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In this work, we investigate the use of OpenStreetMap data for semanticlabeling of Earth Observation images. Deep neural networks have been used inthe past for remote sensing data classification from various sensors, includingmultispectral, hyperspectral, SAR and LiDAR data. While OpenStreetMap hasalready been used as ground truth data for training such networks, thisabundant data source remains rarely exploited as an input information layer. Inthis paper, we study different use cases and deep network architectures toleverage OpenStreetMap data for semantic labeling of aerial and satelliteimages. Especially , we look into fusion based architectures and coarse-to-finesegmentation to include the OpenStreetMap layer into multispectral-based deepfully convolutional networks. We illustrate how these methods can besuccessfully used on two public datasets: ISPRS Potsdam and DFC2017. We showthat OpenStreetMap data can efficiently be integrated into the vision-baseddeep learning models and that it significantly improves both the accuracyperformance and the convergence speed of the networks.

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