72 citations · 287 across the 33 of their papers we have counts for
4 papers · 1 filter
Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation
Veda Sunkara, Matthew Purri, Bertrand Le Saux +1
To address the mounting destruction caused by floods in climate-vulnerable regions, we propose Street to Cloud, a machine learning pipeline for incorporating crowdsourced ground tr…
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study
Javiera Castillo-Navarro, Bertrand Le Saux, Alexandre Boulch +2
The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms. Indeed, raw image data are abundant whi…
Interactive Learning for Semantic Segmentation in Earth Observation
Gaston Lenczner, Adrien Chan-Hon-Tong, Nicola Luminari +2
Dense pixel-wise classification maps output by deep neural networks are of extreme importance for scene understanding. However, these maps are often partially inaccurate due to a v…
DISIR: Deep Image Segmentation with Interactive Refinement
Gaston Lenczner, Bertrand Le Saux, Nicola Luminari +2
This paper presents an interactive approach for multi-class segmentation of aerial images. Precisely, it is based on a deep neural network which exploits both RGB images and annota…