Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning Safonova, Anastasiia Tabik, Siham Alcaraz Segura, Domingo Rubtsov, Alexey Maglinets, Yuriy Herrera Triguero, Francisco Multi-class classification Drone Aerial photography Siberian fir Siberia Deep learning Convolutional neural networks Forest health We are very grateful to the reviewers for their valuable comments that helped to improve the paper. We appreciate the support of a vice-director of the “Stolby” State Nature Reserve, Anastasia Knorre. We also thank two Ph.D. students Egor Trukhanov and Anton Perunov from Siberian Federal University for their help in data acquisition (aerial photography from UAV) on two research plots in 2016 and raw imagery processing. Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve “Stolby” (Krasnoyarsk, Russia). 2020-05-06T10:20:42Z 2020-05-06T10:20:42Z 2019-03-16 info:eu-repo/semantics/article Safonova, A.; Tabik, S.; Alcaraz-Segura, D.; Rubtsov, A.; Maglinets, Y.; Herrera, F. Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning. Remote Sens. 2019, 11, 643. [doi:10.3390/rs11060643] http://hdl.handle.net/10481/61827 10.3390/rs11060643 eng EC/H2020/641762 http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España MDPI