Description: The goal is to use aerial photos coming from drones and/or satellite images, combining them together with ground truth data, in order to teach deep learning models to correctly classify tree species in Japanese forests based on their canopy. The long-term aims are to map and quantify Japanese mixed forests, identifying invasive species and their impact to the overall flora of the areas under study. Advanced deep learning models are being used (i.e. RNN, LSTM), capturing satellite images and aerial photos from drones in different seasons of the years, allowing to address the problem with high precision.
Collaboration with: Prof. Larry Lopez, Sarah Kentsch (Yamagata University, Japan)
Techniques used: Satellite imagery, aerial imagery (UAVs, drones), deep learning, Internet of Things
Started: November, 2019
Publications: Sarah Kentsch, Savvas Karatsiolis, Andreas Kamilaris, Luca Tomhave and Maximo Larry Lopez Caceres, Identification of Tree Species in Japanese Forests based on Aerial Photography and Deep Learning, In Proc. of EnviroInfo, Nicosia, Cyprus, September 2020.