A hybrid convolutional neural network/active contour approach to segmenting dead trees in aerial imagery (Papers Track)

Jacquelyn Shelton (Hong Kong Polytechnic University); Przemyslaw Polewski (TomTom Location Technology Germany GmbH); Drew J Lipman (MITRE corp.); Marco Heurich (Bavarian Forest National Park); Wei Yao (The Hong Kong Polytechnic University)



Dead trees constitute 8% of the global carbon stocks and are decomposed by several natural factors, e.g. climate and insects. Accurate detection and modeling of dead trees is critical to understand forest ecology, the carbon cycle and decomposers. We present a novel method to construct precise shape contours of dead trees from aerial photographs by combining established convolutional neural networks with a novel active contour model in an energy minimization framework. Our approach yields greater accuracy over state-of-the-art in terms of precision, recall, and intersection over union of detected dead trees. This improved performance is essential to meet the emerging challenges caused by climate change (and other man-made perturbations to the systems) and estimate carbon stock decay rates.