FLAIR #1: semantic segmentation and domain adaptation dataset

TL;DR

This paper introduces the FLAIR #1 dataset, a high-resolution aerial imagery dataset aimed at improving semantic segmentation and domain adaptation for land cover mapping across heterogeneous French territories.

Contribution

The paper presents a new dataset designed to enhance deep learning models for land cover semantic segmentation and domain adaptation in diverse geographic contexts.

Findings

Dataset enables improved land cover classification accuracy.

Facilitates research on domain adaptation in remote sensing.

Supports development of robust AI models for heterogeneous terrains.

Abstract

The French National Institute of Geographical and Forest Information (IGN) has the mission to document and measure land-cover on French territory and provides referential geographical datasets, including high-resolution aerial images and topographic maps. The monitoring of land-cover plays a crucial role in land management and planning initiatives, which can have significant socio-economic and environmental impact. Together with remote sensing technologies, artificial intelligence (IA) promises to become a powerful tool in determining land-cover and its evolution. IGN is currently exploring the potential of IA in the production of high-resolution land cover maps. Notably, deep learning methods are employed to obtain a semantic segmentation of aerial images. However, territories as large as France imply heterogeneous contexts: variations in landscapes and image acquisition make it challenging to provide uniform, reliable and accurate results across all of France. The FLAIR-one dataset presented is part of the dataset currently used at IGN to establish the French national reference land cover map "Occupation du sol \`a grande \'echelle" (OCS- GE).