Automated Segmentation and Classification of Aerial Forest Imagery

Abstract

Monitoring the health and safety of forests has become a rising problem with the advent of global wildfires, rampant logging, and reforestation efforts. This paper proposes a model for the automatic segmentation and classification of aerial forest imagery. The model is based on U-net architecture and relies on dice coefficients, binary cross-entropy, and accuracy as loss functions. While models without autoencoder-based structures can only reach a dice coefficient of 45%, the proposed model can achieve a dice coefficient of 79.85%. In addition, for barren adn dense forestry image classification, the proposed model can achieve 82.51%. This paper demonstrates how complex convolutional neural networks can be applied to aerial forest images to help preserve and save the forest environment.