Deep learning for understanding satellite imagery : an experimental survey

Translating satellite imagery into maps requires intensive effort and time, especially leading to inaccurate maps of the affected regions during disaster and conflict. The combination of availability of recent datasets and advances in computer vision made through deep learning paved the way toward automated satellite image translation. To facilitate research in this direction, we introduce the Satellite Imagery Competition using a modified SpaceNet dataset. Participants had to come up with different segmentation models to detect positions of buildings on satellite images. In this work, we present five approaches based on improvements of U-Net and Mask R-Convolutional Neuronal Networks models, coupled with unique training adaptations using boosting algorithms, morphological filter, Conditional Random Fields and custom losses. The good results-as high as AP=0.937 and AR=0.959 -from these models demonstrate the feasibility of Deep Learning in automated satellite image annotation

What Data are needed for Semantic Segmentation in Earth Observation?

International audienceThis 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