ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"A Spatially Masked Adaptive Gated Network for multimodal post-flood water extent mapping using SAR and incomplete multispectral data","authors":"Hyunho Lee,&nbsp;Wenwen Li","doi":"10.1016/j.isprsjprs.2025.12.023","DOIUrl":"10.1016/j.isprsjprs.2025.12.023","url":null,"abstract":"<div><div>Mapping water extent during a flood event is essential for effective disaster management throughout all phases: mitigation, preparedness, response, and recovery. In particular, during the response stage, when timely and accurate information is important, Synthetic Aperture Radar (SAR) data are primarily employed to produce water extent maps. This is because SAR sensors can observe through cloud cover and operate both day and night, whereas Multispectral Imaging (MSI) data, despite providing higher mapping accuracy, are only available under cloud-free and daytime conditions. Recently, leveraging the complementary characteristics of SAR and MSI data through a multimodal approach has emerged as a promising strategy for advancing water extent mapping using deep learning models. This approach is particularly beneficial when timely post-flood observations, acquired during or shortly after the flood peak, are limited, as it enables the use of all available imagery for more accurate post-flood water extent mapping. However, the adaptive integration of partially available MSI data into the SAR-based post-flood water extent mapping process remains underexplored. To bridge this research gap, we propose the Spatially Masked Adaptive Gated Network (SMAGNet), a multimodal deep learning model that utilizes SAR data as the primary input for post-flood water extent mapping and integrates complementary MSI data through feature fusion. In experiments on the C2S-MS Floods dataset, SMAGNet consistently outperformed other multimodal deep learning models in prediction performance across varying levels of MSI data availability. Specifically, SMAGNet achieved the highest IoU score of 86.47% using SAR and MSI data and maintained the highest performance with an IoU score of 79.53% even when MSI data were entirely missing. Furthermore, we found that even when MSI data were completely missing, the performance of SMAGNet remained statistically comparable to that of a U-Net model trained solely on SAR data. These findings indicate that SMAGNet enhances the model robustness to missing data as well as the applicability of multimodal deep learning in real-world flood management scenarios. The source code is available at <span><span>https://github.com/ASUcicilab/SMAGNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 492-508"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond synthetic scenarios: Weakly-supervised super-resolution for spatiotemporally misaligned remote sensing images","authors":"Quanyi Guo ,&nbsp;Rui Liu ,&nbsp;Yangtian Fang ,&nbsp;Yi Gao ,&nbsp;Jun Chen ,&nbsp;Xin Tian","doi":"10.1016/j.isprsjprs.2025.12.019","DOIUrl":"10.1016/j.isprsjprs.2025.12.019","url":null,"abstract":"<div><div>Deep learning-based remote sensing image super-resolution is crucial for enhancing the spatial resolution of Earth observation data. Due to the absence of perfectly aligned pairs of high- and low-resolution remote sensing images, most existing supervised and self-supervised approaches rely on synthetic degradation models or internal structural consistency to generate training data. Consequently, these methods suffer from the domain gap between synthetic and real datasets, which limits their ability to model realistic degradation and degrades their performance in real scenes. To overcome this challenge, we propose STANet, a weakly-supervised super-resolution method for spatiotemporally misaligned remote sensing images. In particular, STANet directly utilizes images of the same region captured by multiple satellites at different resolutions as datasets, to boost the real remote sensing image super-resolution performance. However, this approach also introduces new challenges related to spatiotemporal misalignment. To address this, we design a spatiotemporal align module that includes a Scale Align Module (SAM) and a Temporal Align Module (TAM). SAM uses affine transformations to align spatial features at both the pixel and global levels, while TAM applies window-based attention to adjust the weight of image content, mitigating the misleading effects of temporal misalignment on results. Besides, we also design a style encoder based on contrastive learning and a structure encoder based on variational inference, which guide SAM and TAM for feature alignment and enhance adaptability. Finally, the feature-aligned output, after upsampling, are fused with the high-frequency-enhancing output of the texture transfer module through the weighted fusion module to generate the super-resolution image. Extensive experiments on synthetic datasets based on AID and RSSR25, real datasets captured by GaoFen satellites, and cross-satellite experiments on Landsat-8 datasets demonstrate STANet’s superiority over other state-of-the-art methods.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 524-541"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TFRSUB: A terrain-feature retention and spatial uniformity balancing method for simplifying LiDAR ground point clouds","authors":"Chuanfa Chen,&nbsp;Ziming Yang,&nbsp;Hongming Pan,&nbsp;Yanyan Li,&nbsp;Jinda Hao","doi":"10.1016/j.isprsjprs.2025.12.015","DOIUrl":"10.1016/j.isprsjprs.2025.12.015","url":null,"abstract":"<div><div>The processing of high-density LiDAR point clouds presents significant computational challenges for DEM generation due to data redundancy and topographic feature degradation during simplification. To overcome this problem, this study proposes a Terrain-Feature Retention and Spatial Uniformity Balancing (TFRSUB) method that integrates three key innovations: (i) a Distance-Geometric Synergy Index (DGSI) combining orthogonal deviation distance and point sampling interval to mitigate boundary contraction artifacts; (ii) a Composite Terrain Factor (CTF) synthesizing multiple terrain parameters to characterize diverse topographic features; and (iii) a cluster-driven Gaussian Process Regression (GPR) framework using CTF for iterative feature point selection, optimizing the trade-off between topographic fidelity and point distribution homogeneity. Evaluated on eight high-resolution LiDAR terrain point clouds across six retention ratios, TFRSUB demonstrates significant accuracy improvements over seven state-of-the-art methods, achieving reductions of 9.22%–64.70% in DEM root mean square error, 7.80%–61.34% in mean absolute error, 16.43%–76.88% in slope error, and 28.12%–81.35% in mean curvature error. These results establish TFRSUB as an alternative solution for LiDAR point cloud simplification that maintains topographic fidelity while addressing computational storage challenges.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 389-407"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An SW-TES hybrid algorithm for retrieving mountainous land surface temperature from high-resolution thermal infrared remote sensing data","authors":"Zhi-Wei He ,&nbsp;Bo-Hui Tang ,&nbsp;Zhao-Liang Li","doi":"10.1016/j.isprsjprs.2026.01.016","DOIUrl":"10.1016/j.isprsjprs.2026.01.016","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Mountainous land surface temperature (MLST) is a key parameter for studying the energy exchange between land surface and atmosphere in mountainous areas. However, traditional land surface temperature (LST) retrieval methods often neglect the influence of three-dimensional (3D) structures and adjacent pixels due to rugged terrain. To address this, a mountainous split-window and temperature-emissivity separation (MSW-TES) hybrid algorithm was proposed to retrieve MLST. The hybrid algorithm that combines the improved split window (SW) algorithm and temperature-emissivity separation (TES) algorithm, which considering the topographic and adjacent effects (T-A effect) to retrieve MLST from five thermal infrared (TIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). In this hybrid algorithm, an improved mountainous canopy multiple scattering TIR radiative transfer model was proposed to construct the simulation dataset. Then, an improved SW algorithm was developed to build a 3D lookup table (LUT) of regression coefficients using small-scale self-heating parameter (SSP) and sky-view factor (SVF) to estimate brightness temperature (BT) at ground level. Furthermore, The TES algorithm was refined to account for the influence of rugged terrain within pixel on mountainous land surface effective emissivity (MLSE) by reconstructing the relationship between minimum emissivity and maximum-minimum difference (MMD) for different SSPs. Results from simulated data show that the accuracy of the improved SW algorithm is increased by up to 0.5 K at most for estimating BT at ground level. The MSW-TES algorithm, considering the T-A effect, generally retrieves lower LST values compared to those without this consideration. The hybrid algorithm yielded root mean square error (RMSE) of 0.99 K and 1.83 K for LST retrieval with and without the T-A effect, respectively, with most differences falling between 0.0 K and 3.0 K. The sensitivity analysis indicated that the perturbation of input parameters has little influence on MLST and MLSE, which proves that the MSW-TES algorithm has strong robustness. Additionally, the accuracy of MLST retrieval by the MSW-TES algorithm was validated using both discrete anisotropic radiative transfer (DART) model simulations and &lt;em&gt;in-situ&lt;/em&gt; measurements. The validation result of DART simulations showed biases ranging from −0.13 K to 1.03 K and RMSEs from 0.76 K to 1.29 K across the five ASTER TIR bands, while validation result of the in-situ measurements yielded a bias of 0.97 K and an RMSE of 1.25 K, demonstrating consistent and reliable results. This study underscores the necessity of accounting for the T-A effect to improve MLST retrieval and provides a promising pathway for global clear-sky high-resolution MLST mapping in upcoming thermal missions. The source code and simulated data are available at &lt;span&gt;&lt;span&gt;https://github.com/hezwppp/MSW-TES&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;.&lt;/div&gt;&lt;/div","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 865-889"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing monocular height estimation via sparse LiDAR-guided correction","authors":"Jian Song ,&nbsp;Hongruixuan Chen ,&nbsp;Naoto Yokoya","doi":"10.1016/j.isprsjprs.2025.12.004","DOIUrl":"10.1016/j.isprsjprs.2025.12.004","url":null,"abstract":"<div><div>Monocular height estimation (MHE) from very-high-resolution (VHR) remote sensing imagery via deep learning is notoriously challenging due to the lack of sufficient structural information. Conventional digital elevation models (DEMs), typically derived from airborne LiDAR or multi-view stereo, remain costly and geographically limited. While state-of-the-art monocular height estimation (MHE) and depth estimation (MDE) models show great promise, their robustness under varied illumination conditions remains a significant challenge. To address this, we introduce a novel and fully automated correction pipeline that integrates sparse, imperfect global LiDAR measurements (ICESat-2) with deep learning outputs to enhance local accuracy and robustness. Importantly, the entire workflow is fully automated and built solely on publicly available models and datasets, requiring only a single georeferenced optical image to generate corrected height maps, thereby ensuring unprecedented accessibility and global scalability. Furthermore, we establish the first comprehensive benchmark for this task, evaluating a suite of correction methods that includes two random forest-based approaches, four parameter-efficient fine-tuning techniques, and full fine-tuning. We conduct extensive experiments across six large-scale, diverse regions at 0.5<!--> <!-->m resolution, totaling approximately 297<!--> <!-->km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, encompassing the urban cores of Tokyo, Paris, and São Paulo, as well as mixed suburban and forest landscapes. Experimental results demonstrate that the best-performing correction method reduces the MHE model’s mean absolute error (MAE) by an average of 30.9% and improves its <span><math><msubsup><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow><mrow><mtext>HE</mtext></mrow></msubsup></math></span> score by 44.2%. For the MDE model, the MAE is improved by 24.1% and the <span><math><msubsup><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow><mrow><mtext>HE</mtext></mrow></msubsup></math></span> score by 25.1%. These findings validate the effectiveness of our correction pipeline, demonstrating how sparse real-world LiDAR data can systematically bolster the robustness of both MHE and MDE models and paving the way for scalable, low-cost, and globally applicable 3D mapping solutions.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 155-171"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145704894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TUM2TWIN: Introducing the large-scale multimodal urban digital twin benchmark dataset","authors":"Olaf Wysocki ,&nbsp;Benedikt Schwab ,&nbsp;Manoj Kumar Biswanath ,&nbsp;Michael Greza ,&nbsp;Qilin Zhang ,&nbsp;Jingwei Zhu ,&nbsp;Thomas Froech ,&nbsp;Medhini Heeramaglore ,&nbsp;Ihab Hijazi ,&nbsp;Khaoula Kanna ,&nbsp;Mathias Pechinger ,&nbsp;Zhaiyu Chen ,&nbsp;Yao Sun ,&nbsp;Alejandro Rueda Segura ,&nbsp;Ziyang Xu ,&nbsp;Omar AbdelGafar ,&nbsp;Mansour Mehranfar ,&nbsp;Chandan Yeshwanth ,&nbsp;Yueh-Cheng Liu ,&nbsp;Hadi Yazdi ,&nbsp;Boris Jutzi","doi":"10.1016/j.isprsjprs.2025.12.013","DOIUrl":"10.1016/j.isprsjprs.2025.12.013","url":null,"abstract":"<div><div>Urban Digital Twins (UDTs) have become essential for managing cities and integrating complex, heterogeneous data from diverse sources. Creating UDTs involves challenges at multiple process stages, including acquiring accurate 3D source data, reconstructing high-fidelity 3D models, maintaining models’ updates, and ensuring seamless interoperability to downstream tasks. Current datasets are usually limited to one part of the processing chain, hampering comprehensive Urban Digital Twin (UDT)s validation. To address these challenges, we introduce the first comprehensive multimodal Urban Digital Twin benchmark dataset: TUM2TWIN. This dataset includes georeferenced, semantically aligned 3D models and networks along with various terrestrial, mobile, aerial, and satellite observations boasting 32 data subsets over roughly 100,000 <span><math><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> and currently 767 GB of data. By ensuring georeferenced indoor–outdoor acquisition, high accuracy, and multimodal data integration, the benchmark supports robust analysis of sensors and the development of advanced reconstruction methods. Additionally, we explore downstream tasks demonstrating the potential of TUM2TWIN, including novel view synthesis of NeRF and Gaussian Splatting, solar potential analysis, point cloud semantic segmentation, and LoD3 building reconstruction. We are convinced this contribution lays a foundation for overcoming current limitations in UDT creation, fostering new research directions and practical solutions for smarter, data-driven urban environments. The project is available under: <span><span>https://tum2t.win</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 810-830"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SmartQSM: a novel quantitative structure model using sparse-convolution-based point cloud contraction for reconstruction and analysis of individual tree architecture","authors":"Jie Yang ,&nbsp;Huaiqing Zhang ,&nbsp;Jinyang Li ,&nbsp;Haoyue Yang ,&nbsp;Tian Gao ,&nbsp;Tingdong Yang ,&nbsp;Jiaxin Wang ,&nbsp;Xiaoli Zhang ,&nbsp;Ting Yun ,&nbsp;Yuxin Duanmu ,&nbsp;Sihan Chen ,&nbsp;Yukai Shi","doi":"10.1016/j.isprsjprs.2026.01.011","DOIUrl":"10.1016/j.isprsjprs.2026.01.011","url":null,"abstract":"<div><div>Tree architecture analysis is fundamental to forestry, but complex trees challenge the accuracy and efficiency of point-cloud-based reconstruction. Here, we present SmartQSM, a novel quantitative structure model designed for reconstructing individual trees and extracting their parameters using ground-based laser scanning data. The method achieves point cloud contraction and forms the thin structures required for skeletonization by iteratively applying a sparse-convolution-based residual U-shaped network (ResUNet) to predict point movement towards the medial axis. This process is integrated with techniques from previous studies to form a complete reconstruction pipeline. Following the organization and QSM-based quantification of 47 individual-scale, 26 organ-scale, and 8 plot-scale parameters, the proposed method provides comprehensive support for extracting these metrics using the input point cloud and its outputs, including the skeleton and mesh. The performance was verified using the two-period leaf-off LiDAR data of a natural coniferous and broad-leaved mixed forest plot (in Qingyuan county, Liaoning province, China), and 2 open forest datasets. The existing major QSMs was used for comparison. The inference network adopted a three-stage hierarchical spatial compression architecture, initiating with 8 input channels and predicting with multi-layer perceptron. The reconstruction was insensitive to remaining leaves and the model did not have apparent distortion. The processing speed is efficient, about 12,000 points per second. In terms of major architectural parameters, the <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> scores for trunk length, trunk volume, and bole height on the tested two period data of different tree species in the plot reached 0.97, 0.957, and 0.949, respectively, which were 0.043, 0.114, 0.029 higher than existing methods. the <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> scores for branch length, branching angle, and tip deflection angle remained around 0.95. The overestimation of stem volume or aboveground biomass has been alleviated. The high reconstruction quality, efficiency, rich parameters, and unique visual interaction capabilities of the proposed method offer a novel and practical solution for forestry research and broader domains. The implementation code is currently available at: <span><span>https://github.com/project-lightlin/SmartQSM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 712-739"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic learning for primitive-based building model reconstruction from point clouds","authors":"Zhixin Li,&nbsp;Jie Shan","doi":"10.1016/j.isprsjprs.2025.12.012","DOIUrl":"10.1016/j.isprsjprs.2025.12.012","url":null,"abstract":"<div><div>The rapid advancement of digital 3D environments has significantly increased the demand for geometrically accurate and semantically rich parametric building models. However, existing primitive- or model-based building reconstruction approaches often struggle with limited availability of labeled datasets and insufficient reconstruction accuracy. To address these challenges, we propose a novel learning-based method for building reconstruction from point clouds that leverages roof primitives and relies exclusively on synthetic data for supervision. Our approach begins with the generation of a large synthetic dataset comprising 100,000 buildings of varying scales based on a predefined library of 10 roof primitive classes. The synthetic point clouds are created by randomly sampling not only the interiors but also the edges and corners of the roof primitives. Two lightweight transformer-based neural networks are then trained to classify roof primitive classes and estimate their corresponding parameters. Compared to conventional learning-free fitting methods, our learning-based approach achieves higher parameter estimation accuracy and greater robustness when applied to six real-world point cloud datasets collected from drone, airborne, and spaceborne platforms. Notably, the synthetic learning approach reduces primitive parameter estimation errors from approximately 50% to 6% of the point ground spacing — demonstrating a distinctive advantage when trained effectively on synthetic data. Future work may explore generating synthetic data for irregular, complex buildings, expanding the library with additional roof primitive classes, and applying the proposed training strategy to such synthetic datasets.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 336-352"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A spectral index using generic global endmembers from Landsat multispectral data for mapping urban areas","authors":"Ruiyi Zhao ,&nbsp;Cai Cai ,&nbsp;Xinfan Cai ,&nbsp;Peijun Li","doi":"10.1016/j.isprsjprs.2025.12.025","DOIUrl":"10.1016/j.isprsjprs.2025.12.025","url":null,"abstract":"<div><div>Quantifying urban land cover from space is crucial for studying and understanding its spatial distribution and changes, as well as for assessing the impact of these changes on environmental and socio-economic dynamics worldwide. Owing to the diversity and spectral heterogeneity of urban reflectance, spectrally mixed pixels predominate in moderate resolution multispectral images. The standardized linear spectral mixture model from Landsat multispectral images, which represents the radiance measurements of mixed pixels as linear mixtures of generic global Substrate (S), Vegetation (V), and Dark Surfaces (D) endmember radiances, offers an effective method for characterizing urban reflectance. Based on the analysis of SVD endmember fractions of urban land and other land cover types, this study proposes a spectral index using Landsat global SVD endmembers, termed the Urban Index using Global Endmembers (GEUI), to highlight and map urban land. GEUI is evaluated through comparisons with five established spectral indices: including Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI), Biophysical Composition Index (BCI), Built-up Land Features Extraction Index (BLFEI), and Urban Composition Index (UCI), all of which rely on pure spectral signatures of urban pixels. Additionally, GEUI is compared to two deep learning methods in urban area mapping, i.e., two-dimensional convolutional neural network (2D CNN) and one-dimensional CNN (1D CNN). The results demonstrate that the proposed GEUI outperforms these comparative indices in qualitative evaluation, separability analysis, and urban land mapping, and also showed superior performance in urban land mapping compared to CNN methods. GEUI achieved overall accuracies ranging from 84.36% to 93.02% and F-scores between 84.80% and 92.64%, obtaining the highest accuracy in half of the study urban areas. Since S, V, and D endmembers used in GEUI are globally available, the proposed GEUI has the advantage of being applicable across diverse locations and times. Furthermore, GEUI can be readily extended to other broadband multispectral data, such as Sentinel-2 and MODIS. Therefore, the proposed GEUI provides an effective variable for mapping urban land and holds the potential<!--> <!-->for diverse<!--> <!-->urban applications.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 613-637"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering spatial process heterogeneity from graph-based deep spatial regression","authors":"Di Zhu ,&nbsp;Sheng Wang ,&nbsp;Peng Luo","doi":"10.1016/j.isprsjprs.2025.12.008","DOIUrl":"10.1016/j.isprsjprs.2025.12.008","url":null,"abstract":"<div><div>One can never specify the true statistical form of a complex spatial process. Model misspecification, such as linearity and additivity, will lead to fundamentally flawed interpretations in the estimated coefficients, particularly in empirical geographic studies involving a large number of observations and complex data generation processes. Motivated to learn representative patterns of spatial process heterogeneity, we propose a deep explainable spatial regression (XSR) framework based on graph convolutional neural networks (GCN), which bypasses the conventional parametric statistical assumptions in spatial regression modeling and can generate deep spatially varying coefficients that depict the heterogeneity structure of spatial processes. We introduce an analytical framework to (1) perform deep spatial regression modeling in multivariate cross-sectional scenarios, (2) reconstruct spatial heterogeneity patterns from the learned deep coefficients, and (3) explain the effectiveness of heterogeneity through a simple diagnostic test. Experiments on Greater Boston house prices modeling demonstrate better fitting performance over spatial regression baselines. The spatial patterns of deep local coefficients consistently exhibit stronger explanatory power than those derived from geographically weighted regression, indicating a better representation of the true spatial process heterogeneity uncovered by graph-based deep spatial regression.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 509-523"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}