Automated Remote Sensing Forest Inventory Using Satelite Imagery
- URL: http://arxiv.org/abs/2110.08590v1
- Date: Sat, 16 Oct 2021 15:24:12 GMT
- Title: Automated Remote Sensing Forest Inventory Using Satelite Imagery
- Authors: Abduragim Shtanchaev, Artur Bille, Olga Sutyrina, Sara Elelimy
- Abstract summary: We use embeddings of tree crowns generated by Autoencoders as a data set to train classical Machine Learning algorithms.
We compare our Autoencoder (AE) based approach to traditional convolutional neural networks (CNN) end-to-end classifiers.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: For many countries like Russia, Canada, or the USA, a robust and detailed
tree species inventory is essential to manage their forests sustainably. Since
one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to
large-scale forest inventory applications, the utilization of machine learning
algorithms on satellite imagery is a rising topic of research. Although
satellite imagery quality is relatively low, additional spectral channels
provide a sufficient amount of information for tree crown classification tasks.
Assuming that tree crowns are detected already, we use embeddings of tree
crowns generated by Autoencoders as a data set to train classical Machine
Learning algorithms. We compare our Autoencoder (AE) based approach to
traditional convolutional neural networks (CNN) end-to-end classifiers.
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