# Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery

arXiv: https://arxiv.org/abs/2407.19184 · HTML: https://mlsanity.com/paper/2407.19184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 27 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [Libra R-CNN: Towards Balanced Learning for Object Detection](https://mlsanity.com/paper/1904.02701/) (2019)
- [Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training](https://mlsanity.com/paper/2004.06002/) (2020)
- [Grid R-CNN](https://mlsanity.com/paper/1811.12030/) (2018)
- [Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data](https://mlsanity.com/paper/2010.07445/) (2020)

## Abstract (arXiv)

Forest fires pose a significant threat to ecosystems, economies, and human health worldwide. Early detection and assessment of forest fires are crucial for effective management and conservation efforts. Unmanned Aerial Vehicles (UAVs) equipped with advanced computer vision algorithms offer a promising solution for forest fire detection and assessment. In this paper, we optimize an integrated forest fire risk assessment framework using UAVs and multi-stage object detection algorithms. We introduce improvements to our previous framework, including the adoption of Faster R-CNN, Grid R-CNN, Sparse R-CNN, Cascade R-CNN, Dynamic R-CNN, and Libra R-CNN detectors, and explore optimizations such as CBAM for attention enhancement, random erasing for preprocessing, and different color space representations. We evaluate these enhancements through extensive experimentation using aerial image footage from various regions in British Columbia, Canada. Our findings demonstrate the effectiveness of multi-stage detectors and optimizations in improving the accuracy of forest fire risk assessment. This research contributes to the advancement of UAV-based forest fire detection and assessment systems, enhancing their efficiency and effectiveness in supporting sustainable forest management and conservation efforts.