非法建筑和拆除废物填埋场的自动识别:一种计算机视觉方法
Qiaoqiao Yong1, Huanyu Wu1, Jiayuan Wang1
1College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China; Sino-Australia Joint Research Centre in BIM and Smart Construction, Shenzhen University, Shenzhen 518060, China.
Waste management (New York, N.Y.)
|November 5, 2023
概括
本研究引入了一种深度学习方法,用于使用遥感图像自动识别非法建筑和拆除废弃物 (CDW) 垃圾填埋场,改善安全和环境监督.
科学领域:
- 环境工程 环境工程
- 地质技术工程 地质技术工程
- 遥感 遥感 遥感 遥感
背景情况:
- 建筑和拆除废物 (CDW) 垃圾填埋场的山体滑坡事故是一个全球性的问题.
- 目前用于垃圾填埋场检测的现场调查方法对于大面积的区域是低效的.
- 及时识别非法CDW垃圾填埋场对于预防事故至关重要.
研究的目的:
- 开发一种自动化方法,使用遥感技术大规模识别CDW垃圾填埋场.
- 评估深度学习模型对CDW垃圾填埋场检测的有效性.
- 为加强对CDW垃圾填埋场的监督提供一个工具.
主要方法:
- 利用遥感图像的语义细分.
- 采用深度学习模型,包括DeepLabV3+,Pspnet和HRNet.
- 在深进行了一项案例研究,以展示该方法的应用.
主要成果:
- 拟议的模型在识别CDW垃圾填埋场时实现了96.30%的准确性和74.60%的IOU.
- DeepLabV3+的表现优于PSPnet和HRNet,HRNet在优化后显示出具有竞争力的结果.
- 在深确定了52个潜在的CDW垃圾填埋场,其中包括48个涉嫌非法地点.
结论:
- 深度学习方法为大规模识别非法CDW垃圾填埋场提供了一种有效的方法.
- 这项技术有助于对CDW垃圾填埋场的监督和管理.
- 这些发现对环境保护和公共安全有重大影响.
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