An open flame and smoke detection dataset for deep learning in remote sensing based fire detection

FASDD is a largest and most generalized Flame And Smoke Detection Dataset for object detection tasks, characterized by the utmost complexity in fire scenes, the highest heterogeneity in feature distribution, and the most significant variations in image size and shape. FASDD serves as a benchmark for developing advanced fire detection models, which can be deployed on watchtowers, drones, or satellites in a space-air-ground integrated observation network for collaborative fire warning. This endeavor provides valuable insights for government decision-making and fire rescue operations. FASDD contains fire, smoke, and confusing non-fire/non-smoke images acquired at different distances (near and far), different scenes (indoor and outdoor), different light intensities (day and night), and from various visual sensors (surveillance cameras, UAVs, and satellites). FASDD consists of three sub-datasets, a Computer Vision (CV) dataset (i.e. FASDD_CV), a Unmanned Aerial Vehicle (UAV) dataset (i.e. FASDD_UAV), and an Remote Sensing (RS) dataset (i.e. FASDD_RS). FASDD comprises 122,634 samples, with 70,581 annotated as positive samples and 52,073 labeled as negative samples. There are 113,154 instances of flame objects and 73,072 instances of smoke objects in the entire dataset. FASDD_CV contains 95,314 samples for general computer vision, while FASDD_UAV consists of 25,097 samples captured by UAV, and FASDD_RS comprises 2,223 samples from satellite imagery. FASDD_CV contains 73,297 fire instances and 53,080 smoke instances. The CV dataset exhibits considerable variation in image size, ranging from 78 to 10,600 pixels in width and 68 to 8,858 pixels in height. The aspect ratios of the images also vary significantly, ranging from 1:6.6 to 1:0.18. FASDD_UAV contains 36,308 fire instances and 17,222 smoke instances, with image aspect ratios primarily distributed between 4:3 and 16:9. In FASDD_RS, there are 2,770 smoke instances and 3,549 flame instances. The sizes of remote sensing images are predominantly around 1,000×1,000 pixels.FASDD is provided in three compressed files: FASDD_CV.zip, FASDD_UAV.zip, and FASDD_RS.zip, which correspond to the CV dataset, the UAV dataset, and the RS dataset, respectively. Additionally, there is a FASDD_RS_SWIR. zip folder storing pseudo-color images for detecting flame objects in remote sensing imagery. Each zip file contains two folders: "images" for storing the source data and "annotations" for storing the labels. The "annotations" folder consists of label files in four formats: YOLO, VOC, COCO, and TDML. The dataset is divided randomly into training, validation, and test sets, with a ratio of 1/2, 1/3, and 1/6, respectively, within each label format. In FASDD_CV, FASDD_UAV, and FASDD_RS, images and their corresponding annotation files have been individually sorted starting from 0. The flame and smoke objects in FASDD are given the labels "fire" and "smoke" for the object detection task, respectively. The names of all images and annotation files are prefixed with "Fire", "Smoke", "FireAndSmoke", and "NeitherFireNorSmoke", representing different categories for scene classification tasks.When using this dataset, please cite the following paper. Thank you very much for your support and cooperation：################################################################################使用数据集请引用对应论文，非常感谢您的关注和支持：Wang, M., Yue, P., Jiang, L., Yu, D., Tuo, T., & Li, J. (2025). An open flame and smoke detection dataset for deep learning in remote sensing based fire detection. Geo-spatial Information Science, 28(2), 511-526.################################################################################

FASDD是目前规模最大、通用性最强的火焰与烟雾检测（Flame And Smoke Detection）数据集，适用于目标检测（object detection）任务。其特点为火灾场景复杂度极高、特征分布异质性最强，且图像尺寸与形状差异最为显著。
FASDD可作为开发先进火灾检测模型的基准数据集，可部署于瞭望塔、无人机或卫星，用于空天地一体化观测网络中开展协同火灾预警。本数据集可为政府决策与火灾救援行动提供有价值的参考依据。
FASDD包含不同拍摄距离（近景与远景）、不同场景（室内与室外）、不同光照条件（白天与黑夜），以及由多种视觉传感器（监控摄像头、无人机、卫星）采集的火焰、烟雾及易混淆的非火/非烟雾图像。
FASDD包含三个子数据集，分别为计算机视觉（Computer Vision，CV）数据集（即FASDD_CV）、无人机（Unmanned Aerial Vehicle，UAV）数据集（即FASDD_UAV）以及遥感（Remote Sensing，RS）数据集（即FASDD_RS）。
FASDD总计包含122634个样本，其中正样本标注70581个，负样本标注52073个。全数据集共包含113154个火焰目标实例与73072个烟雾目标实例。
FASDD_CV包含95314个通用计算机视觉样本；FASDD_UAV包含25097个无人机采集的样本；FASDD_RS包含2223个卫星影像样本。
FASDD_CV包含73297个火焰实例与53080个烟雾实例。该计算机视觉数据集的图像尺寸差异显著，宽度范围为78至10600像素，高度范围为68至8858像素，图像宽高比差异同样显著，范围为1:6.6至1:0.18。
FASDD_UAV包含36308个火焰实例与17222个烟雾实例，其图像宽高比主要分布于4:3至16:9之间。
在FASDD_RS中，共包含2770个烟雾实例与3549个火焰实例，遥感影像的尺寸多集中于1000×1000像素左右。
FASDD以三个压缩包形式提供，分别为FASDD_CV.zip、FASDD_UAV.zip与FASDD_RS.zip，对应上述三个子数据集。此外，还包含FASDD_RS_SWIR.zip文件夹，用于存储遥感影像中火焰目标检测所需的伪彩色图像。每个压缩包均包含两个文件夹：名为“images”的文件夹用于存储源数据，名为“annotations”的文件夹用于存储标注文件。“annotations”文件夹包含四种格式的标注文件：YOLO、VOC、COCO与TDML。
该数据集按照每种标注格式，随机划分为训练集、验证集与测试集，划分比例分别为1/2、1/3与1/6。在FASDD_CV、FASDD_UAV与FASDD_RS三个子数据集中，图像及其对应的标注文件均从0开始单独排序。
FASDD中的火焰与烟雾目标在目标检测任务中分别被标注为“fire”（火焰）与“smoke”（烟雾）。所有图像与标注文件的文件名前缀分别为“Fire”、“Smoke”、“FireAndSmoke”与“NeitherFireNorSmoke”，对应场景分类任务中的不同类别。
使用本数据集时，请引用以下论文。衷心感谢您的支持与配合：王梦, 岳鹏, 姜林, 余达, 拓涛, 李骥. (2024). 面向遥感火灾检测深度学习的开放火焰与烟雾检测数据集. 《地理空间信息科学》, 1-16.

搜集汇总

背景与挑战

背景概述

FASDD是一个大规模、多样化的火焰和烟雾检测数据集，包含来自不同场景、距离和传感器的图像，适用于计算机视觉、无人机和遥感领域的火灾检测任务。数据集提供了丰富的标注信息和多种格式，支持对象检测和场景分类任务，是开发先进火灾检测模型的理想基准。