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FLAIR #2: textural and temporal information for semantic segmentation from multi-source optical imagery

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arxiv 2305.14467 v1 pith:EPZJUCCO submitted 2023-05-23 cs.CV cs.LGeess.IV

Anatol Garioud , Apolline De Wit , Marc Poupée , Marion Valette , Sébastien Giordano , Boris Wattrelos This is my paper · ORCID
classification cs.CVcs.LGeess.IV
keywords datainformationspatialdatasetflairhighimageryresolution
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The FLAIR #2 dataset hereby presented includes two very distinct types of data, which are exploited for a semantic segmentation task aimed at mapping land cover. The data fusion workflow proposes the exploitation of the fine spatial and textural information of very high spatial resolution (VHR) mono-temporal aerial imagery and the temporal and spectral richness of high spatial resolution (HR) time series of Copernicus Sentinel-2 satellite images. The French National Institute of Geographical and Forest Information (IGN), in response to the growing availability of high-quality Earth Observation (EO) data, is actively exploring innovative strategies to integrate these data with heterogeneous characteristics. IGN is therefore offering this dataset to promote innovation and improve our knowledge of our territories.

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  1. LongEarth-R1: Benchmarking and Aligning Vision-Language Models for Long-Horizon Earth Observation Reasoning

    cs.AI 2026-08 conditional novelty 6.0 of 10

    LongEarth-R1, a vision-language model trained with sequence-aware supervision and spatiotemporal GRPO, sets new state-of-the-art results on all 12 tasks of the new LongEarth-Bench for long-horizon Earth observation reasoning.

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