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Remote Sens. 2016, 8(10), 807;

Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation

Food and Agricultural Organization of the United Nations, Forestry Department, Rome 00154, Italy
United Nations Development Programme, Bureau for Policy and Programme Support, New York, NY 10017, USA
School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
Landscape Ecology and Plant Production Systems Unit, Université Libre de Bruxelles, Brussels 1050, Belgium
Google, Mountain View, CA 94043, USA
Papua New Guinea Forest Authority, Boroko 111, Papua New Guinea
Author to whom correspondence should be addressed.
Academic Editors: Chandra Giri, James Campbell, Clement Atzberger and Prasad S. Thenkabail
Received: 2 June 2016 / Revised: 11 September 2016 / Accepted: 22 September 2016 / Published: 28 September 2016
(This article belongs to the Special Issue Monitoring of Land Changes)
Full-Text   |   PDF [4996 KB, uploaded 8 October 2016]   |  


Collect Earth is a free and open source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). Built on Google desktop and cloud computing technologies, Collect Earth facilitates access to multiple freely available archives of satellite imagery, including archives with very high spatial resolution imagery (Google Earth, Bing Maps) and those with very high temporal resolution imagery (e.g., Google Earth Engine, Google Earth Engine Code Editor). Collectively, these archives offer free access to an unparalleled amount of information on current and past land dynamics for any location in the world. Collect Earth draws upon these archives and the synergies of imagery of multiple resolutions to enable an innovative method for land monitoring that we present here: augmented visual interpretation. In this study, we provide a full overview of Collect Earth’s structure and functionality, and we present the methodology used to undertake land monitoring through augmented visual interpretation. To illustrate the application of the tool and its customization potential, an example of land monitoring in Papua New Guinea (PNG) is presented. The PNG example demonstrates that Collect Earth is a comprehensive and user-friendly tool for land monitoring and that it has the potential to be used to assess land use, land use change, natural disasters, sustainable management of scarce resources and ecosystem functioning. By enabling non-remote sensing experts to assess more than 100 sites per day, we believe that Collect Earth can be used to rapidly and sustainably build capacity for land monitoring and to substantively improve our collective understanding of the world’s land use and land cover. View Full-Text
Keywords: land monitoring; augmented visual interpretation; assessment; land use; land use change; very high resolution imagery; open source; Google Earth; Collect Earth land monitoring; augmented visual interpretation; assessment; land use; land use change; very high resolution imagery; open source; Google Earth; Collect Earth

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

  • Externally hosted supplementary file 1
    Description: For users who wish to learn more about Collect Earth or use the software for a land assessment, please see the following supplementary materials: 1. Collect Earth User Manual (version 1) (available online: 2. Collect Earth installation file for Windows operating systems (available online: 3. Collect Earth installation file for Mac operating systems (available online: 4. Collect Earth - Papua New Guinea customization (CEP) file

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Bey, A.; Sánchez-Paus Díaz, A.; Maniatis, D.; Marchi, G.; Mollicone, D.; Ricci, S.; Bastin, J.-F.; Moore, R.; Federici, S.; Rezende, M.; Patriarca, C.; Turia, R.; Gamoga, G.; Abe, H.; Kaidong, E.; Miceli, G. Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation. Remote Sens. 2016, 8, 807.

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