Aerial images for pile burn detection using drones (UAVs)

Wildfires are one of the deadliest and dangerous natural disasters in the world. Wildfires burn millions of forests and they put many lives of humans and animals in danger. Predicting fire behavior can help firefighters to have better fire management and scheduling for future incidents and also it reduces the life risks for the firefighters. Recent advance in aerial images shows that they can be beneficial in wildfire studies. Among different methods and technologies for aerial images, Unmanned Aerial Vehicles (UAVs) and drones are beneficial to collect information regarding the fire. This study provides an aerial imagery dataset using drones during a prescribed pile burn in Northern Arizona, USA. This dataset consists of different repositories including raw aerial videos recorded by drones' cameras and also raw heatmap footage recorded by an infrared thermal camera. To help researchers, two well-known studies; fire classification and fire segmentation are defined based on the dataset. For approaches such as Neural Networks (NNs) and fire classification, 39,375 frames are labeled ("Fire" vs "Non-Fire") for the training phase. Also, another 8,617 frames are labeled for the test data. 2,003 frames are considered for the fire segmentation and regarding that, 2,003 masks are generated for the purpose of Ground Truth data with pixel-wise annotation.More information about this study and the two machine learning challenges that we used is available here:https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicleA sample video is available on YouTube:https://www.youtube.com/watch?v=bHK6g37_KyA

野火是全球最致命且极具破坏性的自然灾害之一。野火吞噬数百万公顷森林，将大量人类与动物的生命置于险境。精准预测野火行为，可帮助消防人员优化未来火情的管理与调度方案，同时降低消防员面临的生命风险。

近年来航空影像技术的进步表明，其在野火研究中具备可观的应用价值。在各类航空影像采集方法与技术中，无人机（Unmanned Aerial Vehicles, UAVs）在火情相关信息采集方面优势显著。

本研究针对美国亚利桑那州北部的计划性堆烧作业，构建了一套无人机航空影像数据集。该数据集包含多个数据源：一是无人机搭载可见光相机录制的原始航空视频，二是红外热像仪采集的原始热成像画面。

为便于研究者开展相关工作，本数据集基于此定义了两项经典机器学习任务：火情分类与火情分割。针对神经网络（Neural Networks, NNs）及火情分类任务，共标注39375帧图像（分为“火情”与“非火情”两类）用于模型训练，另有8617帧图像作为测试集标注数据。针对火情分割任务，选取2003帧图像进行像素级标注，并生成了对应的2003张真值掩码（Ground Truth）。

更多关于本研究及所采用的两项机器学习挑战的细节，可访问以下链接：https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle
示例视频可在YouTube平台查看：https://www.youtube.com/watch?v=bHK6g37_KyA