mlsanityData to 7 Oct 2026

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

arXiv 2110.08733 ↗First posted 17 Oct 2021cs.CVcited by 259

Not on the map. Why?

Cited by 27 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping. However, urban and rural scenes can show completely different geographical landscapes, and the inadequate generalizability of these algorithms hinders city-level or national-level mapping. Most of the existing HSR land-cover datasets mainly promote the research of learning semantic representation, thereby ignoring the model transferability. In this paper, we introduce the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) dataset to advance semantic and transferable learning. The LoveDA dataset contains 5987 HSR images with 166768 annotated objects from three different cities. Compared to the existing datasets, the LoveDA dataset encompasses two domains (urban and rural), which brings considerable challenges due to the: 1) multi-scale objects; 2) complex background samples; and 3) inconsistent class distributions. The LoveDA dataset is suitable for both land-cover semantic segmentation and unsupervised domain adaptation (UDA) tasks. Accordingly, we benchmarked the LoveDA dataset on eleven semantic segmentation methods and eight UDA methods. Some exploratory studies including multi-scale architectures and strategies, additional background supervision, and pseudo-label analysis were also carried out to address these challenges. The code and data are available at https://github.com/Junjue-Wang/LoveDA.

Key facts. Cited by 259 papers in the map (data through 7 Oct 2026). Builds on 36 of its 56 references that are in the map. First posted on arXiv 17 Oct 2021.

Builds on

36 of 56 references.

References · 36

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24 more
  1. 2019Panoptic Feature Pyramid Networkscited by 644
  2. 2014Deep Domain Confusion: Maximizing for Domain Invariancecited by 597
  3. 2018Learning to Adapt Structured Output Space for Semantic Segmentationcited by 584
  4. 2019Confidence Regularized Self-Trainingcited by 418
  5. 2018Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptationcited by 295
  6. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 276
  7. 2017LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentationcited by 212
  8. 2019iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Imagescited by 195
  9. 2018SpaceNet: A Remote Sensing Dataset and Challenge Seriescited by 178
  10. 2020Instance Adaptive Self-Training for Unsupervised Domain Adaptationcited by 177
  11. 2019Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentationcited by 151
  12. 2018Pyramid Attention Network for Semantic Segmentationcited by 148
  13. 2020Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentationcited by 142
  14. 2018Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Modelscited by 118
  15. 2019Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approachcited by 103
  16. 2020Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagerycited by 73
  17. 2019Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Imagescited by 51
  18. 2020LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagerycited by 40
  19. 2019A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenescited by 33
  20. 2018Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate modelscited by 29
  21. 2020StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardizationcited by 23
  22. 2020LandCoverNet: A global benchmark land cover classification training datasetcited by 18
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 17; 2023: 39; 2024: 51; 2025: 85; 2026: 66.

259 papers.

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  1. 2023RemoteCLIP: A Vision Language Foundation Model for Remote Sensingcited by 357
  2. 2023GeoChat: Grounded Large Vision-Language Model for Remote Sensingcited by 320
  3. 2023SegGPT: Segmenting Everything In Contextcited by 193
  4. 2024EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domaincited by 181
  5. 2022Advancing Plain Vision Transformer Towards Remote Sensing Foundation Modelcited by 149
  6. 2024VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Taskscited by 136
  7. 2023SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensingcited by 123
  8. 2024VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysiscited by 84
  9. 2023The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shotcited by 83
  10. 2022OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mappingcited by 80
  11. 2021TorchGeo: Deep Learning With Geospatial Datacited by 73
  12. 2023Semantic Segmentation using Vision Transformers: A surveycited by 73
38 more
  1. 2024HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Modelcited by 72
  2. 2024MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretrainingcited by 66
  3. 2023A Billion-scale Foundation Model for Remote Sensing Imagescited by 62
  4. 2024CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Modelcited by 54
  5. 2023EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answeringcited by 51
  6. 2023Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Modelscited by 44
  7. 2024LSKNet: A Foundation Lightweight Backbone for Remote Sensingcited by 44
  8. 2023FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagerycited by 41
  9. 2024SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentationcited by 41
  10. 2025GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolutioncited by 41
  11. 2022EarthNets: Empowering AI in Earth Observationcited by 40
  12. 2025SegEarth-R1: Geospatial Pixel Reasoning via Large Language Modelcited by 40
  13. 2023VDD: Varied Drone Dataset for Semantic Segmentationcited by 39
  14. 2023SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Modelcited by 38
  15. 2025RemoteSAM: Towards Segment Anything for Earth Observationcited by 38
  16. 2024Samba: Semantic Segmentation of Remotely Sensed Images with State Space Modelcited by 37
  17. 2023SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraintscited by 35
  18. 2023Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMscited by 34
  19. 2024RS3Mamba: Visual State Space Model for Remote Sensing Images Semantic Segmentationcited by 33
  20. 2022Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagerycited by 31
  21. 2025Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensingcited by 31
  22. 2022Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Imagescited by 30
  23. 2024RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Expertscited by 29
  24. 2023CPSeg: Finer-grained Image Semantic Segmentation via Chain-of-Thought Language Promptingcited by 28
  25. 2025Falcon: A Remote Sensing Vision-Language Foundation Model (Technical Report)cited by 28
  26. 2024CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentationcited by 27
  27. 2022Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detectioncited by 26
  28. 2024SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Imagescited by 25
  29. 2023What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentationcited by 24
  30. 2024Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Studycited by 24
  31. 2023AerialFormer: Multi-resolution Transformer for Aerial Image Segmentationcited by 23
  32. 2024GiT: Towards Generalist Vision Transformer through Universal Language Interfacecited by 23
  33. 2023SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detectioncited by 19
  34. 2024Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertaintycited by 19
  35. 2024Towards Open-Vocabulary Remote Sensing Image Semantic Segmentationcited by 18
  36. 2025A Survey on Remote Sensing Foundation Models: From Vision to Multimodalitycited by 18
  37. 2025Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectivescited by 18
  38. 2024CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentationcited by 17

50 most cited of 259 All 259 (JSON)