使用可解释的机器学习和卫星图像识别景观中的受保护和人为模式
Timo T Stomberg1, Johannes Leonhardt1, Immanuel Weber2
1Remote Sensing Group, Institute of Geodesy and Geoinformation, Faculty of Agriculture, University of Bonn, Bonn, Germany.
Frontiers in artificial intelligence
|December 18, 2023
概括
绘制自然和人类影响的地图对于保护至关重要. 这项研究引入了一种可解释的机器学习方法,使用卫星图像准确地绘制这些模式,帮助环境研究.
科学领域:
- 环境科学环境科学
- 遥感是一种远程传感.
- 机器学习 机器学习
背景情况:
- 准确地绘制土地覆盖的地图对于环境研究至关重要.
- 定义土地覆盖等级,如自然性和人类影响,具有挑战性,但对于生物多样性和气候监测至关重要.
- 机器学习方法越来越多地用于土地覆盖面分析.
研究的目的:
- 开发一种可解释的机器学习方法,利用卫星图像绘制自然性和人类影响的地图.
- 利用领土保护和人为区域作为自然和人类影响的代理.
- 提高机器学习模型在遥感中的可解释性和一致性.
主要方法:
- 训练一个弱监督的卷积神经网络 (CNN).
- 应用诸如Grad-CAM和闭塞灵敏度映射之类的归因方法.
- 提出了一种新的图像对图像网络架构,具有由全分辨率特征层连接的特定任务头部.
主要成果:
- 开发的方法有效地绘制了自然性和人类影响的模式.
- 分析了中间层激活,以与归属建立一致的关系.
- 发现,在不同场景中,归因是一致的,这使得大规模分析成为可能.
结论:
- 可解释机器学习方法是观察和评估自然性和领土保护的有希望的工具.
- 新型网络架构促进了详细分析和一致的大规模遥感数据分析.
- 这种方法有助于改善用于保护和环境监测的土地覆盖地图.
关键词:
人类保护公司AnthroProtect.哨兵-2 卫星 - 卫星-2 哨兵-2 卫星这些属性属性属性属性属性属性可以解释的机器学习远程传感是一种遥感技术.突出度地图 突出度地图领土保护 领土保护弱监督的学习学习.更多相关视频
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