Outlining where humans live, the World Settlement Footprint 2015
By Mattia Marconcini, A. Metz-Marconcini, Soner Üreyen, Daniela Palacios-Lopez, Wiebke Hanke, Felix Bachofer, J. Zeidler, Thomas Esch, Noel Gorelick, Ashwin Kakarla and 2 more. Scientific Data, 2019
308 citations recorded by the source corpus.
Human settlements are the cause and consequence of most environmental and societal changes on Earth; however, their location and extent is still under debate. We provide here a new 10 m resolution (0.32 arc sec) global map of human settlements on Earth for the year 2015, namely the World Settlement Footprint 2015 (WSF2015). The raster dataset has been generated by means of an advanced classification system which, for the first time, jointly exploits open-and-free optical and radar satellite imagery. The WSF2015 has been validated against 900,000 samples labelled by crowdsourcing photointerpretation of very high resolution Google Earth imagery and outperforms all other similar existing layers; in particular, it considerably improves the detection of very small settlements in rural regions and better outlines scattered suburban areas. The dataset can be used at any scale of observation in support to all applications requiring detailed and accurate information on human presence (e.g., socioeconomic development, population distribution, risks assessment, etc.). Measurement(s) global settlement extent Technology Type(s) satellite imaging • machine learning Factor Type(s) geographic location Sample Characteristic - Environment anthropogenic environment • populated place Sample Characteristic - Location Earth (planet) Measurement(s) global settlement extent Technology Type(s) satellite imaging • machine learning Factor Type(s) geographic location Sample Characteristic - Environment anthropogenic environment • populated place Sample Characteristic - Location Earth (planet) Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12424970
Subfields: Computer Vision and Pattern Recognition, Global and Planetary Change, Media Technology
View Paper: https://www.semanticscholar.org/paper/5e4f184c15d0909e5276157c1910a94da7c68f37
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Citations in the corpus
- A 30 m global map of elevation with forests and buildings removed (2022, background): “Predictor data-sets include information on population density (WorldPop [58]), travel times [59], night-time lights [60], urban building footprints (World Settlement Footprint [61]), built-up areas per capita, GDP and average greenness (GHS-UCDB R2019A [62]).”
- High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs (2021, unknown): “Another promising dataset in this context is the World Settlement Footprint, which has global coverage at 10 m resolution and to our knowledge has not yet been employed for global onshore wind potential analyses [72].”