# Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study

arXiv: https://arxiv.org/abs/2101.03409 · HTML: https://mlsanity.com/paper/2101.03409/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 9 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Learning to Segment Object Candidates](https://mlsanity.com/paper/1506.06204/) (2015)
- [Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classification](https://mlsanity.com/paper/1802.03518/) (2018)
- [A CNN-Based Super-Resolution Technique for Active Fire Detection on Sentinel-2 Data](https://mlsanity.com/paper/1906.10413/) (2019)

## Built on by (most cited first, 21 of 21)

- [A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management](https://mlsanity.com/paper/2401.02456/) (2024)
- [What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2306.15521/) (2023)
- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning](https://mlsanity.com/paper/2303.07035/) (2023)
- [M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data](https://mlsanity.com/paper/2406.04230/) (2024)
- [TS-SatFire: A Multi-Task Satellite Image Time-Series Dataset for Wildfire Detection and Prediction](https://mlsanity.com/paper/2412.11555/) (2024)
- [Improved Active Fire Detection using Operational U-Nets](https://mlsanity.com/paper/2304.09721/) (2023)
- [Anomaly Detection in Satellite Videos using Diffusion Models](https://mlsanity.com/paper/2306.05376/) (2023)
- [Landsat-Bench: Datasets and Benchmarks for Landsat Foundation Models](https://mlsanity.com/paper/2506.08780/) (2025)
- [Any-Optical-Model: A Universal Foundation Model for Optical Remote Sensing](https://mlsanity.com/paper/2512.17224/) (2025)
- [Analyzing Multispectral Satellite Imagery of South American Wildfires Using Deep Learning](https://mlsanity.com/paper/2201.09671/) (2022)
- [Fire360: A Benchmark for Robust Perception and Episodic Memory in Degraded 360-Degree Firefighting Videos](https://mlsanity.com/paper/2506.02167/) (2025)
- [Wildfire Forecasting with Satellite Images and Deep Generative Model](https://mlsanity.com/paper/2208.09411/) (2022)
- [Creating a Safety Assurance Case for an ML Satellite-Based Wildfire Detection and Alert System](https://mlsanity.com/paper/2211.04530/) (2022)
- [Towards AI-driven Integrative Emissions Monitoring & Management for Nature-Based Climate Solutions](https://mlsanity.com/paper/2312.11566/) (2023)
- [Real-Time Wildfire Localization on the NASA Autonomous Modular Sensor using Deep Learning](https://mlsanity.com/paper/2601.14475/) (2026)
- [Fighting Fires from Space: Leveraging Vision Transformers for Enhanced Wildfire Detection and Characterization](https://mlsanity.com/paper/2504.13776/) (2025)
- [CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities](https://mlsanity.com/paper/2506.08690/) (2025)
- [Near--Real-Time Conflict-Related Fire Detection in Sudan Using Unsupervised Deep Learning](https://mlsanity.com/paper/2512.07925/) (2025)
- [Scale-based Approach for Active Wildfire Segmentation on Satellite Imagery](https://mlsanity.com/paper/2609.01392/) (2026)
- [A Sentinel-2 benchmark dataset for deep-learning active-fire segmentation across 25 California wildfires](https://mlsanity.com/paper/2609.16199/) (2026)

## Abstract (arXiv)

Active fire detection in satellite imagery is of critical importance to the management of environmental conservation policies, supporting decision-making and law enforcement. This is a well established field, with many techniques being proposed over the years, usually based on pixel or region-level comparisons involving sensor-specific thresholds and neighborhood statistics. In this paper, we address the problem of active fire detection using deep learning techniques. In recent years, deep learning techniques have been enjoying an enormous success in many fields, but their use for active fire detection is relatively new, with open questions and demand for datasets and architectures for evaluation. This paper addresses these issues by introducing a new large-scale dataset for active fire detection, with over 150,000 image patches (more than 200 GB of data) extracted from Landsat-8 images captured around the world in August and September 2020, containing wildfires in several locations. The dataset was split in two parts, and contains 10-band spectral images with associated outputs, produced by three well known handcrafted algorithms for active fire detection in the first part, and manually annotated masks in the second part. We also present a study on how different convolutional neural network architectures can be used to approximate these handcrafted algorithms, and how models trained on automatically segmented patches can be combined to achieve better performance than the original algorithms - with the best combination having 87.2% precision and 92.4% recall on our manually annotated dataset. The proposed dataset, source codes and trained models are available on Github (https://github.com/pereira-gha/activefire), creating opportunities for further advances in the field