Global S&T Development Trend Analysis Platform of Resources and Environment
Spatial Characterization of Urban Land Use through Machine Learning | |
Peter Kerins; Emily Nilson; Eric Mackres; Taufiq Rashid; Brook GuzderWilliams; Steven Brumby | |
2020-06 | |
出版年 | 2020 |
国家 | 美国 |
领域 | 气候变化 ; 资源环境 |
英文摘要 | This technical note describes the data sources and methodology underpinning a computer system for the automated generation of land use/land cover (LULC) maps of urban areas. Deploying a rich taxonomy to distinguish between different types of LULC within a built-up area, rather than merely distinguishing between artificial and natural land cover, enables a huge variety of potential applications for policy, planning, and research. Applying supervised machine learning techniques to satellite imagery yielded trained algorithms that can characterize LULC over a large spatial and temporal range, while avoiding many of the onerous constraints and expenses of the historical LULC mapping process: manual identification and classification of features. This note presents the construction and results of one such set of algorithms—city-specific convolutional neural networks—used to establish the technical viability of such an approach. |
URL | 查看原文 |
来源平台 | World Resources Institute |
文献类型 | 科技报告 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/274536 |
专题 | 气候变化 资源环境科学 |
推荐引用方式 GB/T 7714 | Peter Kerins,Emily Nilson,Eric Mackres,et al. Spatial Characterization of Urban Land Use through Machine Learning,2020. |
条目包含的文件 | 条目无相关文件。 |
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