ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"National mapping of wetland vegetation leaf area index in China using hybrid model with Sentinel-2 and Landsat-8 data","authors":"Jianing Zhen ,&nbsp;Dehua Mao ,&nbsp;Yeqiao Wang ,&nbsp;Junjie Wang ,&nbsp;Chenwei Nie ,&nbsp;Shiqi Huo ,&nbsp;Hengxing Xiang ,&nbsp;Yongxing Ren ,&nbsp;Ling Luo ,&nbsp;Zongming Wang","doi":"10.1016/j.isprsjprs.2025.11.031","DOIUrl":"10.1016/j.isprsjprs.2025.11.031","url":null,"abstract":"<div><div>Leaf area index (LAI) of wetland vegetation provides vital information for its growth condition, structure and functioning. Accurately mapping LAI at a broad scale is essential for conservation and rehabilitation of wetland ecosystem. However, owing to the spatial complexity and periodic inundation characteristics of wetland vegetation, retrieving LAI of wetlands remains a challenging task with significant uncertainty. Here, with 865 in-situ measurements across different wetland biomes in China during 2013–2023, we proposed a hybrid strategy that incorporated active learning (AL) technique, physically-based PROSAIL-5B model, and Random Forest machine learning algorithm to map wetland biomes LAI across China from Sentinel-2 and Landsat-8 imagery. The validation results showed that the hybrid approach outperformed physically-based and empirically-based methods and achieved higher accuracy (R² increased by 0.15–0.40, RMSE decreased by 0.02–0.27, and RRMSE reduced by 3.37–12.78 %). Additionally, three indices that we newly-developed (TBVI5, TBVI3 and TBVI1) exhibited superior potential for LAI inversion across different types of wetland vegetation. Our mapping results exhibited spatial details and consistency, and matched with in-situ observations from Sentinel-2 compared to Landsat-8 and the other MODIS-based products. In this study, we developed the first national-scale mapping of wetland vegetation LAI in China. The findings offer insights into accurate retrieval of LAI in wetland vegetation, providing valuable support for the scientific restoration of wetlands and assessing their responses to climate change.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 18-33"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658151","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":"Explainable spatiotemporal deep learning for subseasonal super-resolution forecasting of Arctic sea ice concentration during the melting season","authors":"Jianxin He ,&nbsp;Yuxin Zhao ,&nbsp;Shuo Yang ,&nbsp;Woping Wu ,&nbsp;Jian Wang ,&nbsp;Xiong Deng","doi":"10.1016/j.isprsjprs.2025.11.027","DOIUrl":"10.1016/j.isprsjprs.2025.11.027","url":null,"abstract":"<div><div>Accurate, high-resolution forecasting of Arctic sea-ice concentration (SIC) during the melting season is crucial for climate monitoring and polar navigation, yet remains hindered by the system’s complex, multi-scale, and cross-sphere dynamics. We present MSS-STFormer, an explainable multi-scale spatiotemporal Transformer designed for subseasonal SIC super-resolution forecasting. The model integrates 14 environmental factors spanning the ice, ocean, and atmosphere, and incorporates four specialized modules to enhance spatiotemporal representation and physical consistency. Trained with OSTIA satellite observations and ERA5 reanalysis data, MSS-STFormer achieves high forecasting skill over a 60-day horizon, yielding an RMSE of 0.049, a correlation of 0.9951, an SSIM of 0.9603, and a BACC of 0.9656. Post-hoc explainability methods, Gradient SHAP and LIME—reveal that the model captures a temporally evolving prediction mechanism: early forecasts are dominated by persistence of initial conditions, mid-term phases are governed by atmospheric dynamics such as wind and pressure, and later stages transition to a coupled influence of radiative and dynamic processes. This progression aligns closely with established thermodynamic and dynamic theories of sea-ice evolution, underscoring the model’s ability to identify physically meaningful drivers. The framework demonstrates strong potential for advancing explainable GeoAI in Earth observation, combining predictive accuracy with physical explainability for operational Arctic SIC monitoring and climate applications.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 1-17"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625097","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":"Mapping melliferous tree species in Kenya via one-class classification with hyperspectral unsupervised domain adaptation","authors":"Zhaozhi Luo ,&nbsp;Janne Heiskanen ,&nbsp;Ilja Vuorinne ,&nbsp;Ian Ocholla ,&nbsp;Shiqi Zhang ,&nbsp;Saana Järvinen ,&nbsp;Xinyu Wang ,&nbsp;Yanfei Zhong ,&nbsp;Petri Pellikka","doi":"10.1016/j.isprsjprs.2025.12.028","DOIUrl":"10.1016/j.isprsjprs.2025.12.028","url":null,"abstract":"<div><div>The beekeeping sector holds significant potential for livelihood diversification among the agropastoral communities in Kenya. Melliferous tree species play a critical role by providing essential nectar sources for bees. However, limited knowledge of their precise spatial distributions constrains the full development of beekeeping. One-class classification (OCC) offers a practical solution for detecting single target species without requiring extensive labeled data from other classes. Although existing OCC methods perform well in trained domains, the generalization capability to unseen domains remains limited due to domain shift. To address these challenges, this study proposes a hyperspectral unsupervised domain adaptation OCC framework (HyUDA-One) for tree species mapping using airborne hyperspectral imagery and laser scanning data. The spatial–spectral regularized pseudo-positive learning was designed to mitigate domain shift and improve model generalizability. The effectiveness of HyUDA-One was demonstrated by mapping three key melliferous tree species in two savanna landscapes in southern Kenya. The results show that HyUDA-One significantly improves performance in unlabeled domains. The F1-scores of 0.788, 0.845, and 0.768 were achieved for <em>Senegalia mellifera</em>, <em>Vachellia tortilis</em>, and <em>Commiphora africana</em> in the trained domain, respectively. In the untrained domain, the F1-scores of <em>Senegalia mellifera</em> and <em>Vachellia tortilis</em> were 0.756 and 0.884, respectively. The distribution maps revealed the spatial patterns of these melliferous tree species and the nectar source availability, offering an important reference for sustainable beekeeping development in savanna landscapes. Furthermore, the proposed framework can potentially be extended to other mapping applications, such as invasive species detection.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 638-655"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925540","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":"Advancing table beet root yield estimation via unmanned aerial systems (UAS) multi-modal sensing","authors":"Mohammad S. Saif ,&nbsp;Robert Chancia ,&nbsp;Sean P. Murphy ,&nbsp;Sarah Pethybridge ,&nbsp;Jan van Aardt","doi":"10.1016/j.isprsjprs.2025.12.026","DOIUrl":"10.1016/j.isprsjprs.2025.12.026","url":null,"abstract":"<div><div>Unmanned aerial systems (UAS) offer significant potential to improve agricultural practices due to their multi-modal payload capacity, ease of deployment, and lower cost. However, there is a need to expand UAS capabilities by including root crops, offering robust, growth-stage-independent models, and providing a comprehensive assessment of various imaging systems, i.e., identifying application-specific sensing modalities. This study aims to tackle those challenges by presenting a unified Gaussian Process Regression (GPR) model for predicting end-of-season table beet (a subterranean root crop) yield using UAS-derived spectral and structural features, combined with meteorological data, while remaining robust to flight and harvest timing. Field trials were conducted at Cornell AgriTech in Geneva, NY during the 2021 and 2022 growing seasons. UAS flights captured five-band (475, 560, 668, 717, and 840 nm) multispectral imagery, hyperspectral imagery (400–1000 nm), and light detection and ranging (LiDAR) data at multiple times throughout the season. Our model achieved <em>R²_test</em> = 0.81 and MAPE_test = 15.7 % using only multispectral imagery, while the hyperspectral + LiDAR model attained <em>R²_test</em> = 0.79 and MAPE_test = 17.4 %, which is comparable to recent root yield modeling studies using UAS data. Shapely analysis was performed to gain further insight into model behavior. This analysis revealed canopy volume information to contain high relative importance, as compared to other features, for table beet root yield estimation. Our study demonstrated that UAS-based imaging, combined with a unified machine learning model, can effectively predict root crop yield, providing a scalable and transferable approach for precision agriculture.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 542-560"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925600","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":"AMS-Former: Adaptive multi-scale transformer for multi-modal image matching","authors":"Jiahao Rao ,&nbsp;Rui Liu ,&nbsp;Jianjun Guan ,&nbsp;Xin Tian","doi":"10.1016/j.isprsjprs.2026.01.021","DOIUrl":"10.1016/j.isprsjprs.2026.01.021","url":null,"abstract":"<div><div>Multi-modal image (MMI) matching plays a crucial role in the fusion of multi-source image information. However, due to the significant geometric and modality differences in MMI, existing methods often fail to achieve satisfactory matching performance. To address these challenges, we propose an end-to-end MMI matching approach, named adaptive multi-scale transformer (AMS-Former). First, AMS-Former constructs a multi-scale image matching framework that integrates contextual information across different scales, effectively identifying potential corresponding points and thereby improving matching accuracy. To handle the challenges caused by modality differences, we design a cross-modal feature extraction module with an adaptive modulation strategy. This module effectively couples features from different modalities, enhancing feature representation and improving model robustness under complex modality differences. To further enhance matching performance, we design a suitable loss function for the proposed AMS-Former to guide the optimization of network parameters. Finally, we use a cross-scale mutual supervision strategy to remove incorrect corresponding points and enhance the reliability of the matching results. Extensive experiments on five MMI datasets demonstrate that AMS-Former outperforms state-of-the-art methods, including RIFT, ASS, COFSM, POS-GIFT, Matchformer, SEMLA, TopicFM, and Lightglue. Our code is available at: <span><span>https://github.com/Henryrjh/AMS_Former</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 957-973"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001036","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":"Mapping oil spills under varying sun glint conditions using a diffusion model with spatial-spectral-frequency constraints","authors":"Kai Du ,&nbsp;Yi Ma ,&nbsp;Zhongwei Li ,&nbsp;Rongjie Liu ,&nbsp;Zongchen Jiang ,&nbsp;Junfang Yang","doi":"10.1016/j.isprsjprs.2025.11.032","DOIUrl":"10.1016/j.isprsjprs.2025.11.032","url":null,"abstract":"<div><div>Marine oil spill detection via optical remote sensing is critically challenged by complex optical signatures induced by sun glint, which causes contrast reversals and masks the spectral features of different oil emulsion types. This study introduces a novel deep learning framework, the Spatial-Spectral Attention Conditioned Diffusion Probabilistic Model with Dual-branch Frequency Parser (OSS-Diff). The core architecture integrates two specialized modules. The first, a physics-informed Dual-branch Frequency Parser (DF-P) module, is designed to disentangle contrast variations by separately processing high-frequency edge features (characteristic of bright spills in strong glint) and low-frequency background information (typical of dark spills in weak glint). Concurrently, a systematically designed Spatial-Spectral Attention (SS-A) module targets spectral ambiguity by adaptively amplifying the discriminative features crucial for identifying oil emulsification states. Extensive experiments confirm the model’s superior performance. Under weak sun glint, it achieved F1-scores of 0.907 for non-emulsified and 0.862 for emulsified slicks. For positive-contrast spills under strong sun glint, it achieved an F1-score of 0.918. Ablation studies validate the synergistic effect of the proposed components, with the full model achieving a 4.9% F1-score gain over the baseline. Significantly, this work also reveals a potential link between the contrast inversion angle and the oil’s refractive index, suggesting a novel avenue for remotely characterizing the physical properties of oil spills. This research provides a robust framework for automated oil spill monitoring, offering a reliable model for environmental protection and emergency response.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 48-61"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657554","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":"Roof-aware indoor BIM reconstruction from LiDAR via graph-attention for residential buildings","authors":"Biao Xiong ,&nbsp;Bohan Wang ,&nbsp;Yiyi Liu ,&nbsp;Liangliang Wang ,&nbsp;Yanchao Yang ,&nbsp;Liang Zhou ,&nbsp;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":"2026-02-01","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}

{"title":"Cross-satellite hierarchical multimodal denoising for hyperspectral imagery","authors":"Minghua Wang ,&nbsp;Bo Shang ,&nbsp;Yilun Li ,&nbsp;Xin Zhao ,&nbsp;Lianru Gao ,&nbsp;Xu Sun ,&nbsp;Longfei Ren","doi":"10.1016/j.isprsjprs.2025.11.028","DOIUrl":"10.1016/j.isprsjprs.2025.11.028","url":null,"abstract":"<div><div>The rapid proliferation of remote sensing satellite constellations has ushered in an era of unprecedented access to hyperspectral (HS) and multispectral (MS) Earth observation data. HS denoising remains a critical research focus for enhancing the interpretation and application of satellite data. However, on the one hand, the available characteristics of noisy HS satellite data are often limited under real-world and unknown conditions. On the other hand, existing works ignore the complementary advantages offered by MS satellite imagery that can be employed to enhance HS denoising performance. Leveraging the synergistic potential of HS and MS data, this study pioneers a novel paradigm for the joint exploitation and optimization of multi-source remote sensing satellites. A novel Hierarchical Dual Tucker Decomposition (DTucker) framework is proposed to capitalize on the low-rank (LR) tensor property of HS and MS cross-satellite data. The inheritance of properties from the original tensor to the core tensor is explored through a manually designed model-driven constraint or a data-driven multilayer perceptron (MLP) framework. This enables robust integration of MS-derived spatial richness with HS details and significantly enhances the denoising capability. We construct HS–MS data pairs from real satellite observations, including Earth Observing-1, Sentinel-2, Gaofen-1, Gaofen-5, and Gaofen-6. Four datasets span diverse scenes such as runways, urban areas, rivers, and farmlands. The proposed method demonstrates strong generalization across various satellite combinations and application scenarios. Notably, the incorporation of MS data markedly enhances both class discrimination and structural detail in the denoised HS outputs, promoting the performance of subsequent classification and analysis tasks. The datasets and codes implemented in MATLAB and PYTHON will be available on the website: <span><span>https://github.com/MinghuaWang123/DTucker</span><svg><path></path></svg></span>, contributing to the remote sensing community.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 94-108"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684885","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":"Extrapolate azimuth angles: Text and edge guided ISAR image generation based on foundation model","authors":"Jiawei Zhang,&nbsp;Xiaolin Zhou,&nbsp;Weidong Jiang,&nbsp;Xiaolong Su,&nbsp;Zhen Liu,&nbsp;Li Liu","doi":"10.1016/j.isprsjprs.2025.12.002","DOIUrl":"10.1016/j.isprsjprs.2025.12.002","url":null,"abstract":"<div><div>Inverse Synthetic Aperture Radar (ISAR) has been widely applied in remote sensing and space target monitoring. Automatic Target Recognition (ATR) based on ISAR imagery plays a critical role in target interpretation and pose estimation. With the growing adoption of intelligent methods in the ATR domain, the quantity and quality of ISAR data have become decisive factors influencing algorithm performance. However, due to the complexity of ISAR imaging algorithms and the high cost of data acquisition, high-quality ISAR image datasets remain extremely scarce. As a result, learning the underlying characteristics of existing ISAR data to generate large-scale usable samples has become a pressing research focus. Although some preliminary studies have explored ISAR image data augmentation, most of them rely on image sequence interpolation or conditional generation, both of which exhibit critical limitations: the former requires densely sampled image sequences with small angular intervals, while the latter can only model the mapping between limited azimuth conditions and ISAR images. Neither approach is capable of generating images of new targets under unseen azimuth conditions, resulting in poor generalization and leaving substantial room for further exploration. To address these limitations, we formally define a novel research problem, termed ISAR azimuth angle extrapolation. This task fundamentally involves high-dimensional, structured, cross-view image synthesis, requiring the restoration of visual details while ensuring physical consistency and structural stability. To address this problem, we propose ISAR-ExtraNet, a foundation-model-based framework for ISAR azimuth angle extrapolation. ISAR-ExtraNet leverages the strong representation, modeling, and generalization capabilities of pretrained foundation models to generate ISAR images of new targets under novel azimuth conditions. Specifically, the model employs a two-stage coarse-to-fine fine-tuning strategy, incorporating optical image contours and scattering center distribution constraints to guide the generation process. This design enhances both semantic alignment and structural fidelity in the generated ISAR images. Comprehensive experiments demonstrate that ISAR-ExtraNet significantly outperforms baseline methods and fine-tuned foundation models, achieving 28.76 dB in PSNR and 0.80 in SSIM. We hope that the training paradigm introduced in ISAR-ExtraNet will inspire further exploration of the ISAR azimuth extrapolation problem and foster progress in this emerging research area.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 109-123"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684886","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":"CityVLM: Towards sustainable urban development via multi-view coordinated vision–language model","authors":"Junjue Wang ,&nbsp;Weihao Xuan ,&nbsp;Heli Qi ,&nbsp;Zihang Chen ,&nbsp;Hongruixuan Chen ,&nbsp;Zhuo Zheng ,&nbsp;Junshi Xia ,&nbsp;Yanfei Zhong ,&nbsp;Naoto Yokoya","doi":"10.1016/j.isprsjprs.2025.11.030","DOIUrl":"10.1016/j.isprsjprs.2025.11.030","url":null,"abstract":"<div><div>Vision–language models (VLMs) have shown remarkable promise in Earth Vision, particularly in providing human-interpretable analysis of remote sensing imagery. While existing VLMs excel at general visual perception tasks, they often fall short in addressing the complex needs of geoscience, which requires comprehensive urban analysis across geographical, social, and economic dimensions. To bridge this gap, we expand VLM capabilities to tackle sustainable urban development challenges by integrating two complementary sources: remote sensing (RS) and street-view (SV) imagery. Specifically, we first design a multi-view vision–language dataset (<em>CitySet</em>), comprising 20,589 RS images, 1.1 million SV images, and 0.8 million question–answer pairs. <em>CitySet</em> facilitates geospatial object reasoning, social object analysis, urban economic assessment, and sustainable development report generation. Besides, we develop <em>CityVLM</em> to integrate macro- and micro-level semantics using geospatial and temporal modeling, while its language modeling component generates detailed urban reports. We extensively benchmarked 10 advanced VLMs on our dataset, revealing that state-of-the-art models struggle with urban analysis tasks, primarily due to domain gaps and limited multi-view data alignment capabilities. By addressing these issues, <em>CityVLM</em> achieves superior performance consistently across all tasks and advances automated urban analysis through practical applications like heat island effect monitoring, offering valuable tools for city planners and policymakers in their sustainability efforts.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 62-74"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685023","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}