FLAIR #2: textural and temporal information for semantic segmentation from multi-source optical imagery
This dataset addresses land cover mapping challenges for geospatial and environmental researchers, but it is incremental as it builds on existing data fusion approaches.
The authors tackled land cover mapping by introducing the FLAIR #2 dataset, which fuses very high-resolution aerial imagery with Sentinel-2 time series for semantic segmentation, resulting in a dataset designed to exploit textural and temporal information.
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.