用高分辨率卫星图像和无监督深度学习来表现城市建筑和自然环境
A Barbara Metzler1, Ricky Nathvani1, Viktoriia Sharmanska2
1Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, Imperial College London, London, UK.
The Science of the total environment
|June 14, 2023
概括
我们开发了一种新的深度学习方法,使用卫星图像来绘制发展中国家城市的城市环境. 这种方法有效地确定了可持续发展跟踪的独特城市特征.
科学领域:
- 城市规划和遥感技术
- 地理空间分析和人工智能
- 可持续发展研究 可持续发展研究
背景情况:
- 发展中国家的快速城市化需要及时的数据来监测城市变化.
- 对于城市环境而言,现有的数据来源往往是有限的,不频繁的或昂贵的.
- 了解建筑和自然环境的复杂相互作用对于可持续的城市发展至关重要.
研究的目的:
- 介绍和评估一种新的无监督深度聚类方法,用于使用高分辨率卫星图像对城市环境进行分类.
- 描述城市的建筑和自然特征及其与人口密度的关系.
- 评估实时城市发展跟踪方法的可扩展性和可解释性.
主要方法:
- 无监督深度聚类算法的应用在加纳阿克拉的高分辨率 (0.3米/像素) 卫星图像上.
- 基于建筑物,植被,道路和水体等特征的城市环境的分类.
- 用外部人口和环境数据对图像衍生集群进行上下文化.
主要成果:
- 深度聚类方法成功地从单独的卫星图像中识别出不同的,可解释的城市现象型.
- 集群准确地代表了城市的自然 (植被,水) 和建筑 (建筑特征,道路网络) 环境.
- 该方法揭示了城市特征和人口分布之间的关系,根据集群复杂性和规模的不同强度.
结论:
- 无监督的卫星数据深度学习为监测城市发展提供了具有成本效益和可扩展性的解决方案.
- 该方法为城市结构和土地利用提供了可解释的洞察力,特别是在传统数据稀缺的地方非常有价值.
- 这种方法支持实时跟踪可持续的城市增长和规划.
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