# Deep Learning based Automated Forest Health Diagnosis from Aerial Images

arXiv: https://arxiv.org/abs/2010.08437 · HTML: https://mlsanity.com/paper/2010.08437/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [SkyScenes: A Synthetic Dataset for Aerial Scene Understanding](https://mlsanity.com/paper/2312.06719/) (2023)
- [Low-Cost Tree Crown Dieback Estimation Using Deep Learning-Based Segmentation](https://mlsanity.com/paper/2409.08171/) (2024)
- [Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review](https://mlsanity.com/paper/2210.03829/) (2022)
- [Manual Labelling Artificially Inflates Deep Learning-Based Segmentation Performance on RGB Images of Closed Canopy: Validation Using TLS](https://mlsanity.com/paper/2503.14273/) (2025)
- [ADA-Net: Attention-Guided Domain Adaptation Network with Contrastive Learning for Standing Dead Tree Segmentation Using Aerial Imagery](https://mlsanity.com/paper/2504.04271/) (2025)
- [Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation](https://mlsanity.com/paper/2605.02471/) (2026)

## Abstract (arXiv)

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.