2009
DOI: 10.3390/rs1030345
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Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover

Abstract: Global land cover is one of the essential terrestrial baseline datasets available for ecosystem modeling, however uncertainty remains an issue. Tools such as Google Earth offer enormous potential for land cover validation. With an ever increasing amount of very fine spatial resolution images (up to 50 cm × 50 cm) available on Google Earth, it is becoming possible for every Internet user (including non remote sensing experts) to distinguish land cover features with a high degree of reliability. Such an approach… Show more

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Cited by 306 publications
(225 citation statements)
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References 24 publications
(28 reference statements)
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“…The weaknesses of current land-cover datasets with respect to cropland identification are slowly being rectified through evaluation and harmonization of different datasets in different situations (see ref. 25, for example). The irrigated areas are based on the Food and Agriculture Organization (FAO) Aquastat map version 4.0.1 (26).…”
Section: Methodsmentioning
confidence: 99%
“…The weaknesses of current land-cover datasets with respect to cropland identification are slowly being rectified through evaluation and harmonization of different datasets in different situations (see ref. 25, for example). The irrigated areas are based on the Food and Agriculture Organization (FAO) Aquastat map version 4.0.1 (26).…”
Section: Methodsmentioning
confidence: 99%
“…However, new approaches such as crowdsourcing (citizen observatories) have indicated that the collection of large collections of ground information is now feasible (Schuurman 2009;Heipke 2010;Goodchild 2007). Recent efforts have shown the potential to use citizen observed data to validate land cover maps (Iwao et al 2006;Fritz et al 2009). Similar efforts have the potential to provide the input to enhance our characterization of landscape structure information at larger scales.…”
Section: Ways Forwardmentioning
confidence: 99%
“…However other studies such as Over et al (2010), Haklay (2010), Girres and Touya (2010) compares OSM very favourably, in terms of geometric accuracy, against road and street network databases from National Mapping Agencies and commercial sources. Fritz et al (2009) even suggest at using OSM and other crowd-sourced VGI as an alternative approach for "validating and calibrating global land cover".…”
Section: Overview Of Related Workmentioning
confidence: 99%