UAV-StrawFire

Straw burning poses a significant threat to air quality and public health, primarily due to the emissions of smoke aerosols and associated hazards. Satellite-based remote sensing techniques often have limitations such as long revisit times and low spatial resolution to detect small-scale straw-burning sites. Unmanned aerial vehicles (UAVs) with imaging sensors offer a fast, cost-effective alternative for monitoring and detecting straw fires. By leveraging high-resolution multi-modal images, UAVs can provide valuable insights into the occurrence and spread of straw fires. This paper introduces a straw fire image dataset collected using drones (UAV-StrawFire) during prescribed straw residue burning experiments in southern China. The UAV-StrawFire dataset comprises RGB and thermal infrared (TIR) images and videos of straw fires, along with annotations and labeling that enable researchers to apply detection, segmentation, and tracking algorithms effectively. (1) flame detection in aligned multi-modal frames; (2) flame segmentation to precisely determine fire boundaries, employing real-time lightweight models; (3) flame tracking to monitor the real-time progression of straw fire points.

秸秆露天焚烧会通过烟雾气溶胶排放及伴随的各类危害，对空气质量与公众健康构成严重威胁。基于卫星的遥感技术往往存在重访周期长、空间分辨率低的局限，难以探测小规模秸秆焚烧火点。搭载成像传感器的无人机（Unmanned Aerial Vehicles, UAVs）则为秸秆火情的监测与探测提供了一种快速且高性价比的替代方案。借助高分辨率多模态影像，无人机可有效获取秸秆火情发生与蔓延的关键信息。本文介绍了一套在中国南方地区开展计划性秸秆残体焚烧实验期间采集的秸秆火情影像数据集——UAV-StrawFire。该数据集包含秸秆火情的RGB影像、热红外（Thermal Infrared, TIR）影像及视频数据，同时配套标注与标签体系，可支持研究人员高效应用检测、分割与跟踪算法，具体涵盖三类任务：(1) 对齐多模态帧内的火焰检测；(2) 采用轻量化实时模型精准划定火情边界的火焰分割；(3) 实时追踪秸秆火情点位动态演变的火焰跟踪。

搜集汇总

背景与挑战

背景概述

UAV-StrawFire是一个用于稻草燃烧监测的数据集，包含RGB和热红外图像及视频，以及火焰分割和检测的标注数据。该数据集适用于农业和人工智能领域的研究，特别是无人机图像融合和深度学习应用。