Joint Learning from Earth Observation and OpenStreetMap Data to Get
Faster Better Semantic Maps

Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps

17 May 2017

Nicolas Audebert

Bertrand Le Saux

Sébastien Lefèvre

Papers citing "Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps"

6 / 6 papers shown

Title
MapFormer: Boosting Change Detection by Using Pre-change Information Maximilian Bernhard Niklas Strauß Matthias Schubert 27 6 0 31 Mar 2023
PP-LinkNet: Improving Semantic Segmentation of High Resolution Satellite Imagery with Multi-stage Training An Tran Ali Zonoozi Jagannadan Varadarajan Hannes Kruppa SSeg 14 14 0 14 Oct 2020
SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation Qinghui Liu Michael C. Kampffmeyer Robert Jenssen Arnt-Børre Salberg GNN SSeg 20 10 0 03 Sep 2020
Machine-learned Regularization and Polygonization of Building Segmentation Masks Stefano Zorzi K. Bittner Friedrich Fraundorfer GAN 9 58 0 24 Jul 2020
OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing John E. Vargas-Muñoz Shivangi Srivastava D. Tuia A. X. Falcão 13 104 0 13 Jul 2020
Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images Qinghui Liu Michael C. Kampffmeyer Robert Jenssen Arnt-Børre Salberg 8 14 0 30 Aug 2019