Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 295

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Mapping urban areas on a global scale: which of the eight maps now available is more accurate?

Potere, David, Schneider, Annemarie, Angel, Shlomo, Civco, Daniel L.·2009·International Journal of Remote Sensing

Abstract

Eight groups from government and academia have created 10 global maps that offer a ca 2000 portrait of land in urban use. Our initial investigation found that their estimates of the total amount of urban land differ by as much as an order of magnitude (0.27–3.52 ×106 km2). Since it is not possible for these heterogeneous maps to all represent urban areas accurately, we undertake the first global accuracy assessment of these maps using a two-tiered approach that draws on a stratified random sample of 10 000 high-resolution Google Earth validation sites and 140 medium-resolution Landsat-based city maps. Employing a wide range of accuracy measures at different spatial scales, we conclude that the new MODIS 500 m resolution global urban map has the highest accuracy, followed by a thresholded version of the Global Impervious Surface Area map based on the Night-time Lights and LandScan datasets.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/01431160903121134

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Cited by

See all 295

Metadata

Authors
Potere, D., Schneider, A., Angel, S., & Civco, D. L.
Venue
International Journal of Remote Sensing
Published
2009-11-27
Volume
30
Issue
24
Pages
6531-6558
ISSN
  • 0143-1161 (ISSN-L)
  • 1366-5901
OpenAlex
W1999697646
Cited by
295
References
2
FWCI
9.12
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Mapping urban areas on a global scale: which of the eight maps now available is more accurate? https://app.cochat.ai/papers/10.1080/01431160903121134
The paper
Potere, D., Schneider, A., Angel, S., & Civco, D. L. (2009). Mapping urban areas on a global scale: which of the eight maps now available is more accurate? International Journal of Remote Sensing. https://doi.org/10.1080/01431160903121134

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.