在印度班加罗尔使用深度学习和无人机摄影仪测量对花岩石采石场的监测
Oussama Himmy1, Thanh Thi Nguyen1,2, Prem Jose Vazhacharickal3
1Organic Plant Production and Agroecosystems Research in the Tropics and Subtropics, Universität Kassel, Witzenhausen, Germany.
PloS one
|November 6, 2025
概括
这项研究引入了一个新的框架,使用深度学习和卫星图像来监测印度班加罗尔的花岩采石. 开发的系统准确地绘制了采石场的足迹和量化体积,有助于环境管理.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
- 人工智能的人工智能
背景情况:
- 开采花岩在经济上至关重要,但会造成重大环境和社会问题,包括土地退化和污染.
- 班加罗尔的快速城市化增加了对花岩的需求,引发了对环境可持续性和资源开采影响的担忧.
- 现有的监测方法对于城市扩张的规模和速度及其资源需求是不够的.
研究的目的:
- 开发和验证一个创新的多模式框架,用于监测班加罗尔的花岩采石活动.
- 将深度学习与卫星和无人机数据相结合,用于精确的采石区分和体积量化量化.
- 为快速城市化地区的环境管理和监督提供可行的证据.
主要方法:
- 用Sentinel-2图像进行采石区分,对五种卷积神经网络 (CNN) 架构 (U-Net,PSPNet,DeepLabV3+,FCN,EMANet) 进行基准测试.
- 使用运动多视频立体声 (SfM-MVS) 摄像度的无人机结构进行精确的体积计算.
- 实施卫星到无人机的数据处理管道,以实现自动化,可扩展的映射和量化.
主要成果:
- 在独立审计中,PSPNet在采石区分方面表现最强 (F1=93.4%,IOU=87.6%).
- 该框架成功确定了227个已确认的采石场,占地740公.
- 在一个地点的无人机摄影仪测量结果显示,挖掘总体为9,280,051立方米,准确度高 (97.2%的验收率).
结论:
- 开发的卫星到无人机管道使得花岩采石操作的自动化和可扩展监控成为可能.
- 这种方法为环境管理,监管监督和可持续资源开采提供了关键数据.
- 该框架为评估城市资源开采的社会生态后果提供了可靠的解决方案.
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