While Multimodal Large Language Models (MLLMs) excel in general vision-language tasks, their application to remote sensing change understanding is hindered by a fundamental "temporal blindness". Existing architectures lack intrinsic mechanisms for multi-temporal contrastive reasoning and struggle with precise spatial grounding. To address this, we first introduce Delta-QA, a comprehensive benchmark comprising 180k visual question-answering samples. Delta-QA unifies pixel-level segmentation and visual question answering across bi- and tri-temporal scenarios, structuring change interpretation into four progressive cognitive dimensions. Methodologically, we propose Delta-LLaVA, a novel MLLM framework explicitly tailored for multi-temporal remote sensing interpretation. It overcomes the limitations of naive feature concatenation through three core innovations: a Change-Enhanced Attention module that systematically isolates and amplifies visual differences, a Change-SEG module utilizing Change Prior Embedding to extract differentiable difference features as input for the LLM, and Local Causal Attention to prevent cross-temporal contextual leakage. Extensive experiments demonstrate that Delta-LLaVA decisively outperforms leading generalist MLLMs and specialized segmentation models in complex change deduction and high-precision boundary localization, establishing a unified framework for earth observation intelligence.
UniRS: Unifying Multi-temporal Remote Sensing Tasks through Vision Language Models
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Geographic Sciences, Hunan Normal University（湖南师范大学）；City University of Hong Kong（香港城市大学）
SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Information and Communications Engineering, Xi’an Jiaotong University（西安交通大学信息与通信工程学院）；Ministry of Education Key Lab of Intelligent Network Security, Xi’an, China（教育部智能网络安全重点实验室）；Pazhou Laboratory (Huangpu), Radarweg 29, Guangzhou, China（琶洲实验室（黄埔），雷达路29号，广州，中国）
Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction
Jiangsu Provincial Key Laboratory for Advanced Remote Sensing and Geographic Information Technology（江苏省先进遥感与地理信息科技重点实验室）；Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources（自然资源部土地卫星遥感应用重点实验室）；School of Geography and Ocean Science, Nanjing University（南京大学地理与海洋科学学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）
Advances in high resolution remote sensing image analysis are currently hampered by the difficulty of gathering enough annotated data for training deep learning methods, giving rise to a variety of small datasets and associated dataset-specific methods. Moreover, typical tasks such as classification and retrieval lack a systematic evaluation on standard benchmarks and training datasets, which make it hard to identify durable and generalizable scientific contributions. We aim at unifying remote sensing image retrieval and classification with a new large-scale training and testing dataset, SF300…
SpecAware: A Spectral-Content Aware Foundation Model for Unifying Multi-Sensor Learning in Hyperspectral Remote Sensing Mapping
Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University（空间-时间大数据分析与应用国家级重点实验室（自然资源部），东华师范大学）；School of Geospatial Artificial Intelligence, East China Normal University（地理信息人工智能学院，东华师范大学）；Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University（地理信息科学重点实验室（教育部），东华师范大学）；School of Geographic Sciences, East China Normal University（地理科学学院，东华师范大学）；Shanghai Municipal Institute of Surveying and Mapping（上海市测绘院）；Institute of Defense Engineering, AMS（工程院，AMS）
Prior-guided Fusion of Multimodal Features for Change Detection from Optical-SAR Images
Institute of Geospatial Information, Information Engineering University（地理信息研究所，信息工程大学）；Academy of Digital China (Fujian), Fuzhou University（数字中国研究院（福建），福州大学）；The School of Electronics and Communication Engineering, Sun Yat-sen University（电子与通信工程学院，中山大学）；The Department of Information Engineering and Computer Science, University of Trento（信息工程与计算机科学系，特伦托大学）
1%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimization
BNRist, Department of Computer Science；Technology, Tsinghua University, Beijing, China；School of Automation；Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China；Nankai lnternational Advanced Research Institute (Shenzhen Futian) \& SLAI, Shenzhen, China
We address the curve lane detection problem which poses more realistic challenges than conventional lane detection for better facilitating modern assisted/autonomous driving systems. Current hand-designed lane detection methods are not robust enough to capture the curve lanes especially the remote parts due to the lack of modeling both long-range contextual information and detailed curve trajectory. In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information whil…
In many developing nations, a lack of poverty data prevents critical humanitarian organizations from responding to large-scale crises. Currently, socioeconomic surveys are the only method implemented on a large scale for organizations and researchers to measure and track poverty. However, the inability to collect survey data efficiently and inexpensively leads to significant temporal gaps in poverty data; these gaps severely limit the ability of organizational entities to address poverty at its root cause. We propose a transfer learning model based on surface temperature change and remote sens…
Task-Guided Prompting for Unified Remote Sensing Image Restoration
School of Electronic and Information Engineering, Ningbo University of Technology（宁波工程学院电子与信息工程学院）；University of Exeter（埃克塞特大学）；Engineering Research Center of Intelligent Finance, Ministry of Education（教育部智能金融工程研究中心）；School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机与人工智能学院）
SpectralTrain: A Universal Framework for Hyperspectral Image Classification
School of Medical Information, Wannan Medical University（皖南医学院信息学院）；University of Chinese Academy of Sciences（中国科学院大学）；The Chinese University of Hong Kong（香港中文大学）；Northeastern University（东北大学）
JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering
Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学电子工程系，北京信息科学与技术国家研究中心）；Chang Guang Satellite Technology Co., Ltd. (CGSTL)（长光卫星技术股份有限公司）；College of Communications Engineering, Army Engineering University of PLA（中国人民解放军陆军工程大学通信工程学院）
