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

arXiv: https://arxiv.org/abs/2305.14467 · HTML: https://mlsanity.com/paper/2305.14467/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [FLAIR #1: semantic segmentation and domain adaptation dataset](https://mlsanity.com/paper/2211.12979/) (2022)

## Built on by (most cited first, 9 of 9)

- [FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery](https://mlsanity.com/paper/2310.13336/) (2023)
- [FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring](https://mlsanity.com/paper/2312.10114/) (2023)
- [GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI](https://mlsanity.com/paper/2511.15658/) (2025)
- [DOFA-CLIP: Multimodal Vision-Language Foundation Models for Earth Observation](https://mlsanity.com/paper/2503.06312/) (2025)
- [FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping](https://mlsanity.com/paper/2506.07080/) (2025)
- [MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data](https://mlsanity.com/paper/2508.10894/) (2025)
- [IC-EO: Interpretable Code-based assistant for Earth Observation](https://mlsanity.com/paper/2602.00117/) (2026)
- [LongEarth-R1: Benchmarking and Aligning Vision-Language Models for Long-Horizon Earth Observation Reasoning](https://mlsanity.com/paper/2608.13344/) (2026)
- [Reuse or Relearn? A Spectral View of Earth Observation Foundation Models](https://mlsanity.com/paper/2609.32756/) (2026)

## Abstract (arXiv)

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.