基于改进Mask RCNN的散坟火灾隐患点自动检测与空间分析

Automatic detection and spatial analysis of fire hazard points in scattered graves using improved Mask RCNN

散坟是森林火灾风险的一个重要来源,是林业、应急、民政部门需要掌握的重要基础数据。由于缺乏足够的重视,目前国内外鲜有散坟快速检测与自动清查的研究报道。本研究提出一个基于Detectron2工具箱的Mask RCNN识别网络,利用高分辨率航拍影像和实地调查数据对中国湖南长沙县的散坟进行快速检测与自动清查。结合各种空间分布指数,如近邻指数、地理集中指数、不平衡指数和核密度指数,对散落坟墓的空间分布进行绘制和分析。结果表明:改进后的Mask RCNN网络,能够有效识别不同土地覆盖、分布密度和空间区域的散坟目标,平均F1分数分别为90.33%、92.61%和90.41%;研究发现散坟总体呈集群式-随机分布,密度为25.13座/km3,主要受地形和道路因素的影响。研究首次提出绘制散坟火灾隐患点分布数据,对全国散坟的自动检测和及时预警具有潜在的应用价值。

Scattered graves are a significant source of forest fire risk and are important data for forest,emergency,and civil affairs departments to track.However,there is a lack of research on the rapid detection and automatic inventory of scattered graves.This study proposes a Mask RCNN recognition network based on the Detectron2 toolbox to detect and inventory scattered graves in Changsha County of Hunan Province,using high-resolution aerial imagery and field survey data.The distribution of scattered graves was mapped and analyzed using various spatial distribution indices and factors such as the nearest neighbor index,geographic concentration index,unbalanced index,and kernel density index.The results showed that the improved Mask RCNN network was effective at identifying scattered graves with average F1 scores of 90.33%,92.61%,and 90.41%for different land cover,distribution densities,and research areas.Scattered graves were found to have a clustered-random distribution with a density of 25.13 graves/km 2,and were primarily influenced by topography and road factors.This study presents the first data on the distribution of fire hazard points in scattered graves and has potential applications for the automatic detection and timely warning of scattered graves nationwide.