Journal of Remote Sensing / 2021 / Article / Tab 4

Review Article

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

Table 4

Definitions and details of the eight global 30 m impervious surface products.

Map namePeriodDefinitionMethodLiterature

MSMT_IS302015, 2020Impervious surfaces are usually covered by anthropogenic materials which prevent water from penetrating into the soil and are primarily composed of asphalt, sand and stone, concrete, bricks, glass, etc.
Dataset link:,
The Earth is divided into 950 geographical grid cells; each of these is then classified using the local adaptive random forest model and multisource and multitemporal datasets.Zhang et al. [22]
Zhang et al. [100]
NUACI1980–2015 (5-year)Artificial cover and structures such as pavement, concrete, brick, stone, and other man-made impenetrable cover types
Dataset link:
Uses the proposed NUACI combined with the GEE platform to produce global multitemporal impervious surface productsLiu et al. [19]
GAIA1985–2018 (annual)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:
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.Gong et al. [21]
FROM_GLC2010, 2015, 2017Primarily based on artificial covers such as asphalt, concrete, sand and stone, bricks, glasses, and other cover materials (e.g., concrete, cement, asphalt, and black shingles)
Dataset link:
The impervious surface class is treated as an independent layer and classified using 91,433 training samples and multitemporal Landsat imagery.Gong et al. [12]
GlobeLand302000, 2010, 2017Artificial surfaces mainly consist of urban areas, roads, rural cottages, and mines, which are primarily based on asphalt, concrete, sand and stone, bricks, glasses, and other materials.
Dataset link:
Uses a combination of pixel-based classification, multiple-scale segmentation, and manual editing based on visual comparison with auxiliary datasetsChen et al. [14]
Chen et al. [101]
HBASE/GMIS2010The HBASE definition includes all types of human built-up surfaces and the areas surrounding them that are functionally linked to those surfaces (e.g., urban green spaces).
Dataset link:
Segmentation and random forest algorithms are used to produce the initial impervious masks; postprocessing is then applied to further improve the mapping accuracy.Wang et al. [102]
Wang et al. [103]
GHSL1975, 1990, 2000, 2015The 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:
Uses a combination of data-driven and knowledge-driven reasoning by implementing supervised and unsupervised classification processesFlorczyk et al. [20]
Pesaresi et al. [52]
GUF2011–2012In 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:
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.Esch et al. [104]
Esch et al. [105]