mlsanityData to 14 Oct 2026

FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping

arXiv 2506.07080 ↗First posted 8 Jun 2025cs.CVcited by 6

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Abstract (arXiv)

The growing availability of high-quality Earth Observation (EO) data enables accurate global land cover and crop type monitoring. However, the volume and heterogeneity of these datasets pose major processing and annotation challenges. To address this, the French National Institute of Geographical and Forest Information (IGN) is actively exploring innovative strategies to exploit diverse EO data, which require large annotated datasets. IGN introduces FLAIR-HUB, the largest multi-sensor land cover dataset with very-high-resolution (20 cm) annotations, covering 2528 km2 of France. It combines six aligned modalities: aerial imagery, Sentinel-1/2 time series, SPOT imagery, topographic data, and historical aerial images. Extensive benchmarks evaluate multimodal fusion and deep learning models (CNNs, transformers) for land cover or crop mapping and also explore multi-task learning. Results underscore the complexity of multimodal fusion and fine-grained classification, with best land cover performance (78.2% accuracy, 65.8% mIoU) achieved using nearly all modalities. FLAIR-HUB supports supervised and multimodal pretraining, with data and code available at https://ignf.github.io/FLAIR/flairhub.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 71 of its 132 references that are in the map. First posted on arXiv 8 Jun 2025.

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71 of 132 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2026UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observationcited by 5
  2. 2025RAMEN: Resolution-Adjustable Multimodal Encoder for Earth Observationcited by 4
  3. 2026SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mappingcited by 3
  4. 2025MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Datacited by 2
  5. 2026The Global-Local loop: what is missing in bridging the gap between geospatial data from numerous communities?cited by 0
  6. 2026Who Needs Labels? Adapting Vision Foundation Models With the Metadata You Already Havecited by 0