MiniFrance

Citation Author(s):
Javiera
Castillo Navarro
ONERA
Bertrand
Le Saux
ESA/ESRIN
Alexandre
Boulch
valeo.ai
Nicolas
Audebert
CNAM
Sébastien
Lefèvre
IRISA/Université de Bretagne-Sud
Submitted by:
Javiera Castill...
Last updated:
Tue, 06/07/2022 - 07:35
DOI:
10.21227/b9pt-8x03
Data Format:
License:
5
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Abstract 

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