ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"PANet: A multi-scale temporal decoupling network and its high-resolution benchmark dataset for detecting pseudo changes in cropland non-agriculturalization","authors":"Songman Sui ,&nbsp;Jixian Zhang ,&nbsp;Haiyan Gu ,&nbsp;Yue Chang","doi":"10.1016/j.isprsjprs.2026.01.029","DOIUrl":"10.1016/j.isprsjprs.2026.01.029","url":null,"abstract":"<div><div>Cropland non-agriculturalization (CNA) refers to the conversion of cropland into non-agricultural land such as construction land or ponds, posing threats to food security and ecological balance. Remote sensing technology enables precise monitoring of this process, but bi-temporal methods are susceptible to errors caused by seasonal spectral fluctuations, weather interference, and imaging discrepancies, often leading to false detections. Existing methods, which lack support from temporal datasets, struggle to disentangle the spectral confusion of gradual non-agriculturalization and short-term disturbances, thereby limiting the accuracy of dynamic cropland resource monitoring. To address this issue, a novel phenology-aware temporal change detection network (PANet) is proposed to solve the misclassification challenges in CNA detection caused by “same object with different spectra” and “different objects with similar spectra” issues. A phenology-aware module (PATM) is designed, leveraging a dual-driven decoupling model to dynamically weight phenology-sensitive periods and adaptively represent non-uniform temporal intervals. Through a time-aligned feature enhancement strategy and dual-driven (intra-annual/inter-annual) temporal decay functions, PANet simultaneously focuses on short-term anomalies and robustly models long-term trends. Additionally, a sample balance adjustment module (DFBL) is developed to mitigate the impact of sample imbalance by incorporating prior knowledge of changes and dynamic adjustment factors, enhancing the model’s sensitivity to non-agriculturalization changes. Furthermore, the first high-resolution CNA dataset based on actual production data is constructed, containing 1295 pairs of 512 × 512 masked images. Compared to existing datasets, this dataset offers extensive temporal coverage, capturing comprehensive seasonal periodic characteristics of cropland. Comparative experiments with several classical time-series methods and bi-temporal methods validate the effectiveness of PANet. Experimental results on the LHCD dataset demonstrate that PANet achieves the highest F1 score, specifically, 61.01% and 61.70%. PANet accurately captures CNA information, making it vital for the scientific management and sustainable utilization of limited cropland resources. The LHCD can be downloaded from <span><span>https://github.com/mss-s/LHCD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 126-143"},"PeriodicalIF":12.2,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033659","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":"Detecting global ocean subsurface density change with high-resolution via dual-task densely-former","authors":"Hua Su ,&nbsp;Weiqi Xie ,&nbsp;Luping You ,&nbsp;Sihui Li ,&nbsp;Dian Lin ,&nbsp;An Wang","doi":"10.1016/j.isprsjprs.2026.01.026","DOIUrl":"10.1016/j.isprsjprs.2026.01.026","url":null,"abstract":"<div><div>High-resolution ocean subsurface density is crucial for studying dynamic processes and stratification within the ocean under recent global ocean warming. This study proposes a novel deep learning-based model, named DDFNet (Dual-task Densely-Former Network), for reconstructing ocean subsurface density, to address the challenges in reconstructing high-resolution and high-reliability global ocean subsurface density. DDFNet employs multi-scale feature extraction, attention mechanisms, and a dual-label design, combining an encoder-decoder backbone network with a global spatial attention module to capture the complex spatiotemporal relationships in ocean data effectively. The model utilizes multisource surface remote sensing data as input and incorporates Argo profile data and ORAS5 reanalysis data as labels. An adaptive weighted loss function dynamically balances the contributions of the two label types, improving reconstruction accuracy and achieving a spatial resolution of 0.25°×0.25°. By constructing dual tasks with <em>in situ</em> observations and reanalysis data for joint learning, the true state of the ocean and the consistency of physical processes are enhanced, improving the model’s reconstruction accuracy and physical consistency. Experimental results demonstrate that DDFNet outperforms well-used LightGBM and CNN models, with the reconstructed DDFNet-SD dataset achieving an <em>R²</em> of 0.9863 and an RMSE of 0.2804 kg/m³. The dataset further reveals a declining trend in global ocean subsurface density at a rate of −4.47 × 10^-4 kg/m³/decade, particularly pronounced in the upper 0–700 m, which is likely associated with global ocean warming and salinity changes. The high-resolution dataset facilitates studies on mesoscale ocean dynamics, stratification variability, and climate change impacts.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 158-179"},"PeriodicalIF":12.2,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033934","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":"SuperMapNet for long-range and high-accuracy vectorized HD map construction","authors":"Ruqin Zhou ,&nbsp;Chenguang Dai ,&nbsp;Wanshou Jiang ,&nbsp;Yongsheng Zhang ,&nbsp;Zhenchao Zhang ,&nbsp;San Jiang","doi":"10.1016/j.isprsjprs.2026.01.023","DOIUrl":"10.1016/j.isprsjprs.2026.01.023","url":null,"abstract":"<div><div>Vectorized high-definition (HD) map construction is formulated as the task of classifying and localizing typical map elements based on features in a bird’s-eye view (BEV). This is essential for autonomous driving systems, providing interpretable environmental structured representations for decision and planning. Remarkable work has been achieved in recent years, but several major issues remain: (1) in the generation of the BEV features, single modality methods suffer from limited perception capability and range, while existing multi-modal fusion approaches underutilize cross-modal synergies and fail to resolve spatial disparities between modalities, resulting in misaligned BEV features with holes; (2) in the classification and localization of map elements, existing methods heavily rely on point-level modeling information while neglecting the information between elements and between point and element, leading to low accuracy with erroneous shapes and element entanglement. To address these limitations, we propose SuperMapNet, a multi-modal framework designed for long-range and high-accuracy vectorized HD map construction. This framework uses both camera images and LiDAR point clouds as input. It first tightly couples semantic information from camera images and geometric information from LiDAR point clouds by a cross-attention based synergy enhancement module and a flow-based disparity alignment module for long-range BEV feature generation. Subsequently, local information acquired by point queries and global information acquired by element queries are tightly coupled by three-level interactions for high-accuracy classification and localization, where Point2Point interaction captures local geometric consistency between points of the same element, Element2Element interaction learns global semantic relationships between elements, and Point2Element interaction complement element information for its constituent points. Experiments on the nuScenes and Argoverse2 datasets demonstrate high accuracy, surpassing previous state-of-the-art methods (SOTAs) by 14.9%/8.8% and 18.5%/3.1% mAP under the hard/easy settings, respectively, even over the double perception ranges (up to 120 <span><math><mi>m</mi></math></span> in the X-axis and 60 <span><math><mi>m</mi></math></span> in the Y-axis). The code is made publicly available at <span><span>https://github.com/zhouruqin/SuperMapNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 89-103"},"PeriodicalIF":12.2,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015046","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":"Roadside lidar-based scene understanding toward intelligent traffic perception: A comprehensive review","authors":"Jiaxing Zhang ,&nbsp;Chengjun Ge ,&nbsp;Wen Xiao ,&nbsp;Miao Tang ,&nbsp;Jon Mills ,&nbsp;Benjamin Coifman ,&nbsp;Nengcheng Chen","doi":"10.1016/j.isprsjprs.2026.01.012","DOIUrl":"10.1016/j.isprsjprs.2026.01.012","url":null,"abstract":"<div><div>Urban transportation systems are undergoing a paradigm shift with the integration of high-precision sensing technologies and intelligent perception frameworks. Roadside lidar, as a key enabler of infrastructure-based sensing technology, offers robust and precise 3D spatial understanding of dynamic urban scenes. This paper presents a comprehensive review of roadside lidar-based traffic perception, structured around five key modules: sensor placement strategies; multi-lidar point cloud fusion; dynamic traffic information extraction;subsequent applications including trajectory prediction, collision risk assessment, and behavioral analysis; representative roadside perception benchmark datasets. Despite notable progress, challenges remain in deployment optimization, robust registration under occlusion and dynamic conditions, generalizable object detection and tracking, and effective utilization of heterogeneous multi-modal data. Emerging trends point toward perception-driven infrastructure design, edge-cloud-terminal collaboration, and generalizable models enabled by domain adaptation, self-supervised learning, and foundation-scale datasets. This review aims to serve as a technical reference for researchers and practitioners, providing insights into current advances, open problems, and future directions in roadside lidar-based traffic perception and digital twin applications.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 69-88"},"PeriodicalIF":12.2,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015045","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":"Empowering tree-scale monitoring over large areas: Individual tree delineation from high-resolution imagery","authors":"Xinlian Liang ,&nbsp;Yinrui Wang ,&nbsp;Jun Pan ,&nbsp;Janne Heiskanen ,&nbsp;Ningning Wang ,&nbsp;Siyu Wu ,&nbsp;Ilja Vuorinne ,&nbsp;Jiaojiao Tian ,&nbsp;Jonas Troles ,&nbsp;Myriam Cloutier ,&nbsp;Stefano Puliti ,&nbsp;Aishwarya Chandrasekaran ,&nbsp;James Ball ,&nbsp;Xiangcheng Mi ,&nbsp;Guochun Shen ,&nbsp;Kun Song ,&nbsp;Guofan Shao ,&nbsp;Rasmus Astrup ,&nbsp;Yunsheng Wang ,&nbsp;Petri Pellikka ,&nbsp;Jianya Gong","doi":"10.1016/j.isprsjprs.2025.12.022","DOIUrl":"10.1016/j.isprsjprs.2025.12.022","url":null,"abstract":"<div><div>Accurate individual tree delineation (ITD) is essential for forest monitoring, biodiversity assessment, and ecological modeling. While remote sensing (RS) has significantly advanced forest ITD, challenges persist, especially in complex forest environments. The use of imagery data is compelling given the rapid increase in available high-resolution aerial and satellite imagery data, the increasing need for image-based analysis where reliable 3D data are unavailable, the widening gap between data supply and processing capabilities, and the limited validation of state-of-the-art (SOTA) methods across diverse real-world conditions. This study aims to advance ITD research by evaluating SOTA instance segmentation approaches, including both recently developed and established methods. The analysis evaluates ITD algorithm performance using the largest forest instance-segmentation imagery dataset to date and standardized evaluation protocols. This study identifies key factors affecting accuracy, reveals remaining challenges, and outlines future research directions. Findings in this study reveal that ITD accuracy is heavily influenced by image resolution, forest structure, and method design. Findings also reveal that, while algorithm innovations remain important, robustness and transferability that ensure generalization across diverse environments are what differentiate method performances. In addition, this study highlights that commonly used evaluation metrics may fail to adequately capture precise performance in specific applications, e.g., individual-tree-crown segmentation in this study. Assessment reliability can be strengthened through the adoption of stricter criteria. Future research should focus on expanding datasets, refining evaluation protocols, and developing adaptive models capable of handling varying canopy structures. These advancements will enhance ITD scalability and reliability, contributing to more effective forest research and management at a global scale.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 974-999"},"PeriodicalIF":12.2,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015049","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":"VectorLLM: Human-like extraction of structured building contours via multimodal LLMs","authors":"Tao Zhang ,&nbsp;Shiqing Wei ,&nbsp;Shihao Chen ,&nbsp;Wenling Yu ,&nbsp;Muying Luo ,&nbsp;Shunping Ji","doi":"10.1016/j.isprsjprs.2026.01.025","DOIUrl":"10.1016/j.isprsjprs.2026.01.025","url":null,"abstract":"<div><div>Automatically extracting vectorized building contours from remote sensing imagery is crucial for urban planning, population estimation, and disaster assessment. Current state-of-the-art methods rely on complex multi-stage pipelines involving pixel segmentation, vectorization, and polygon refinement, which limits their scalability and real-world applicability. Inspired by the remarkable reasoning capabilities of Large Language Models (LLMs), we introduce VectorLLM, the first Multi-modal Large Language Model (MLLM) designed for regular building contour extraction from remote sensing images. Unlike existing approaches, VectorLLM performs corner-point by corner-point regression of building contours directly, mimicking human annotators’ labeling process. Our architecture consists of a vision foundation backbone, an MLP connector, and an LLM, enhanced with learnable position embeddings to improve spatial understanding capability. Through comprehensive exploration of training strategies including pretraining, supervised fine-tuning, and direct preference optimization across WHU, WHU-Mix, and CrowdAI datasets, VectorLLM outperforms the previous SOTA methods. Remarkably, VectorLLM exhibits strong zero-shot performance on unseen objects including aircraft, water bodies, and oil tanks, highlighting its potential for unified modeling of diverse remote sensing object contour extraction tasks. Overall, this work establishes a new paradigm for vector extraction in remote sensing, leveraging the topological reasoning capabilities of LLMs to achieve both high accuracy and exceptional generalization. All code and weights will be available at <span><span>https://github.com/zhang-tao-whu/VectorLLM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 55-68"},"PeriodicalIF":12.2,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001038","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":"2026-01-19","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":"WEGLA-NormGAN: wavelet-enhanced Cycle-GAN with global-local attention for radiometric normalization of remote sensing images","authors":"Wenxia Gan ,&nbsp;Yu Feng ,&nbsp;Jianhao Miao ,&nbsp;Xinghua Li ,&nbsp;Huanfeng Shen","doi":"10.1016/j.isprsjprs.2026.01.020","DOIUrl":"10.1016/j.isprsjprs.2026.01.020","url":null,"abstract":"<div><div>The diversity of satellite remote sensing images has significantly enhanced the capability to observe surface information on Earth. However, multi-temporal optical remote sensing images acquired from different sensor platforms often exhibit substantial radiometric discrepancies, and it is difficult to obtain overlapping reference images, which poses critical challenges for seamless large-scale mosaicking, including global radiometric inconsistency, unsmooth local transitions, and visible seamlines. Existing traditional and deep learning methods can achieve reasonable performance on paired datasets, but often face challenges in balancing spatial structural integrity with enhanced radiometric consistency and generalizing to unseen images. To address these issues, a wavelet-enhanced radiometric normalization network called WEGLA-NormGAN is proposed to generate radiometrically normalized imagery with sound radiometric consistency and spatial fidelity. This framework integrates frequency-domain and spatial-domain information to achieve consistent multi-scale radiometric feature modeling while ensuring spatial structural fidelity. Firstly, wavelet transform is introduced to effectively decouple radiometric information and structural features from images, explicitly enhancing radiometric feature representation and edge-texture preservation. Secondly, a U-Net architecture with multi-scale modeling advantages is fused with an adaptive attention mechanism incorporating residual structures. This hybrid design employs a statistical alignment strategy to efficiently extract global shallow features and local statistical information, adaptively adjust the dynamic attention of unseen data, and alleviate local distortions, improving radiometric consistency and achieving high-fidelity spatial structure preservation. The proposed framework generates radiometrically normalized imagery that harmonizes radiometric consistency with spatial fidelity, while achieving outstanding radiometric normalization even in unseen scenarios. Extensive experiments were conducted on two public datasets and a self-constructed dataset. The results demonstrate that WEGLA-NormGAN outperforms seven state-of-the-art methods in cross-temporal scenarios and five in cross-spatiotemporal scenarios in terms of radiometric consistency, structural fidelity, and robustness. The code is available at <span><span>https://github.com/WITRS/WeGLA-Norm.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 39-54"},"PeriodicalIF":12.2,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001037","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":"Attributing GHG emissions to individual facilities using multi-temporal hyperspectral images: Methodology and applications","authors":"Yichi Zhang ,&nbsp;Ge Han ,&nbsp;Yiyang Huang ,&nbsp;Huayi Wang ,&nbsp;Hongyuan Zhang ,&nbsp;Zhipeng Pei ,&nbsp;Yuanxue Pu ,&nbsp;Haotian Luo ,&nbsp;Jinchun Yi ,&nbsp;Tianqi Shi ,&nbsp;Siwei Li ,&nbsp;Wei Gong","doi":"10.1016/j.isprsjprs.2026.01.014","DOIUrl":"10.1016/j.isprsjprs.2026.01.014","url":null,"abstract":"<div><div>Industrial parks are major sources of greenhouse gas (GHG) emissions and the ultimate entities responsible for implementing mitigation policies. Current satellite remote sensing technologies perform well in reporting localized strong point-source emissions, but face significant challenges in monitoring emissions from multiple densely clustered sources. To address the limitation, we propose an emission allocation framework, EA-MILES, which integrates multi-source hyperspectral data with plume modeling to quantify process-level emissions. Simulation experiments show that with existing hyperspectral satellites, EA-MILES can estimate emissions for sources with intensities above 80 t CO₂/h and 100 kg CH₄/h with bias not exceed 13.60 % and 17.08 %. A steel and power production park is selected as a case study, where EA-MILES estimates process-level emissions with uncertainties ranging from 26.33 % to 37.78 %. Estimation results are consistent with inventory values derived from emission factor methods. Top-down Integrated Mass Enhancement method is utilized to compare with EA-MILES results, the estimation bias did not exceed 16.84 %. According to the <em>Climate TRACE</em>, about 32 % of CO₂ and 44 % of CH₄ point-sources worldwide fall within EA-MILES detection coverage, accounting for over 80 % and 55 % of anthropogenic CO₂ and CH₄ emissions. Therefore, this study provides a novel satellite-based approach for reporting facility-scale GHG emissions in industrial parks, offering transparent and accurate monitoring data to support the mitigation and energy transition decision-making.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 937-956"},"PeriodicalIF":12.2,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976628","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":"BEDI: a comprehensive benchmark for evaluating embodied agents on UAVs","authors":"Mingning Guo ,&nbsp;Mengwei Wu ,&nbsp;Jiarun He,&nbsp;Shaoxian Li,&nbsp;Haifeng Li,&nbsp;Chao Tao","doi":"10.1016/j.isprsjprs.2026.01.013","DOIUrl":"10.1016/j.isprsjprs.2026.01.013","url":null,"abstract":"<div><div>With the rapid advancement of low-altitude remote sensing and Vision-Language Models (VLMs), Embodied Agents based on Unmanned Aerial Vehicles (UAVs) have shown significant potential in autonomous tasks. However, current evaluation methods for UAV-Embodied Agents (UAV-EAs) remain constrained by the lack of standardized benchmarks, diverse testing scenarios and open system interfaces. To address these challenges, we propose BEDI (Benchmark for Embodied Drone Intelligence), a systematic and standardized benchmark designed for evaluating UAV-EAs. Specifically, we introduce a novel Dynamic Chain-of-Embodied-Task paradigm based on the perception-decision-action loop, which decomposes complex UAV tasks into standardized, measurable subtasks. Building on this paradigm, we design a unified evaluation framework encompassing six core sub-skills: semantic perception, spatial perception, motion control, tool utilization, task planning and action generation. Furthermore, we develop a hybrid testing platform that<!--> <!-->incorporates a wide range of both virtual and real-world scenarios, enabling a comprehensive evaluation of UAV-EAs across diverse contexts. The platform also offers open and standardized interfaces, allowing researchers to customize tasks and extend scenarios, thereby enhancing flexibility and scalability in the evaluation process. Finally, through empirical evaluations of several state-of-the-art (SOTA) VLMs, we reveal their limitations in embodied UAV tasks, underscoring the critical role of the BEDI benchmark in advancing embodied intelligence research and model optimization. By filling the gap in systematic and standardized evaluation within this field, BEDI facilitates objective model comparison and lays a robust foundation for future development in this field. Our benchmark is now publicly available at <span><span>https://github.com/lostwolves/BEDI</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 910-936"},"PeriodicalIF":12.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976632","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}