ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Geo-scenes dissecting urban fabric: Understanding and recognition combining AI, remotely sensed data and multimodal spatial semantics","authors":"Hanqing Bao ,&nbsp;Lanyue Zhou ,&nbsp;Lukas W. Lehnert","doi":"10.1016/j.isprsjprs.2025.10.011","DOIUrl":"10.1016/j.isprsjprs.2025.10.011","url":null,"abstract":"<div><div>Urban fabric represents the intersection of spatial structure and social function. Analyzing its geographic components, functional semantics, and interactive relationships enables a deeper understanding of the formation and evolution of urban geo-scenes. Urban geo-scenes (UGS), as the fundamental units of urban systems, play a vital role in balancing and optimizing spatial layout, while enhancing urban resilience and vitality. Although multimodal spatial data are widely used to describe UGS, conventional approaches that rely solely on visual or social features are insufficient when addressing the complexity of modern urban systems. The spatial relationships and distributional patterns among urban elements are equally crucial for capturing the full semantic structure of urban geo-scenes. In parallel, most deep learning models still face limitations in effectively mining and fusing such diverse information. To address these challenges, we propose a multimodal deep learning framework for UGS recognition. Guided by the concepts of urban fabric and spatial co-location patterns, our method dissects the internal structure of geo-scenes and constructs a bottom-up urban fabric graph model to capture spatial semantics among geographic entities. Specifically, we employ a customized SE-DenseNet branch to extract deep physical and visual features from high-resolution satellite imagery, along with social semantic information from auxiliary data (e.g., POIs, building footprint coverage). A semantic fusion module is further introduced to enable collaborative interaction among multi-modal and multi-scale features. The framework was validated across four Chinese cities with varying sizes, economic levels, and cultural contexts. The proposed method achieved an overall accuracy of approximately 90%, outperforming existing state-of-the-art multimodal approaches. Moreover, ablation studies conducted in three cities of different scales confirm the critical role of urban fabric in UGS recognition. Our results demonstrate that the joint modeling of visual appearance, functional attributes, and spatial semantics offers a novel and more comprehensive understanding of urban geo-scenes.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"230 ","pages":"Pages 716-737"},"PeriodicalIF":12.2,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321306","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":"Meta Feature Disentanglement under continuous-valued domain modeling for generalizable remote sensing image segmentation on unseen domains","authors":"Chenbin Liang ,&nbsp;Xiaoping Zhang ,&nbsp;Wenlin Fu ,&nbsp;Weibin Li ,&nbsp;Yunyun Dong","doi":"10.1016/j.isprsjprs.2025.09.029","DOIUrl":"10.1016/j.isprsjprs.2025.09.029","url":null,"abstract":"<div><div>As a long-standing challenge, the generalization ability of segmentation models has invoked enormous research on domain-agnostic learning, but current methods tend to be invalid in remote sensing. On the one hand, their common assumption that domains can be represented as discrete labels holds with difficulty in remote sensing, where domain shifts arise from dynamic and continuous changes. On the other hand, they struggle to perform well on unseen domains in remote sensing image segmentation tasks, where gaining diversity-sufficient and semantic-effective training distributions remains a significant challenge. To address these obstacles, this paper develops a novel domain generalization (DG) method, termed Meta Feature Disentanglement (MetaFD), for remote sensing image segmentation. To circumvent the inherent issue of discrete-valued domain modeling, MetaFD outlines domains in remote sensing with the continuous-valued space modeled by a variational autoencoder (VAE) and performs domain-label-free feature disentanglement aided by vector decomposition and semantic guidance. And to enhance generalization on unseen domains, MetaFD expands training distributions under the meta-learning framework by using the VAE to directionally randomize domain-specific variations, which can generate novel domains with vast diversity but no severe semantic distortions, and employs the generated data to maintain disentanglement consistency and design more realistic meta-episodes. Multiple public datasets are organized to construct three DG benchmark datasets for experimental studies. Extensive experimental results demonstrate that MetaFD significantly outperforms other state-of-the-art methods in remote sensing image segmentation tasks. The code is available at <span><span>https://github.com/LCB1970/MetaFD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"230 ","pages":"Pages 738-753"},"PeriodicalIF":12.2,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321333","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":"PMTFIM: Integrating machine learning with nutrient balance theory to estimate multi-stage paddy fertilization information at field scale over large regions","authors":"Housheng Wang ,&nbsp;Xiang Gao ,&nbsp;Wei Jiang ,&nbsp;Xuerong Lang ,&nbsp;Xian Hu ,&nbsp;Meihua Qiu ,&nbsp;Qiankun Guo ,&nbsp;Yonghong Liang ,&nbsp;Xuelei Wang ,&nbsp;Yue Mu ,&nbsp;Rui Ren ,&nbsp;Ganghua Li ,&nbsp;Hengbiao Zheng ,&nbsp;Yanfeng Ding ,&nbsp;Xiaosan Jiang","doi":"10.1016/j.isprsjprs.2025.10.006","DOIUrl":"10.1016/j.isprsjprs.2025.10.006","url":null,"abstract":"<div><div>Accurate estimation of multi-stage field-level paddy fertilization information over large regions provides crucial support for optimizing fertilization management and evaluating greenhouse gases emission and agricultural non-point source pollution risks. Most existing models have utilized survey data from the government statistical agencies or international databases such as FAOSTAT, to estimate annual fertilization information, ignoring the different phenological growth stages of fertilization during the growing season. Although some studies have used LAI to estimate multi-stage fertilization information based on remote sensing, they utilized statistical models, leading to significant uncertainties. In this study, we present a precise tracing multi-stage paddy fertilization information model (PMTFIM), a novel remote sensing-driven framework that integrates Gaussian Process Regression-based LAI time series, phenological modeling, and machine learning coupled with nutrient balance theory to estimate multi-stage paddy fertilization information at the field scale across fragmented agricultural regions. Firstly, we obtained a daily 10-m LAI dataset by using the Gaussian Process Regression method and then integrated a double logistic function to generate training phenological data. Therefore, we estimated the fertilization dates based on rice phenology by coupling the optimal machine learning models. The fertilization amounts models were developed by using optimal machine learning, which accounts for interactions between fertilization, meteorological conditions, soil properties, and LAI dynamics based on nutrient balance. We determined that the random forest model is the optimal model among multiple machine learning models. The results demonstrated that PMTFIM captured the heterogeneity in fertilization information in fragmented paddy fields. The overall prediction accuracy based on training and test datasets (dates: R² &gt; 0.95, RMSE &lt; 5 days; amounts: R² &gt; 0.80, RMSE &lt; 14 kg/ha) has improved compared to existing statistical models. The PMTFIM achieved reliable accuracy at multiple growth stages with R² ranging 0.53–0.80 for fertilization dates and amounts at field scale and in independent sub-regions evaluated on test dataset, while maintaining high overall accuracy across the entire growing season, with R² of 0.80–0.96. Our proposed method has great potential for estimating multi-stage field-level crops fertilization information over large regions, especially in areas with fragmented fields.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"230 ","pages":"Pages 693-715"},"PeriodicalIF":12.2,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321331","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":"Generalized incremental image mosaicking with a coarse-to-fine framework via graph cuts","authors":"Yongjun Zhang ,&nbsp;Peiqi Chen ,&nbsp;Haoyu Guo ,&nbsp;Xinyi Liu ,&nbsp;Yi Wan","doi":"10.1016/j.isprsjprs.2025.09.030","DOIUrl":"10.1016/j.isprsjprs.2025.09.030","url":null,"abstract":"<div><div>Image mosaicking aims to expand spatial coverage by integrating multiple Digital Orthophoto Maps (DOMs) into a unified whole, playing a crucial role in large-scale surface state observation. Optimal seamline detection is a critical process that minimizes intensity differences along effective boundaries, thereby ensuring seamless mosaicking. Recent research has primarily focused on multi-frame joint methods that generate an initial seamline network, followed by the refinement of individual seamlines. However, the simultaneous preparation of all images is not always guaranteed due to the inherent spatial and temporal attributes of the imagery. In contrast, existing frame-to-frame methods perform incremental mosaicking by solely considering simple overlapping relationships within image pairs, without adequately addressing the complexities posed by multi-source images that differ in resolution, size, or topology relative to historical results. Meanwhile, efficiency remains a significant concern, particularly for large-scale and latency-sensitive applications. To address these challenges in a unified manner, we propose an incremental image mosaicking framework capable of processing generalized inputs while effectively bridging the connections between historical and newly acquired imagery. Furthermore, our approach incorporates a graph-cut-based seamline detection method in a coarse-to-fine manner, providing high scalability and adaptability to varying runtime demands through controllable processing granularity. Extensive experiments demonstrate that the seamlines detected by our method exhibit higher quality compared to state-of-the-art commercial software. Moreover, the processing time for aerial images can reach speeds as fast as 2–3 s per task, meeting the requirements for real-time onboard processing. The software is available at <span><span>https://github.com/pq-chen/GIIM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"230 ","pages":"Pages 661-674"},"PeriodicalIF":12.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269126","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":"LVIMOT: Accurate and robust LiDAR-visual-inertial localization and multi-object tracking in dynamic environments via tightly coupled integration","authors":"Shaoquan Feng,&nbsp;Xingxing Li,&nbsp;Zhuohao Yan,&nbsp;Yuxuan Zhou,&nbsp;Chunxi Xia","doi":"10.1016/j.isprsjprs.2025.10.003","DOIUrl":"10.1016/j.isprsjprs.2025.10.003","url":null,"abstract":"<div><div>Most existing LiDAR-visual-inertial (LVI) localization systems typically assume a static environment and often neglect the valuable dynamic object information, which limits localization accuracy and scene understanding in dynamic environments. Meanwhile, precise tracking of surrounding objects is essential for applications such as autonomous driving, augmented reality (AR), and virtual reality (VR). To this end, we propose LVIMOT, a tightly coupled LVI localization and multi-object tracking (MOT) system based on factor graph optimization, capable of jointly estimating the trajectories of the ego-vehicle and surrounding objects. In the proposed method, tracked objects are represented by 2D/3D bounding boxes and continuously associated by combining LiDAR and visual detections with multimodal feature cues. Building upon this, a binary hypothesis testing method for motion status classification is employed to identify object motion status by fusing motion and appearance information. Subsequently, the measurements associated with dynamic objects are fully exploited to construct object-related factors, which are jointly optimized with static features and IMU pre-integration factors within a unified factor graph to refine the trajectories of both the ego-vehicle and tracked objects. Extensive experiments on highly dynamic sequences from the KITTI and nuScenes datasets demonstrate that LVIMOT achieves state-of-the-art performance, with an HOTA of 80.37 for MOT, and the lowest mean translational and rotational absolute trajectory errors (ATE) for self-localization on KITTI (0.36 m, 0.02 rad) and nuScenes (0.13 m, 0.02 rad). The results confirm the effectiveness of integrating object-aware motion constraints and multimodal fusion in improving localization robustness and precision. The source code will be available at <span><span>https://github.com/shqfeng/LVIMOT.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"230 ","pages":"Pages 675-692"},"PeriodicalIF":12.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269125","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":"Intelligent mapping paradigm to overcome systematic bias in remote sensing SOC estimation: A case study of the black soil region in China and the United States","authors":"Chao Wang ,&nbsp;Chong Luo ,&nbsp;Xiangtian Meng ,&nbsp;Changkun Wang ,&nbsp;Huanjun Liu","doi":"10.1016/j.isprsjprs.2025.10.002","DOIUrl":"10.1016/j.isprsjprs.2025.10.002","url":null,"abstract":"<div><div>Soil organic carbon (SOC) is a crucial indicator for maintaining soil fertility and regulating carbon balance in black soil regions. However, its strong spatial heterogeneity and the limited capacity of remote sensing feature extraction often lead to systematic mapping errors, typically manifested as the underestimation of high values and overestimation of low values. To address this issue, we propose an SOC mapping framework that integrates prior geographic knowledge with deep learning, and develop a GMM-AG-CNNLSTM model incorporating fuzzy clustering and spatiotemporal feature extraction. The framework was applied to typical black soil regions in Northeast China and North America. A total of 2,616 surface SOC samples (0–20 cm) were compiled to build a multi-source spatiotemporal feature set. The approach first employs a Gaussian mixture model (GMM) to partition SOC levels and mitigate prediction bias caused by spatial heterogeneity. Subsequently, a weighted attention mechanism, convolutional neural networks (CNN), and long short-term memory (LSTM) networks are combined to achieve deep spatiotemporal feature fusion and generate SOC distribution maps at a 30 m resolution. Results demonstrate that the GMM-AG-CNNLSTM model achieved prediction accuracies of R² = 0.73/RMSE = 5.42 g/kg in Northeast China and R² = 0.70/RMSE = 5.89 g/kg in North America, outperforming random forest and conventional deep learning models, with greater stability in both high- and low-SOC regions. Spatial analysis further revealed that SOC in Northeast China exhibited higher mean values and a larger proportion of high-value areas compared with North America, though with a wider distribution of low-value areas. This study presents a high-accuracy SOC remote sensing mapping method that can provide valuable support for carbon sequestration assessment and degradation monitoring in black soil regions.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"230 ","pages":"Pages 644-660"},"PeriodicalIF":12.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268645","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":"Multi-source geo-localization in urban built environments for crowd-sourced images by contrastive learning","authors":"Qianbao Hou ,&nbsp;Ce Hou ,&nbsp;Fan Zhang ,&nbsp;Qihao Weng","doi":"10.1016/j.isprsjprs.2025.09.024","DOIUrl":"10.1016/j.isprsjprs.2025.09.024","url":null,"abstract":"<div><div>Crowd-sourced images (CSIs) offer an unprecedented opportunity for gaining deeper insights into urban built environments. However, the lack of precise geographic information limits their effectiveness in various urban applications. Traditional geo-localization methods, which rely on matching CSIs with geo-tagged street-view images (SVIs), face significant challenges due to sparse coverage and temporal misalignment of reference data, especially in developing countries. To overcome these limitations, this paper proposes a novel contrastive learning framework that integrates SVIs and satellite images (SIs), utilizing a multi-scale channel attention module and InfoNCE loss to enhance the geo-localization accuracy of CSIs. Additionally, we leverage SIs to generate synthetic SVIs in areas where actual SVIs are unavailable or outdated, ensuring comprehensive coverage across diverse urban environments. A simple yet efficient data preprocessing method is proposed to align multi-view images for enhanced feature fusion. As part of our research efforts, we introduce a Multi-Source Geo-localization Dataset (MSGD) consisting of 500k geo-tagged pairs collected from 12 cities across six continents, encompassing diverse urban typologies from dense skyscraper districts to low-density areas, providing a valuable resource for future research and advancements in geo-localization methods. Our experiments show that the proposed method outperforms state-of-the-art approaches on the challenging MSGD dataset, highlighting the importance of incorporating SIs as a complementary data source for accurate geo-localization. Our code and dataset will be released at <span><span>https://github.com/RCAIG/CrowdsourcingGeoLocalization</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"230 ","pages":"Pages 616-629"},"PeriodicalIF":12.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268639","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":"Prior knowledge-informed semantic segmentation framework for precise glacial lake mapping from multimodal imagery","authors":"Huizhi Tan ,&nbsp;Liming Jiang ,&nbsp;Haoran Liu ,&nbsp;Tingbin Zhang ,&nbsp;Irene Cheng","doi":"10.1016/j.isprsjprs.2025.09.022","DOIUrl":"10.1016/j.isprsjprs.2025.09.022","url":null,"abstract":"<div><div>Variation in size and number of glacial lakes (GLs) is important indicators of climate change in the cryosphere and have attracted increasing research attention. However, publicly annotated datasets suitable for computer vision techniques, especially deep learning-based GL mapping, remain scarce. Moreover, existing datasets often contain noisy labels, which affect evaluation results and subsequently hinder downstream processes such as multimodal remote sensing data fusion. To address these issues, we propose a prior knowledge-informed framework for GL segmentation that integrates a self-training-based correction algorithm for glacial lake segmentation dataset (ST-CAGL), which iteratively refines noisy annotations without manual intervention. We also introduce a dual encoder glacial lake semantic segmentation network (DEGSNet) that has a cross-modal feature rectification module (CM-FRM) to enhance multimodal data fusion. Through comparative and ablation experiments, our method achieves an IoU of 86.26% and a DICE of 92.05% at the patch level, yielding improvements of 3.39% in IoU and 2.15% in DICE over the best-performing CNN-based model (UNet), and 5.92% in IoU and 4.33% in DICE over the best-performing Transformer-based model (SegFormer-B3), when these two models are trained with uncorrected labels. In addition, our framework demonstrates superior performance in extracting small GLs, compared to current works. The source code and dataset are available at <span><span>https://github.com/tanhuizhi123/GlacierSeg</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"230 ","pages":"Pages 630-643"},"PeriodicalIF":12.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268646","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":"Disentangling vegetation physiological responses under extreme drought in the Amazon Rainforest: A multispectral remote sensing approach with insights from ET, SIF, and VOD","authors":"Xiang Zhang ,&nbsp;Junyi Liu ,&nbsp;Chao Yang ,&nbsp;Xihui Gu ,&nbsp;Aminjon Gulakhmadov ,&nbsp;Jiangyuan Zeng ,&nbsp;Hongliang Ma ,&nbsp;Zeqiang Chen ,&nbsp;Lin Zhao ,&nbsp;Lingtong Du ,&nbsp;Panda Rabindra Kumar ,&nbsp;Mahlatse Kganyago ,&nbsp;Veber Costa ,&nbsp;Won-Ho Nam ,&nbsp;Peng Sun ,&nbsp;Yonglin Shen ,&nbsp;Dev Niyogi ,&nbsp;Nengcheng Chen","doi":"10.1016/j.isprsjprs.2025.09.027","DOIUrl":"10.1016/j.isprsjprs.2025.09.027","url":null,"abstract":"<div><div>Extreme drought has profound effects on global vegetation, shaping carbon and water cycles and drawing significant research attention. Physiological responses and structural adaptations are two main aspects when vegetation dealing with drought. Traditional remote sensing methods, relying on indicators like Leaf Area Index (LAI), Solar-Induced Fluorescence (SIF), and Near Infrared reflectance of vegetation (NIRv), face challenges in disentangling mixed signals and capturing fine-scale physiological changes. To address this issue, we proposed a multi-spectral remote sensing approach to construct models that disentangle remote sensing signals only representing vegetation’s physiological response to drought. To achieve that, two separate random forest models were constructed using vegetation structural variables and hydro-meteorological variables to predict total and structural components of functional anomalies, quantified using SIF, Evapotranspiration (ET), and Vegetation Optical Depth (VOD) ratio. Subsequently, model residuals were calculated from the two models and used to disentangle the physiological component in observed remote sensing signals. The results in Amazon rainforest revealed that the physiological component explained the majority of functional anomalies during drought, with the physiological contributions of photosynthesis, transpiration, and water regulation functions accounting for 74.1%, 64.2%, and 71.8% of the anomalies in wet regions, and 67.7%, 62.6%, and 66.2% in dry regions, respectively. Attribution analysis indicated that regional hydro-meteorological conditions and vegetation types contributed to shaping the spatial patterns of vegetation physiological responses to drought, explaining 75.28% and 82.17% of the spatial variability in the physiological components during drought development and recovery phases. Structural equation modeling further elucidating causal pathways linking key environmental drivers to these physiological responses. The uncertainty of model predictions was quantified using the leave-one-out approach, yielding interquartile ranges of 0.72, 0.41, and 0.82 for the physiological component proportions of the three functional variables. This research disentangles physiological and structural responses with finer spatial and temporal resolution, providing a clearer view of vegetation dynamic changes and adaptation mechanisms. These findings emphasize the value of multi-spectral remote sensing in understanding vegetation functions under extreme drought conditions, offering a more detailed and accurate representation of vegetation dynamics.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"230 ","pages":"Pages 599-615"},"PeriodicalIF":12.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269131","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":"ControlBldg: A variable-controlled generative framework for conditioned modeling of vast 3D urban buildings","authors":"Lingfeng Liao ,&nbsp;Yoshiki Ogawa ,&nbsp;Chenbo Zhao ,&nbsp;Yoshihide Sekimoto","doi":"10.1016/j.isprsjprs.2025.09.026","DOIUrl":"10.1016/j.isprsjprs.2025.09.026","url":null,"abstract":"<div><div>Development of urban digital twins critically focuses on modeling three-dimensional (3D) buildings. Although numerous approaches have been proposed for 3D building reconstruction in urban environments, most cannot handle data deficiencies in specific areas, which prevents further improvements into more efficient approaches. While emerging methodologies using artificial intelligence-generated content provide alternative 3D digital cousin models without strict data source requirements, this study derived building digital cousins from it and proposed a generative framework incorporating multiple controlling factors for creating simulated building digital cousin representations as simulated approximations for efficient real-world 3D urban modeling. Our framework uses building footprints as a graphical control and parameter series as an appearance control to approximate building geometries by generating a pixel-wise building height map and then reconstructing the 3D architecture of the second level of details (LoD). This approach fully utilizes abundant pre-trained resources from existing large visual models and yields satisfactory results. In quantitative and qualitative evaluations, our proposed framework achieves excellent performance, with an average root mean square error (RMSE) lower than 0.27 m and a scaling accuracy higher than 96%, surpassing several baseline methodologies and competing with existing state-of-the-art reconstruction methods such as City3D and SimpliCity, while requiring far fewer visual data references. A comparison with LoD1 ground-truth models of the PLATEAU dataset demonstrates a 50% improvement in geometric proximities, confirming the robustness and adaptability of the framework. The involved artifacts are available at <span><span>https://github.com/Alive59/ControlBldg/tree/master</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"230 ","pages":"Pages 581-598"},"PeriodicalIF":12.2,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269130","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}