Synopsis

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.

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