mlsanityData to 7 Oct 2026

FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding

arXiv 2012.02951 ↗First posted 5 Dec 2020cs.CVcited by 181

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Cited by 32 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g. image classification, segmentation, object detection), these datasets are hardly suitable for post disaster damage assessments. On the other hand, existing natural disaster datasets include mainly satellite imagery which have low spatial resolution and a high revisit period. Therefore, they do not have a scope to provide quick and efficient damage assessment tasks. Unmanned Aerial Vehicle(UAV) can effortlessly access difficult places during any disaster and collect high resolution imagery that is required for aforementioned tasks of computer vision. To address these issues we present a high resolution UAV imagery, FloodNet, captured after the hurricane Harvey. This dataset demonstrates the post flooded damages of the affected areas. The images are labeled pixel-wise for semantic segmentation task and questions are produced for the task of visual question answering. FloodNet poses several challenges including detection of flooded roads and buildings and distinguishing between natural water and flooded water. With the advancement of deep learning algorithms, we can analyze the impact of any disaster which can make a precise understanding of the affected areas. In this paper, we compare and contrast the performances of baseline methods for image classification, semantic segmentation, and visual question answering on our dataset.

Key facts. Cited by 181 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Dec 2020.

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Citing papers per year: 2021: 5; 2022: 13; 2023: 21; 2024: 38; 2025: 51; 2026: 53.

181 papers.

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  1. 2023GeoChat: Grounded Large Vision-Language Model for Remote Sensingcited by 320
  2. 2023CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentationcited by 215
  3. 2024EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domaincited by 181
  4. 2023CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoderscited by 167
  5. 2024SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understandingcited by 101
  6. 2023The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shotcited by 83
  7. 2024EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialoguescited by 81
  8. 2024TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Datacited by 69
  9. 2023EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answeringcited by 51
  10. 2025UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobilitycited by 48
  11. 2023ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modelingcited by 46
  12. 2025ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Taskscited by 41
38 more
  1. 2022RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessmentcited by 40
  2. 2022EarthNets: Empowering AI in Earth Observationcited by 40
  3. 2023VDD: Varied Drone Dataset for Semantic Segmentationcited by 39
  4. 2025BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster responsecited by 39
  5. 2024Towards Vision-Language Geo-Foundation Model: A Surveycited by 36
  6. 2025DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Responsecited by 35
  7. 2024On the Foundations of Earth and Climate Foundation Modelscited by 31
  8. 2023CPSeg: Finer-grained Image Semantic Segmentation via Chain-of-Thought Language Promptingcited by 28
  9. 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 27
  10. 2025UAVScenes: A Multi-Modal Dataset for UAVscited by 26
  11. 2022Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidentscited by 25
  12. 2025TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observationcited by 25
  13. 2023What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentationcited by 24
  14. 2023HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Imagescited by 21
  15. 2025Harnessing Large Language Models for Disaster Management: A Surveycited by 20
  16. 2024Towards Open-Vocabulary Remote Sensing Image Semantic Segmentationcited by 18
  17. 2025A Survey on Remote Sensing Foundation Models: From Vision to Multimodalitycited by 18
  18. 2025Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectivescited by 18
  19. 2023A Survey on Semi-Supervised Semantic Segmentationcited by 16
  20. 2023Kuro Siwo: 33 billion m2 under the water. A global multi-temporal satellite dataset for rapid flood mappingcited by 16
  21. 2021VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysiscited by 15
  22. 2024RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learningcited by 15
  23. 2024On Domain-Adaptive Post-Training for Multimodal Large Language Modelscited by 13
  24. 2025LISAT: Language-Instructed Segmentation Assistant for Satellite Imagerycited by 13
  25. 2025RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Eventscited by 13
  26. 2026OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agentscited by 13
  27. 2023SkyScenes: A Synthetic Dataset for Aerial Scene Understandingcited by 12
  28. 2024Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Surveycited by 12
  29. 2024Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentationcited by 12
  30. 2025EarthSynth: Generating Informative Earth Observation with Diffusion Modelscited by 12
  31. 2024UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mappingcited by 11
  32. 2024OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Constructioncited by 11
  33. 2023Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenariocited by 10
  34. 2024RescueADI: Adaptive Disaster Interpretation in Remote Sensing Images with Autonomous Agentscited by 10
  35. 2025SegEarth-R2: Towards Comprehensive Language-guided Segmentation for Remote Sensing Imagescited by 10
  36. 2024RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image Understandingcited by 9
  37. 2024CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagerycited by 9
  38. 2023Segment Any Buildingcited by 8

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