Global S&T Development Trend Analysis Platform of Resources and Environment
Mapping Urban Land Use in India and Mexico using Remote Sensing and Machine Learning | |
Peter Kerins; Brook GuzderWilliams; Eric Mackres; Taufiq Rashid; Eric Pietraszkiewicz | |
2021-02 | |
出版年 | 2021 |
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 based on medium-resolution (10–30m/pixel) satellite imagery. The system and maps deploy the LULC taxonomy of the Atlas of Urban Expansion—2016 Edition: open, nonresidential, roads, and four types of residential space. We used supervised machine learning techniques to apply this taxonomy at scale. Distinguishing between recognizable, clearly defined types of land use within a built-up area, rather than merely delineating artificial land cover, enables a huge variety of potential applications for policy, planning, and research. We demonstrate the training and application of machine-learning-based algorithms to characterize LULC over a large spatial and temporal range in a way that avoids many of the onerous constraints and expenses of the traditional LULC mapping process: manual identification and classification of features. This document supersedes the previous technical note, Spatial Characterization of Urban Land Use through Machine Learning, and the methodology described here supersedes our previously reported techniques. |
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/314227 |
专题 | 气候变化 资源环境科学 |
推荐引用方式 GB/T 7714 | Peter Kerins,Brook GuzderWilliams,Eric Mackres,et al. Mapping Urban Land Use in India and Mexico using Remote Sensing and Machine Learning,2021. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论