mlsanityData to 7 Oct 2026

RSVQA: Visual Question Answering for Remote Sensing Data

arXiv 2003.07333 ↗First posted 16 Mar 2020cs.CVcited by 197

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Cited by 90 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 197 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 7; 2023: 26; 2024: 38; 2025: 60; 2026: 59.

197 papers.

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  1. 2023GeoChat: Grounded Large Vision-Language Model for Remote Sensingcited by 320
  2. 2023RSGPT: A Remote Sensing Vision Language Model and Benchmarkcited by 204
  3. 2024EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domaincited by 181
  4. 2023RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensingcited by 150
  5. 2024LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Modelcited by 133
  6. 2022RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Datacited by 121
  7. 2024VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understandingcited by 116
  8. 2024SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Modelcited by 112
  9. 2024SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understandingcited by 101
  10. 2024Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performancecited by 94
  11. 2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93
  12. 2023The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shotcited by 83
38 more
  1. 2024EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialoguescited by 81
  2. 2023Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignmentcited by 71
  3. 2024TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Datacited by 69
  4. 2024Many-Shot In-Context Learning in Multimodal Foundation Modelscited by 64
  5. 2023RRSIS: Referring Remote Sensing Image Segmentationcited by 59
  6. 2023EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answeringcited by 51
  7. 2025Unified Autoregressive Visual Generation and Understanding with Continuous Tokenscited by 51
  8. 2025XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?cited by 50
  9. 2024RS-Agent: Automating Remote Sensing Tasks through Intelligent Agentcited by 46
  10. 20213D Question Answeringcited by 41
  11. 2025GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolutioncited by 41
  12. 2022EarthNets: Empowering AI in Earth Observationcited by 40
  13. 2022From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Datacited by 38
  14. 2024Assessment of Multimodal Large Language Models in Alignment with Human Valuescited by 36
  15. 2024Towards Vision-Language Geo-Foundation Model: A Surveycited by 36
  16. 2025When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token Pruningcited by 35
  17. 2025DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Responsecited by 35
  18. 2025Backdoor Cleaning without External Guidance in MLLM Fine-tuningcited by 34
  19. 2024On the Foundations of Earth and Climate Foundation Modelscited by 31
  20. 2024Large Language Models for Captioning and Retrieving Remote Sensing Imagescited by 30
  21. 2021Change Detection Meets Visual Question Answeringcited by 29
  22. 2022A Review on Methods and Applications in Multimodal Deep Learningcited by 29
  23. 2022Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networkscited by 28
  24. 2023Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forwardcited by 28
  25. 2024UrBench: A Comprehensive Benchmark for Evaluating Large Multimodal Models in Multi-View Urban Scenarioscited by 28
  26. 2021Towards a Collective Agenda on AI for Earth Science Data Analysiscited by 27
  27. 2024GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilotscited by 27
  28. 2021Recent Advances and Trends in Multimodal Deep Learning: A Reviewcited by 26
  29. 2024MMNeuron: Discovering Neuron-Level Domain-Specific Interpretation in Multimodal Large Language Modelcited by 26
  30. 2021Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directionscited by 24
  31. 2023Parameter-Efficient Transfer Learning for Remote Sensing Image-Text Retrievalcited by 24
  32. 2022AI Security for Geoscience and Remote Sensing: Challenges and Future Trendscited by 23
  33. 2024ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Modelscited by 23
  34. 2024MG-LLaVA: Towards Multi-Granularity Visual Instruction Tuningcited by 22
  35. 2023HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Imagescited by 21
  36. 2025MLLM-CL: Continual Learning for Multimodal Large Language Modelscited by 21
  37. 2022Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classificationcited by 20
  38. 2023Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images!cited by 19

50 most cited of 197 All 197 (JSON)