Abstract

This paper explores different aspects of semantic segmentation of remote sensing data using deep neural networks. Learning with deep neural networks was revolutionized by the creation of ImageNet. Remote sensing benefited of these new techniques, however Earth Observation (EO) datasets remain small in comparison. In this work, we investigate how we can progress towards the ImageNet of remote sensing. In particular, two questions are addressed in this paper. First, how robust are existing supervised learning strategies with respect to data volume? Second, which properties are expected from a large-scale EO dataset? The main contributions of this work are: (i) a strong robustness analysis of existing supervised learning strategies with respect to remote sensing data, (ii) the introduction of a new, large-scale dataset named MiniFrance.

Citation

@inproceedings{castillo2019data,
  title={What Data are needed for Semantic Segmentation in Earth Observation?},
  author={Castillo-Navarro, Javiera and Audebert, Nicolas and Boulch, Alexandre and Le Saux, Bertrand and Lef{\`e}vre, S{\'e}bastien},
  booktitle={2019 Joint Urban Remote Sensing Event (JURSE)},
  pages={1--4},
  year={2019},
  organization={IEEE}
}