Rapid semantic mapping: Learn environment classifiers on the fly

We propose solutions to provide unmanned aerial vehicles (UAV) with features to understand the scene below and help the operational planning. First, using a visual mapping of the environnement, interactive learning of specific targets of interest is performed on the ground control station to build semantic maps useful for planning. Then, the learned target detectors are transformed to be applied to new images captured by the UAV. On the technical side, we present: (i) an online gradient boost algorithm to interactively design context-dependent detectors; (ii) a video-domain adaptation method to use object detectors on on-board-camera images. We verify our approach on challenging data captured in real-world conditions.