This paper introduces the task of visual question answering for remote sensing data (RSVQA). Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method introduced in this article, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task. We report the results obtained by applying a model based on Convolutional Neural Networks (CNNs) for the visual part and on a Recurrent Neural Network (RNN) for the natural language part to this task. The model is trained on the two datasets, yielding promising results in both cases.
Technical University of Munich（慕尼黑工业大学）；German Aerospace Center（德国航空航天中心）；Remote Sensing Technology Institute（遥感技术研究所）
Visual question answering (VQA) for remote sensing scene has great potential in intelligent human-computer interaction system. Although VQA in computer vision has been widely researched, VQA for remote sensing data (RSVQA) is still in its infancy. There are two characteristics that need to be specially considered for the RSVQA task. 1) No object annotations are available in RSVQA datasets, which makes it difficult for models to exploit informative region representation; 2) There are questions with clearly different difficulty levels for each image in the RSVQA task. Directly training a model w…
With the new generation of satellite technologies, the archives of remote sensing (RS) images are growing very fast. To make the intrinsic information of each RS image easily accessible, visual question answering (VQA) has been introduced in RS. VQA allows a user to formulate a free-form question concerning the content of RS images to extract generic information. It has been shown that the fusion of the input modalities (i.e., image and text) is crucial for the performance of VQA systems. Most of the current fusion approaches use modality-specific representations in their fusion modules instea…
Copy-Move Forgery Detection and Question Answering for Remote Sensing Image
Faculty of Information Science and Engineering, Ocean University of China（信息科学与工程学院，中国海洋大学）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（杭州高等研究院，中国科学院大学）
RSHallu: Dual-Mode Hallucination Evaluation for Remote-Sensing Multimodal Large Language Models with Domain-Tailored Mitigation
College of Computer Science, Chongqing University（重庆大学计算机科学学院）；Heavy Rainfall Research Center of China（中国强降水研究中心）；CMA Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing Institute of Meteorological Sciences（中国气象局气候资源转化经济重点开放实验室、重庆气象科学研究院）；Chongqing Metropolitan College of Science（重庆科学理工学院）；School of Intelligence Science（智能科学学院）；College of Artificial Intelligence, Harbin Institute of Technology（人工智能学院、哈尔滨工业大学）；BNU-UIC Institute of Artificial Intelligence（北京师范大学-国际学院人工智能与未来网络研究院）；Future Networks, Beijing Normal University at Zhuhai（未来网络、北京师范大学珠海分校）
Environmental Computational Science and Earth Observation Laboratory (ECEO), EPFL（环境计算科学与地球观测实验室（ECEO），洛桑联邦理工学院（EPFL））；Laboratoire d’Informatique Paris Descartes (LIPADE), Université de Paris（巴黎笛卡尔大学信息实验室（LIPADE），巴黎大学）
Visual question answering (VQA) has recently been introduced to remote sensing to make information extraction from overhead imagery more accessible to everyone. VQA considers a question (in natural language, therefore easy to formulate) about an image and aims at providing an answer through a model based on computer vision and natural language processing methods. As such, a VQA model needs to jointly consider visual and textual features, which is frequently done through a fusion step. In this work, we study three different fusion methodologies in the context of VQA for remote sensing and analy…
GRASP: Guided Region-Aware Sparse Prompting for Adapting MLLMs to Remote Sensing
School of Computing, Kyung Hee University（京畿大学计算机学院）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；College of Computer Science and Information Engineering, Harbin Normal University（哈尔滨师范大学计算机科学与信息工程学院）；College of Professional and Continuing Education, The Hong Kong Polytechnic University（香港理工大学专业及继续教育学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）
GeoReason: Aligning Thinking And Answering In Remote Sensing Vision-Language Models Via Logical Consistency Reinforcement Learning
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；Key Laboratory of Target Cognition and Application Technology, Chinese Academy of Sciences（中国科学院目标认知与应用技术重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；Lehigh University（利哈伊大学）
For glaciologists, studying ice sheets from the polar regions is critical. With the advancement of deep learning techniques, we can now extract high-level information from the ice sheet data (e.g., estimating the ice layer thickness, predicting the ice accumulation for upcoming years, etc.). However, a vision-based conversational deep learning approach has not been explored yet, where scientists can get information by asking questions about images. In this paper, we have introduced the task of Visual Question Answering (VQA) on remote-sensed ice sheet imagery. To study, we have presented a uni…
RSVLM-QA: A Benchmark Dataset for Remote Sensing Vision Language Model-based Question Answering
School of Computer Science, University of Technology Sydney（技术悉尼大学计算机科学学院）；SEDE, University of Technology Sydney（技术悉尼大学SEDE）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China（网络与交换技术国家重点实验室，北京邮电大学，中国）；City University of HongKong, Hong Kong 999077, China（香港城市大学，中国）；School of Geographic Sciences, Hunan Normal University, Changsha 410081, China（地理科学学院，湖南师范大学，中国）
RSAdapter: Adapting Multimodal Models for Remote Sensing Visual Question Answering
Humboldt-Universität zu Berlin（柏林洪堡大学）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗森多夫亥姆霍兹研究中心）；Helmholtz Institute Freiberg for Resource Technology（弗莱贝格资源技术亥姆霍兹研究所）；Lancaster University（兰卡斯特大学）
RS-MoE: A Vision-Language Model with Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering
China Academy of Electronics and Information Technology（中国电子科技集团公司电子科学研究院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology（哈尔滨工业大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）
Seeing Clearly without Training: Mitigating Hallucinations in Multimodal LLMs for Remote Sensing
School of Computer Science, Wuhan University, China（武汉大学计算机学院）；Zhongguancun Academy, China（中关村学院）；School of Computer Science, Chongqing University, China（重庆大学计算机学院）
TinyRS-R1: Compact Multimodal Language Model for Remote Sensing
Center for the Image Analysis (OGAM) and Department of Electrical and Electronics Engineering of Middle East Technical University (METU)（图像分析中心（OGAM）和中欧技术大学（METU）电子与电气工程系）
Multimodal Large Language Models for Remote Sensing Image Understanding: Domain-Specific or General-Purpose?
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Intelligent Game and Decision Lab (IGDL)（智能游戏与决策实验室）；Yuelushan Center for Industrial Innovation（岳麓山工业创新中心）
An Efficient and Effective Encoder Model for Vision and Language Tasks in the Remote Sensing Domain
INESC-ID, Instituto Superior Técnico, University of Lisbon（INESC-ID，理工学院，里斯本大学）；Department of Computer Science, Faculty of Science and Technology, Universidade NOVA de Lisboa（计算机科学系，科学与技术学院，诺瓦大学）；School of Architecture, Civil and Environmental Engineering, EPFL（建筑、土木和环境工程学院，EPFL）
Remote Sensing Retrieval-Augmented Generation: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model
School of Cyber Science and Technology, University of Science and Technology of China（信息科学技术学院，中国科学技术大学）；China Academy of Electronics and Information Technology（电子信息技术研究院）
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）
GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing
RIKEN AIP（日本理化学研究所革新智能研究中心）；Wuhan University（武汉大学）；Linköping University（林雪平大学）；University of Tokyo（东京大学）；Nanjing University of Information Science and Technology（南京信息工程大学）
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Department of Mathematics, University of California, San Diego（加州大学圣地亚哥分校数学系）；School of Software Engineering, Xi'an Jiaotong University（西安交通大学软件工程学院）
Revisiting Change VQA in Remote Sensing with Structured and Native Multimodal Qwen Models
Computer Engineering Department, College of Computer and Information Sciences, King Saud University（计算机工程系，计算机与信息科学学院，沙特国王大学）；Applied Computer Science Department, College of Applied Computer Science, King Saud University（应用计算机科学系，应用计算机科学学院，沙特国王大学）
DescribeEarth: Describe Anything for Remote Sensing Images
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；College of Artificial Intelligence, Xi’an Jiaotong University（西安交通大学人工智能学院）；School of Computer Science and Technology and Ministry of Education Key Lab For Intelligent Networks and Network Security, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院和教育部智能网络与网络安全重点实验室）；School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University（西安交通大学数学与统计学院和教育部智能网络与网络安全重点实验室）；Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China（琶洲实验室（黄埔），广州，广东，中国）
BTCChat: Advancing Remote Sensing Bi-temporal Change Captioning with Multimodal Large Language Model
State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Aerospace Information Research Institute（航天信息研究所）；Chinese Academy of Sciences（中国科学院）；School of Geographical Sciences（地理科学学院）；Hunan Normal University（湖南师范大学）
RS-OPSD: Reliable Privileged On-Policy-Self-Distillation for Ultra-High-Resolution Remote Sensing VQA
Tsinghua University（清华大学）；Zhejiang University（浙江大学）；Central University of Finance and Economics（中央财经大学）；East China Normal University（华东师范大学）；Key Laboratory of Geographic Information Science（地理信息科学重点实验室）
Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey
Department of Aerospace Intelligent Science and Technology, School of Astronautics, Beihang University（航空航天智能科学与技术系，航天学院，北航）；Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, China（航天器设计优化与动态仿真技术重点实验室，教育部，中国）；College of Computer Science, Inner Mongolia University（计算机科学学院，内蒙古大学）；Shen Yuan Honors College of Beihang University（盛元荣誉学院，北航）
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Peking University（北京大学）；Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
