  This work investigates the use of deep fully convolutional neural networks
(DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially,
we train a variant of the SegNet architecture on remote sensing data over an
urban area and study different strategies for performing accurate semantic
segmentation. Our contributions are the following: 1) we transfer efficiently a
DFCNN from generic everyday images to remote sensing images; 2) we introduce a
multi-kernel convolutional layer for fast aggregation of predictions at
multiple scales; 3) we perform data fusion from heterogeneous sensors (optical
and laser) using residual correction. Our framework improves state-of-the-art
accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.
