub Date : 2024-08-30 DOI: 10.1080/17538947.2024.2392842

Changjian Liu, Shuhui Gong, Hui Su, Jianwei Chen, Honglei Guo, Jifeng He, Changfeng Jing, Yu Liu

引用次数: 0

A full-attention network with an open dataset for large-scale building semantic segmentation along long-span high-speed rail lines 用于大跨度高铁沿线大规模建筑物语义分割的全注意力网络与开放数据集

IF 5.1 1区地球科学

International Journal of Digital Earth Pub Date : 2024-08-30 DOI: 10.1080/17538947.2024.2393265

Wenfan Qiao, Li Shen, Jicheng Wang, Zhilin Li

引用次数: 0