Deep Learning based Automated Forest Health Diagnosis from Aerial Images

TL;DR

This paper introduces a novel deep learning framework using Mask RCNN and transfer learning to automatically detect dead trees in aerial images, aiding early forest health assessment and fire risk prediction.

Contribution

It presents a new dataset augmentation method and compares eight fine-tuned models, achieving a mean average precision of 54% for dead tree detection.

Findings

Achieved 54% mean average precision in dead tree detection

Automated counting of dead trees as forest health indicators

Framework links tree health to environmental change and fire risk

Abstract

Global climate change has had a drastic impact on our environment. Previous study showed that pest disaster occured from global climate change may cause a tremendous number of trees died and they inevitably became a factor of forest fire. An important portent of the forest fire is the condition of forests. Aerial image-based forest analysis can give an early detection of dead trees and living trees. In this paper, we applied a synthetic method to enlarge imagery dataset and present a new framework for automated dead tree detection from aerial images using a re-trained Mask RCNN (Mask Region-based Convolutional Neural Network) approach, with a transfer learning scheme. We apply our framework to our aerial imagery datasets,and compare eight fine-tuned models. The mean average precision score (mAP) for the best of these models reaches 54%. Following the automated detection, we are able to automatically produce and calculate number of dead tree masks to label the dead trees in an image, as an indicator of forest health that could be linked to the causal analysis of environmental changes and the predictive likelihood of forest fire.