# Predicting Forest Fire Using Remote Sensing Data And Machine Learning

arXiv: https://arxiv.org/abs/2101.01975 · HTML: https://mlsanity.com/paper/2101.01975/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 52 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [FireNet: Real-time Segmentation of Fire Perimeter from Aerial Video](https://mlsanity.com/paper/1910.06407/) (2019)

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

- [ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery](https://mlsanity.com/paper/2201.11192/) (2022)
- [Scalable Methods for Brick Kiln Detection and Compliance Monitoring from Satellite Imagery: A Deployment Case Study in India](https://mlsanity.com/paper/2402.13796/) (2024)
- [Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management](https://mlsanity.com/paper/2310.01593/) (2023)
- [STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2406.04035/) (2024)
- [CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities](https://mlsanity.com/paper/2506.08690/) (2025)
- [Exploring State-of-the-art models for Early Detection of Forest Fires](https://mlsanity.com/paper/2511.20096/) (2025)

## Abstract (arXiv)

Over the last few decades, deforestation and climate change have caused increasing number of forest fires. In Southeast Asia, Indonesia has been the most affected country by tropical peatland forest fires. These fires have a significant impact on the climate resulting in extensive health, social and economic issues. Existing forest fire prediction systems, such as the Canadian Forest Fire Danger Rating System, are based on handcrafted features and require installation and maintenance of expensive instruments on the ground, which can be a challenge for developing countries such as Indonesia. We propose a novel, cost-effective, machine-learning based approach that uses remote sensing data to predict forest fires in Indonesia. Our prediction model achieves more than 0.81 area under the receiver operator characteristic (ROC) curve, performing significantly better than the baseline approach which never exceeds 0.70 area under ROC curve on the same tasks. Our model's performance remained above 0.81 area under ROC curve even when evaluated with reduced data. The results support our claim that machine-learning based approaches can lead to reliable and cost-effective forest fire prediction systems.