Artificial impervious areas are mainly man-made structures that are composed of any material that impedes the natural infiltration of water into the soil. They include roofs, paved surfaces, and major road surfaces mainly found in human settlements. Dataset link: http://data.ess.tsinghua.edu.cn/gaia.html
The Earth is divided into 583 geographical grid cells; the “exclusion-inclusion” and “temporal check” algorithms are then used to generate the final impervious areas.
The built-up area class is defined as the union of all the spatial units collected by the specific sensor, containing a building or part of it; human settlements are made by population and physical infrastructures. Dataset link: https://ghsl.jrc.ec.europa.eu/datasets.php
Uses a combination of data-driven and knowledge-driven reasoning by implementing supervised and unsupervised classification processes
In the SAR-based GUF approach, the representation of built-up areas might significantly differ from the definition of built-up areas derived from optical data… Some structures (roads, parking lots, runways of airports, etc.) are not assigned as built-up areas in the GUF since they do not show any vertical component. Dataset link: https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-9628
An unsupervised classification scheme is used to identify impervious surfaces based on the backscattering amplitude and texture; the results are used together with postediting and mosaicking to produce the final urban footprint products.