MiniFrance

The Dataset

We introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite.

MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images,; it is varied, covering 16 conurbations in France, with various climates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics. Nevertheless, the most distinctive quality of MiniFrance is being the only dataset in the field especially designed for semi-supervised learning: it contains labeled and unlabeled images in its training partition, which reproduces a life-like scenario.

The Team

• Javiera Castillo Navarro, javiera.castillo_navarro@onera.fr

• Bertrand Le Saux, bls@ieee.org

• Alexandre Boulch, alexandre.boulch@valeo.com

• Nicolas Audebert, nicolas.audebert@cnam.fr

• Sébastien Lefèvre, sebastien.lefevre@irisa.fr