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.
FLAIR #2: textural and temporal information for semantic segmentation from multi-source optical imagery
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abstract
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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cs.AI 1years
2026 1verdicts
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LongEarth-R1: Benchmarking and Aligning Vision-Language Models for Long-Horizon Earth Observation Reasoning
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.