Pub Date : 2026-01-05DOI: 10.1016/j.isprsjprs.2025.12.008
Di Zhu , Sheng Wang , Peng Luo
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.
{"title":"Uncovering spatial process heterogeneity from graph-based deep spatial regression","authors":"Di Zhu , Sheng Wang , 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":"2026-01-05","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}
Pub Date : 2026-01-05DOI: 10.1016/j.isprsjprs.2026.01.006
Jianping Li , Xinhang Xu , Zhongyuan Liu , Shenghai Yuan , Muqing Cao , Lihua Xie
LiDAR-based 3D perception and localization on unmanned aerial vehicles (UAVs) are fundamentally limited by the narrow field of view (FoV) of compact LiDAR sensors and the payload constraints that preclude multi-sensor configurations. Traditional motorized scanning systems with fixed-speed rotations lack scene awareness and task-level adaptability, leading to degraded odometry and mapping performance in complex, occluded environments. Inspired by the active sensing behavior of owls, we propose AEOS (Active Environment-aware Optimal Scanning), a biologically inspired and computationally efficient framework for adaptive LiDAR control in UAV-based LiDAR-Inertial Odometry (LIO). AEOS combines model predictive control (MPC) and reinforcement learning (RL) in a hybrid architecture: an analytical uncertainty model predicts future pose observability for exploitation, while a lightweight neural network learns an implicit cost map from panoramic depth representations to guide exploration. To support scalable training and generalization, we develop a point cloud-based simulation environment with real-world LiDAR maps across diverse scenes, enabling sim-to-real transfer. Extensive experiments in both simulation and real-world environments demonstrate that AEOS significantly improves odometry accuracy compared to fixed-rate, optimization-only, and fully learned baselines, while maintaining real-time performance under onboard computational constraints. The project page can be found at https://kafeiyin00.github.io/AEOS/.
{"title":"AEOS: Active Environment-aware Optimal Scanning Control for UAV LiDAR-Inertial Odometry in Complex Scenes","authors":"Jianping Li , Xinhang Xu , Zhongyuan Liu , Shenghai Yuan , Muqing Cao , Lihua Xie","doi":"10.1016/j.isprsjprs.2026.01.006","DOIUrl":"10.1016/j.isprsjprs.2026.01.006","url":null,"abstract":"<div><div>LiDAR-based 3D perception and localization on unmanned aerial vehicles (UAVs) are fundamentally limited by the narrow field of view (FoV) of compact LiDAR sensors and the payload constraints that preclude multi-sensor configurations. Traditional motorized scanning systems with fixed-speed rotations lack scene awareness and task-level adaptability, leading to degraded odometry and mapping performance in complex, occluded environments. Inspired by the active sensing behavior of owls, we propose AEOS (Active Environment-aware Optimal Scanning), a biologically inspired and computationally efficient framework for adaptive LiDAR control in UAV-based LiDAR-Inertial Odometry (LIO). AEOS combines model predictive control (MPC) and reinforcement learning (RL) in a hybrid architecture: an analytical uncertainty model predicts future pose observability for exploitation, while a lightweight neural network learns an implicit cost map from panoramic depth representations to guide exploration. To support scalable training and generalization, we develop a point cloud-based simulation environment with real-world LiDAR maps across diverse scenes, enabling sim-to-real transfer. Extensive experiments in both simulation and real-world environments demonstrate that AEOS significantly improves odometry accuracy compared to fixed-rate, optimization-only, and fully learned baselines, while maintaining real-time performance under onboard computational constraints. The project page can be found at <span><span>https://kafeiyin00.github.io/AEOS/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 476-491"},"PeriodicalIF":12.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902908","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}
Pub Date : 2025-12-30DOI: 10.1016/j.isprsjprs.2025.12.014
Haoxuan Sun , Taoyang Wang , Qian Cheng , Jiaxuan Huang
Inaccurate or noisy labels have a huge impact on the training of deep learning models. To date, few studies have focused on the label error problem in satellite image stereo matching. In this paper, we analyzed and found the two open datasets US3D and WHU-Stereo contain label errors that cannot be overlooked. A new task is extremely necessary: learning from inaccurate labels with neural networks. Our motivation is to deal with label errors at the training level. A robust stereo matching training framework (RSMT) with geometric correction, clean pixel selection, and loss weighting modules is proposed. In addition, we also propose a dataset correcting method and provide two inaccurate-label stereo matching datasets US3D(E) and WHU(E) based on raw datasets. The framework can be applied to common stereo methods like IGEV-Stereo and ACVNet to achieve SOTA performance on the corrected datasets. To the best of our knowledge, the study is the first systemic inaccurate-label learning framework dedicated to stereo matching. Datasets are available at https://github.com/endu111/robust-satellite-image-stereo-matching.
{"title":"RSMT: Robust stereo matching training with geometric correction, clean pixel selection and loss weighting","authors":"Haoxuan Sun , Taoyang Wang , Qian Cheng , Jiaxuan Huang","doi":"10.1016/j.isprsjprs.2025.12.014","DOIUrl":"10.1016/j.isprsjprs.2025.12.014","url":null,"abstract":"<div><div>Inaccurate or noisy labels have a huge impact on the training of deep learning models. To date, few studies have focused on the label error problem in satellite image stereo matching. In this paper, we analyzed and found the two open datasets US3D and WHU-Stereo contain label errors that cannot be overlooked. A new task is extremely necessary: learning from inaccurate labels with neural networks. Our motivation is to deal with label errors at the training level. A robust stereo matching training framework (RSMT) with geometric correction, clean pixel selection, and loss weighting modules is proposed. In addition, we also propose a dataset correcting method and provide two inaccurate-label stereo matching datasets US3D(E) and WHU(E) based on raw datasets. The framework can be applied to common stereo methods like IGEV-Stereo and ACVNet to achieve SOTA performance on the corrected datasets. To the best of our knowledge, the study is the first systemic inaccurate-label learning framework dedicated to stereo matching. Datasets are available at <span><span>https://github.com/endu111/robust-satellite-image-stereo-matching</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 421-436"},"PeriodicalIF":12.2,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884095","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}
Pub Date : 2025-12-30DOI: 10.1016/j.isprsjprs.2025.12.017
Xiaosong Feng , Lianhuan Wei , Xu Ren , Chaoying Zhao , Meng Ao , Yian Wang , Jiayin Luo , Christian Bignami
Phase unwrapping (PU) is a key step in synthetic aperture radar interferometry (InSAR) techniques, as its accuracy directly determines the precision of deformation estimation. Despite the widespread use of InSAR, many existing PU techniques, such as minimum cost flow (MCF), Snaphu, and recently developed deep-learning models, struggle to maintain high accuracy when faced with large deformation gradients. To address this issue, this study proposes a phase gradient rate constrained minimum cost flow (PGR-MCF) method. It uses the phase gradient rate (PGR) calculated from time series differential interferograms through stacking and fusion, to constrain weighting of the arcs during MCF unwrapping process. By optimizing the unwrapping path to prioritize low-gradient areas, the PGR-MCF method significantly improves unwrapping accuracy in high-gradient regions. In simulated experiments, the PGR-MCF method correctly unwrapped 99.5 % of the pixels in large deformation zones. In the application to Guobu landslide, the PGR-MCF method reduces the root mean square errors (RMSEs) between InSAR and global navigation satellite system (GNSS) deformation time series by more than 69 %, compared to other tested methods. This method does not require external datasets or prior models, ensuring its broad applicability. Additionally, it reliably unwraps interferograms with large spatiotemporal baselines, thus increasing the number of reliable unwrapped interferograms for time-series deformation inversion and improving deformation monitoring accuracy. Moreover, it has been proven to be an effective PU method for deformation estimation in regions with large deformation gradients.
{"title":"Phase gradient rate constrained minimum cost flow: A robust unwrapping method for landslides with large deformation gradients","authors":"Xiaosong Feng , Lianhuan Wei , Xu Ren , Chaoying Zhao , Meng Ao , Yian Wang , Jiayin Luo , Christian Bignami","doi":"10.1016/j.isprsjprs.2025.12.017","DOIUrl":"10.1016/j.isprsjprs.2025.12.017","url":null,"abstract":"<div><div>Phase unwrapping (PU) is a key step in synthetic aperture radar interferometry (InSAR) techniques, as its accuracy directly determines the precision of deformation estimation. Despite the widespread use of InSAR, many existing PU techniques, such as minimum cost flow (MCF), Snaphu, and recently developed deep-learning models, struggle to maintain high accuracy when faced with large deformation gradients. To address this issue, this study proposes a phase gradient rate constrained minimum cost flow (PGR-MCF) method. It uses the phase gradient rate (PGR) calculated from time series differential interferograms through stacking and fusion, to constrain weighting of the arcs during MCF unwrapping process. By optimizing the unwrapping path to prioritize low-gradient areas, the PGR-MCF method significantly improves unwrapping accuracy in high-gradient regions. In simulated experiments, the PGR-MCF method correctly unwrapped 99.5 % of the pixels in large deformation zones. In the application to Guobu landslide, the PGR-MCF method reduces the root mean square errors (RMSEs) between InSAR and global navigation satellite system (GNSS) deformation time series by more than 69 %, compared to other tested methods. This method does not require external datasets or prior models, ensuring its broad applicability. Additionally, it reliably unwraps interferograms with large spatiotemporal baselines, thus increasing the number of reliable unwrapped interferograms for time-series deformation inversion and improving deformation monitoring accuracy. Moreover, it has been proven to be an effective PU method for deformation estimation in regions with large deformation gradients.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 437-456"},"PeriodicalIF":12.2,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884002","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}
Pub Date : 2025-12-29DOI: 10.1016/j.isprsjprs.2025.12.024
Biao Xiong , Bohan Wang , Yiyi Liu , Liangliang Wang , Yanchao Yang , Liang Zhou , Qiegen Liu
Building Information Models (BIMs) provide structured, parametric representations that are fundamental for simulation, facility management, and digital twin applications. However, reconstructing BIMs from terrestrial LiDAR scans remains challenging due to clutter, occlusions, and the geometric complexity of roof structures. This paper presents a roof-aware scan-to-BIM pipeline tailored for residential buildings, which processes indoor LiDAR data through four geometric abstractions, raw points, superpoints, triangle meshes, and volumetric polyhedra, each represented by task-specific graphs. The pipeline integrates three modules: LGNet for semantic segmentation, QTNet for floor plan reconstruction, and PPO for roof–floor fusion. It demonstrates strong cross-dataset generalization, being trained on Structured3D and fine-tuned on the real-world WHUTS dataset. The method produces watertight, Revit-compatible BIMs with an average surface deviation of 9 mm RMS on WHUTS scenes featuring slanted roofs. Compared with state-of-the-art scan-to-BIM and floor plan reconstruction methods, the proposed approach achieves higher geometric accuracy on scenes with slanted roofs, reducing surface reconstruction error by over 12–18% and improving layout reconstruction F1-scores by up to 6–8%. The proposed framework provides a robust, accurate, and fully automated solution for roof-aware BIM reconstruction of residential buildings from terrestrial LiDAR data, offering comprehensive support for slanted roof modeling. The source code and datasets are publicly available at https://github.com/Wangbohan-x/roof-aware-scan2bim.git.
{"title":"Roof-aware indoor BIM reconstruction from LiDAR via graph-attention for residential buildings","authors":"Biao Xiong , Bohan Wang , Yiyi Liu , Liangliang Wang , Yanchao Yang , Liang Zhou , Qiegen Liu","doi":"10.1016/j.isprsjprs.2025.12.024","DOIUrl":"10.1016/j.isprsjprs.2025.12.024","url":null,"abstract":"<div><div>Building Information Models (BIMs) provide structured, parametric representations that are fundamental for simulation, facility management, and digital twin applications. However, reconstructing BIMs from terrestrial LiDAR scans remains challenging due to clutter, occlusions, and the geometric complexity of roof structures. This paper presents a <strong>roof-aware scan-to-BIM pipeline</strong> tailored for residential buildings, which processes indoor LiDAR data through four geometric abstractions, raw points, superpoints, triangle meshes, and volumetric polyhedra, each represented by task-specific graphs. The pipeline integrates three modules: <strong>LGNet</strong> for semantic segmentation, <strong>QTNet</strong> for floor plan reconstruction, and <strong>PPO</strong> for roof–floor fusion. It demonstrates strong cross-dataset generalization, being trained on Structured3D and fine-tuned on the real-world WHUTS dataset. The method produces watertight, Revit-compatible BIMs with an average surface deviation of <strong>9 mm RMS</strong> on WHUTS scenes featuring slanted roofs. Compared with state-of-the-art scan-to-BIM and floor plan reconstruction methods, the proposed approach achieves higher geometric accuracy on scenes with slanted roofs, reducing surface reconstruction error by over <strong>12–18%</strong> and improving layout reconstruction F1-scores by up to <strong>6–8%</strong>. The proposed framework provides a robust, accurate, and fully automated solution for roof-aware BIM reconstruction of residential buildings from terrestrial LiDAR data, offering comprehensive support for slanted roof modeling. The source code and datasets are publicly available at <span><span>https://github.com/Wangbohan-x/roof-aware-scan2bim.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 408-420"},"PeriodicalIF":12.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884003","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}
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.
{"title":"TFRSUB: A terrain-feature retention and spatial uniformity balancing method for simplifying LiDAR ground point clouds","authors":"Chuanfa Chen, Ziming Yang, Hongming Pan, Yanyan Li, 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":"2025-12-29","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}
Pub Date : 2025-12-28DOI: 10.1016/j.isprsjprs.2025.12.010
Yongchang Ye , Xiaoyang Zhang , Yu Shen , Khuong H. Tran , Shuai Gao , Yuxia Liu , Shuai An
Land surface phenology (LSP) has been widely derived from observations of different satellite sensors, including the Advanced Very High-Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). However, the consistency of long-term LSP products is a major concern because the time series data quality may vary greatly, particularly due to temporal gaps caused by cloud contamination, instrumental degradations (e.g., orbital drift), and other factors. Therefore, this study investigated the reconstruction of the high-quality time series of vegetation indices using a Spatiotemporal Shape-Matching Model (SSMM) and the reduction of the temporal gap impacts on LSP detections globally at the climate modeling grid (0.05°). Specifically, we first generated the climatology of a 3-day two-band Enhanced Vegetation Index (EVI2) using the MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset from 2003 to 2022. The temporal climatology EVI2 was used in the SSMM algorithm to fuse the 3-day time series of EVI2 data derived separately from five different surface reflectance products: AVHRR reflectance data (1981–2019), MODIS standard surface reflectance (SSR) and NBAR data (2000–2023), and VIIRS SSR and NBAR data (2012–2023). These five sets of EVI2 time series were further applied to detect LSP metrics. The result indicates that the coefficient of determination (R2) increased by up to 0.2 among the fused EVI2 time series from AVHRR, MODIS SSR, VIIRS SSR, MODIS NBAR, and VIIRS NBAR compared to that among the raw EVI2 time series. Although the AVHRR EVI2 dataset was more consistent with MODIS SSR or VIIRS SSR observations than with MODIS NBAR or VIIRS NBAR datasets, the highest R2 was found between MODIS and VIIRS NBAR EVI2, especially between their fused EVI2 time series. Consequently, the mean absolute difference (MAD) of LSP metrics was reduced by one to three days in comparing fused EVI2 with raw EVI2 time series between two different sensors. Overall, the highest LSP consistency was found between fused MODIS NBAR and fused VIIRS NBAR, which was followed by LSP detections between raw MODIS NBAR and raw VIIRS NBAR, fused MODIS SSR and fused AVHRR, raw MODIS SSR and raw AVHRR, fused MODIS NBAR and fused AVHRR, and raw MODIS NBAR and raw AVHRR. The result suggests that long-term LSP products from 1980 forward should be generated using the fused EVI2 time series from AVHRR, MODIS SSR, and VIIRS SSR, while the product from 2000 forward should be produced using the fused time series from MODIS NBAR and VIIRS NBAR observations.
{"title":"Improvement of the consistency among long-term global land surface phenology products derived from AVHRR, MODIS, and VIIRS observations","authors":"Yongchang Ye , Xiaoyang Zhang , Yu Shen , Khuong H. Tran , Shuai Gao , Yuxia Liu , Shuai An","doi":"10.1016/j.isprsjprs.2025.12.010","DOIUrl":"10.1016/j.isprsjprs.2025.12.010","url":null,"abstract":"<div><div>Land surface phenology (LSP) has been widely derived from observations of different satellite sensors, including the Advanced Very High-Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). However, the consistency of long-term LSP products is a major concern because the time series data quality may vary greatly, particularly due to temporal gaps caused by cloud contamination, instrumental degradations (e.g., orbital drift), and other factors. Therefore, this study investigated the reconstruction of the high-quality time series of vegetation indices using a Spatiotemporal Shape-Matching Model (SSMM) and the reduction of the temporal gap impacts on LSP detections globally at the climate modeling grid (0.05°). Specifically, we first generated the climatology of a 3-day two-band Enhanced Vegetation Index (EVI2) using the MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset from 2003 to 2022. The temporal climatology EVI2 was used in the SSMM algorithm to fuse the 3-day time series of EVI2 data derived separately from five different surface reflectance products: AVHRR reflectance data (1981–2019), MODIS standard surface reflectance (SSR) and NBAR data (2000–2023), and VIIRS SSR and NBAR data (2012–2023). These five sets of EVI2 time series were further applied to detect LSP metrics. The result indicates that the coefficient of determination (R<sup>2</sup>) increased by up to 0.2 among the fused EVI2 time series from AVHRR, MODIS SSR, VIIRS SSR, MODIS NBAR, and VIIRS NBAR compared to that among the raw EVI2 time series. Although the AVHRR EVI2 dataset was more consistent with MODIS SSR or VIIRS SSR observations than with MODIS NBAR or VIIRS NBAR datasets, the highest R<sup>2</sup> was found between MODIS and VIIRS NBAR EVI2, especially between their fused EVI2 time series. Consequently, the mean absolute difference (MAD) of LSP metrics was reduced by one to three days in comparing fused EVI2 with raw EVI2 time series between two different sensors. Overall, the highest LSP consistency was found between fused MODIS NBAR and fused VIIRS NBAR, which was followed by LSP detections between raw MODIS NBAR and raw VIIRS NBAR, fused MODIS SSR and fused AVHRR, raw MODIS SSR and raw AVHRR, fused MODIS NBAR and fused AVHRR, and raw MODIS NBAR and raw AVHRR. The result suggests that long-term LSP products from 1980 forward should be generated using the fused EVI2 time series from AVHRR, MODIS SSR, and VIIRS SSR, while the product from 2000 forward should be produced using the fused time series from MODIS NBAR and VIIRS NBAR observations.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 369-388"},"PeriodicalIF":12.2,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845309","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}
Pub Date : 2025-12-27DOI: 10.1016/j.isprsjprs.2025.12.012
Zhixin Li, Jie Shan
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.
{"title":"Synthetic learning for primitive-based building model reconstruction from point clouds","authors":"Zhixin Li, 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":"2025-12-27","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}
Pub Date : 2025-12-27DOI: 10.1016/j.isprsjprs.2025.12.020
Sijia Li , Xiangming Xiao , Ge Liu , Zhidan Wen , Chong Fang , Yingxin Shang , Hui Tao , Lele Zhang , Yong Li , Qiang Wang , Haoyun Zhou , Kaishan Song
Chlorophyll-a (Chl-a) concentration in lake water is an indicator of its trophic state and water quality. Although many anthropogenic drivers have increased lake trophic state, leading to severe negative consequences, many environmental policies and management were done towards improving water quality. Here, we analyzed the Chl-a concentration maps derived from Landsat images during 1990–2020 and assessed the changes of trophic states over > 1138 large lakes (surface water areas larger than 1 km2), which together account for 60.1 % of total lake areas in China. The average Chl-a concentration over all the lakes declined more rapidly after 2010. Lake trophic states dropped in 180 light-eutrophic lakes (10 < Chl-a < 26, μg L-1), but rose in 14.29 % oligotrophic lakes (Chl-a < 1.6, μg L-1). We observed pronounced decreases in averaged Chl-a levels since 2010, accompanied by increased identified mesotrophic and oligotrophic states; however, this shift exhibited considerable variability across different provinces. The climate change (i.e., increased temperature, decreased precipitation) often lead to increases in Chl-a concentration, but the implementation of national environmental policies lead to decreases in lake Chl-a levels since 2010, particular in those provinces with large economy.
{"title":"Satellite images reveal reduced lake chlorophyll concentration and eutrophication in China","authors":"Sijia Li , Xiangming Xiao , Ge Liu , Zhidan Wen , Chong Fang , Yingxin Shang , Hui Tao , Lele Zhang , Yong Li , Qiang Wang , Haoyun Zhou , Kaishan Song","doi":"10.1016/j.isprsjprs.2025.12.020","DOIUrl":"10.1016/j.isprsjprs.2025.12.020","url":null,"abstract":"<div><div>Chlorophyll-a (Chl-a) concentration in lake water is an indicator of its trophic state and water quality. Although many anthropogenic drivers have increased lake trophic state, leading to severe negative consequences, many environmental policies and management were done towards improving water quality. Here, we analyzed the Chl-a concentration maps derived from Landsat images during 1990–2020 and assessed the changes of trophic states over > 1138 large lakes (surface water areas larger than 1 km<sup>2</sup>), which together account for 60.1 % of total lake areas in China. The average Chl-a concentration over all the lakes declined more rapidly after 2010. Lake trophic states dropped in 180 light-eutrophic lakes (10 < Chl-a < 26, μg L<sup>-1</sup>), but rose in 14.29 % oligotrophic lakes (Chl-a < 1.6, μg L<sup>-1</sup>). We observed pronounced decreases in averaged Chl-a levels since 2010, accompanied by increased identified mesotrophic and oligotrophic states; however, this shift exhibited considerable variability across different provinces. The climate change (i.e., increased temperature, decreased precipitation) often lead to increases in Chl-a concentration, but the implementation of national environmental policies lead to decreases in lake Chl-a levels since 2010, particular in those provinces with large economy.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 353-368"},"PeriodicalIF":12.2,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845310","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}
Pub Date : 2025-12-26DOI: 10.1016/j.isprsjprs.2025.12.009
Darshana Priyasad , Tharindu Fernando , Maryam Haghighat , Harshala Gammulle , Roberto Del Prete , Clinton Fookes
Multi-band optical sensors onboard modern Earth observation satellites capture complementary spectral responses across varying spatial and spectral resolutions. To effectively fuse this information for downstream applications, accurate band co-registration is critical. However, for real-time processing, such registration must be performed onboard, where sensor distortions, platform-induced motion, and spectral disparities introduce significant challenges. Traditional feature matching algorithms struggle to cope with these variations or are often too computationally intensive for the constrained hardware typically found on small satellites. As a result, real-time onboard multimodal fusion has remained largely impractical in operational settings. With the emergence of next-generation satellites equipped with AI-enabled onboard processing, such as Australia’s Kanyini mission, there is now an opportunity to overcome these limitations. In this work, we introduce a deep learning-based, lightweight band registration framework specifically designed for real-time onboard deployment. Our approach features a band-independent teacher network that jointly leverages adversarial learning and supervised regression to estimate affine registration parameters across spectral bands. To meet hardware constraints, we employ a two-stage knowledge distillation strategy that produces a compact yet accurate student model. Experimental results demonstrate that our method delivers robust and efficient registration performance, enabling real-time spectral alignment and significantly enhancing the potential for onboard multimodal data fusion in Earth observation missions.
{"title":"Two-stage offline knowledge distillation for onboard registration of multispectral satellite images","authors":"Darshana Priyasad , Tharindu Fernando , Maryam Haghighat , Harshala Gammulle , Roberto Del Prete , Clinton Fookes","doi":"10.1016/j.isprsjprs.2025.12.009","DOIUrl":"10.1016/j.isprsjprs.2025.12.009","url":null,"abstract":"<div><div>Multi-band optical sensors onboard modern Earth observation satellites capture complementary spectral responses across varying spatial and spectral resolutions. To effectively fuse this information for downstream applications, accurate band co-registration is critical. However, for real-time processing, such registration must be performed onboard, where sensor distortions, platform-induced motion, and spectral disparities introduce significant challenges. Traditional feature matching algorithms struggle to cope with these variations or are often too computationally intensive for the constrained hardware typically found on small satellites. As a result, real-time onboard multimodal fusion has remained largely impractical in operational settings. With the emergence of next-generation satellites equipped with AI-enabled onboard processing, such as Australia’s Kanyini mission, there is now an opportunity to overcome these limitations. In this work, we introduce a deep learning-based, lightweight band registration framework specifically designed for real-time onboard deployment. Our approach features a band-independent teacher network that jointly leverages adversarial learning and supervised regression to estimate affine registration parameters across spectral bands. To meet hardware constraints, we employ a two-stage knowledge distillation strategy that produces a compact yet accurate student model. Experimental results demonstrate that our method delivers robust and efficient registration performance, enabling real-time spectral alignment and significantly enhancing the potential for onboard multimodal data fusion in Earth observation missions.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 319-335"},"PeriodicalIF":12.2,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840693","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}
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