Building Floorspace in China: A Dataset and Learning Pipeline

by   Peter Egger, et al.
ETH Zurich

This paper provides the first milestone in measuring the floor space of buildings (that is, building footprint and height) and its evolution over time for China. Doing so requires building on imagery that is of a medium-fine-grained granularity, as longer cross-sections and time series data across many cities are only available in such format. We use a multi-class object segmenter approach to gauge the floor space of buildings in the same framework: first, we determine whether a surface area is covered by buildings (the square footage of occupied land); second, we need to determine the height of buildings from their imagery. We then use Sentinel-1 and -2 satellite images as our main data source. The benefits of these data are their large cross-sectional and longitudinal scope plus their unrestricted accessibility. We provide a detailed description of the algorithms used to generate the data and the results. We analyze the preprocessing steps of reference data (if not ground truth data) and their consequences for measuring the building floor space. We also discuss the future steps in building a time series on urban development based on our preliminary experimental results.


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