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In this work, we investigate various methods to deal with semantic labelingof very high resolution multi-modal remote sensing data. Especially, we studyhow deep fully convolutional networks can be adapted to deal with multi-modaland multi-scale remote sensing data for semantic labeling. Our contributionsare threefold: a) we present an efficient multi-scale approach to leverage botha large spatial context and the high resolution data, b) we investigate earlyand late fusion of Lidar and multispectral data, c) we validate our methods ontwo public datasets with state-of-the-art results. Our results indicate thatlate fusion make it possible to recover errors steaming from ambiguous data,while early fusion allows for better joint-feature learning but at the cost ofhigher sensitivity to missing data.

Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks