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To address climate change, accurate and automated forest cover monitoring is crucial. In this study, we propose a Convolutional Neural Network (CNN) which mimics professional interpreters' manual techniques. Using simultaneously acquired airborne images and LiDAR data, we attempt to reproduce the 3D knowledge of tree shape, which interpreters potentially make use of. Geospatial features which support interpretation are also used as inputs to the CNN. Inspired by the interpreters' techniques, wedoi:10.5194/isprs-archives-xlii-2-1091-2018 fatcat:zkyxno5jr5bs3d3r4jsujicm5m