# Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset

arXiv: https://arxiv.org/abs/2012.14036 · HTML: https://mlsanity.com/paper/2012.14036/

## Key facts

- Cited by 34 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 28 Dec 2020.

## Built on by (most cited first, 30 of 34)

- [HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection](https://mlsanity.com/paper/2204.03245/) (2022)
- [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)
- [CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery](https://mlsanity.com/paper/2407.17673/) (2024)
- [PhysInOne: Visual Physics Learning and Reasoning in One Suite](https://mlsanity.com/paper/2604.09415/) (2026)
- [A Survey on Infrared Image and Video Sets](https://mlsanity.com/paper/2203.08581/) (2022)
- [FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management](https://mlsanity.com/paper/2412.02831/) (2024)
- [Data Models for Dataset Drift Controls in Machine Learning With Optical Images](https://mlsanity.com/paper/2211.02578/) (2022)
- [FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion](https://mlsanity.com/paper/2403.03463/) (2024)
- [Detecting Wildfires on UAVs with Real-time Segmentation Trained by Larger Teacher Models](https://mlsanity.com/paper/2408.10843/) (2024)
- [E3-UAV: An Edge-based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2308.04774/) (2023)
- [WIT-UAS: A Wildland-fire Infrared Thermal Dataset to Detect Crew Assets From Aerial Views](https://mlsanity.com/paper/2312.09159/) (2023)
- [Fully-echoed Q-routing with Simulated Annealing Inference for Flying Adhoc Networks](https://mlsanity.com/paper/2103.12870/) (2021)
- [UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning](https://mlsanity.com/paper/2105.12823/) (2021)
- [DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition](https://mlsanity.com/paper/2410.13663/) (2024)
- [Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2306.10071/) (2023)
- [Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning](https://mlsanity.com/paper/2308.07273/) (2023)
- [Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning](https://mlsanity.com/paper/2308.12843/) (2023)
- [FlameFinder: Illuminating Obscured Fire through Smoke with Attentive Deep Metric Learning](https://mlsanity.com/paper/2404.06653/) (2024)
- [Through-Foliage Surface-Temperature Reconstruction for Early Wildfire Detection](https://mlsanity.com/paper/2511.12572/) (2025)
- [Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone Networks](https://mlsanity.com/paper/2401.08105/) (2024)
- [Streamlining Forest Wildfire Surveillance: AI-Enhanced UAVs Utilizing the FLAME Aerial Video Dataset for Lightweight and Efficient Monitoring](https://mlsanity.com/paper/2409.00510/) (2024)
- [A comprehensive GeoAI review: Progress, Challenges and Outlooks](https://mlsanity.com/paper/2412.11643/) (2024)
- [Automated Image Recognition Framework](https://mlsanity.com/paper/2506.19261/) (2025)
- [WildSmoke: Ready-to-Use Dynamic 3D Smoke Assets from a Single Video in the Wild](https://mlsanity.com/paper/2509.11114/) (2025)
- [Real-Time Wildfire Localization on the NASA Autonomous Modular Sensor using Deep Learning](https://mlsanity.com/paper/2601.14475/) (2026)
- [Analysis of Deep Learning Architectures and Efficacy of Detecting Forest Fires](https://mlsanity.com/paper/2212.04030/) (2022)
- [Obscured Wildfire Flame Detection By Temporal Analysis of Smoke Patterns Captured by Unmanned Aerial Systems](https://mlsanity.com/paper/2307.00104/) (2023)
- [Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery](https://mlsanity.com/paper/2311.17368/) (2023)
- [Unit: Building Unit Detection Dataset](https://mlsanity.com/paper/2508.03139/) (2025)
- [Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection](https://mlsanity.com/paper/2508.16739/) (2025)

## Abstract (arXiv)

Wildfires are one of the costliest and deadliest natural disasters in the US, causing damage to millions of hectares of forest resources and threatening the lives of people and animals. Of particular importance are risks to firefighters and operational forces, which highlights the need for leveraging technology to minimize danger to people and property. FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) offers a dataset of aerial images of fires along with methods for fire detection and segmentation which can help firefighters and researchers to develop optimal fire management strategies. This paper provides a fire image dataset collected by drones during a prescribed burning piled detritus in an Arizona pine forest. The dataset includes video recordings and thermal heatmaps captured by infrared cameras. The captured videos and images are annotated and labeled frame-wise to help researchers easily apply their fire detection and modeling algorithms. The paper also highlights solutions to two machine learning problems: (1) Binary classification of video frames based on the presence [and absence] of fire flames. An Artificial Neural Network (ANN) method is developed that achieved a 76% classification accuracy. (2) Fire detection using segmentation methods to precisely determine fire borders. A deep learning method is designed based on the U-Net up-sampling and down-sampling approach to extract a fire mask from the video frames. Our FLAME method approached a precision of 92% and a recall of 84%. Future research will expand the technique for free burning broadcast fire using thermal images.