基于机器学习的2021年长江沿线10米尺度的化学工业园区地图
Wenming Song1,2, Mingxing Chen3,4, Zhipeng Tang1,5
1Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China.
Scientific data
|August 3, 2024
概括
这项研究利用卫星数据和机器学习绘制长江沿线化学工业园区 (CIP) 的地图,为污染控制提供了关键信息. 新的高分辨率地图有助于环境治理和化学工业的识别.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 地理信息系统 (GIS) 是指地理信息系统.
背景情况:
- 控制工业污染是中国的一个关键的国家政策,特别是在长江沿线.
- 目前缺乏化学工业园区 (CIP) 的详细分布数据,这阻碍了有效的环境管理.
研究的目的:
- 为了生成长江沿线CIP的高分辨率分布图.
- 为该地区化学工业的良好治理提供科学基础.
- 为准确的化学工业识别提供一个模型.
主要方法:
- 利用 Sentinel-2 卫星图像和提取的光谱和纹理特征.
- 使用随机森林 (RF) 和主动学习算法进行分类.
- 集成的兴趣点 (POI) 数据来构建一个多维特征空间.
- 在谷歌地球引擎 (GEE) 上使用分区训练进行分类.
主要成果:
- 生成了长江沿线CIP的10米分辨率分布图.
- 通过技术验证实现了80%的模型准确性.
- 与传统的手动调查方法相比,显著节省了时间和成本.
结论:
- 开发的方法为绘制CIP提供了及时和成本有效的方法.
- 这项研究提供了第一个公开可用的CIPs地图,位于长江5公里范围内.
- 这些发现支持加强化学工业的污染控制和环境治理.
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