Conflation of expert and crowd reference data to validate global binary thematic maps

Waldner François, Schucknecht Anne, Lesiv Myroslava, Gallego Javier, See Linda, Pérez-Hoyos Ana, d'Andrimont Raphaël, de Maet Thomas, Laso Bayas Juan Carlos, Fritz Steffen, Leo Olivier, Kerdiles Hervé, Díez Mónica, Van Tricht Kristof, Gilliams Sven, Shelestov Andrii, Lavreniuk Mykola, Simoes Margareth, Ferraz Rodrigo P.D., Bellon De La Cruz Beatriz, Bégué Agnès, Hazeu Gerard, Stonacek Vaclav, Kolomaznik Jan, Misurec Jan, Verón Santiago R., de Abelleyra Diego, Plotnikov Dmitry, Mingyong Li, Singha Mrinal, Patil Prashant, Zhang Miao, Defourny Pierre. 2019. Conflation of expert and crowd reference data to validate global binary thematic maps. Remote Sensing of Environment, 221 : 235-246.

Quartile : Outlier, Sujet : ENVIRONMENTAL SCIENCES / Quartile : Q1, Sujet : REMOTE SENSING / Quartile : Outlier, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Géographie-Aménagement-Urbanisme-Architecture

Résumé : With the unprecedented availability of satellite data and the rise of global binary maps, the collection of shared reference data sets should be fostered to allow systematic product benchmarking and validation. Authoritative global reference data are generally collected by experts with regional knowledge through photo-interpretation. During the last decade, crowdsourcing has emerged as an attractive alternative for rapid and relatively cheap data collection, beckoning the increasingly relevant question: can these two data sources be combined to validate thematic maps? In this article, we compared expert and crowd data and assessed their relative agreement for cropland identification, a land cover class often reported as difficult to map. Results indicate that observations from experts and volunteers could be partially conflated provided that several consistency checks are performed. We propose that conflation, i.e., replacement and augmentation of expert observations by crowdsourced observations, should be carried out both at the sampling and data analytics levels. The latter allows to evaluate the reliability of crowdsourced observations and to decide whether they should be conflated or discarded. We demonstrate that the standard deviation of crowdsourced contributions is a simple yet robust indicator of reliability which can effectively inform conflation. Following this criterion, we found that 70% of the expert observations could be crowdsourced with little to no effect on accuracy estimates, allowing a strategic reallocation of the spared expert effort to increase the reliability of the remaining 30% at no additional cost. Finally, we provide a collection of evidence-based recommendations for future hybrid reference data collection campaigns.

Mots-clés Agrovoc : cartographie de l'utilisation des terres, cartographie de l'occupation du sol, analyse de données, données spatiales, Observation satellitaire, photo-interprétation, approche participative

Mots-clés libres : Cropland, Crowdsourcing, Mapping, Validation

Classification Agris : U30 - Méthodes de recherche
A01 - Agriculture - Considérations générales
C30 - Documentation et information

Champ stratégique Cirad : CTS 5 (2019-) - Territoires

Agences de financement européennes : European Commission, European Research Council

Programme de financement européen : FP7

Projets sur financement : (EU) Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM, (EU) Harnessing the power of crowdsourcing to improve land cover and land-use information

Auteurs et affiliations

• Waldner François, UCL (BEL) - auteur correspondant
• Schucknecht Anne, European Commission-Joint Research Centre (ITA)
• Lesiv Myroslava, International Institute for Applied System Analysis (AUS)
• Gallego Javier, European Commission-Joint Research Centre (ITA)
• See Linda, International Institute for Applied System Analysis (AUS)
• Pérez-Hoyos Ana, European Commission-Joint Research Centre (ITA)
• d'Andrimont Raphaël, UCL (BEL)
• de Maet Thomas, UCL (BEL)
• Laso Bayas Juan Carlos, International Institute for Applied System Analysis (AUS)
• Fritz Steffen, International Institute for Applied System Analysis (AUS)
• Leo Olivier, European Commission-Joint Research Centre (ITA)
• Kerdiles Hervé, European Commission-Joint Research Centre (ITA)
• Díez Mónica, Deimos Imaging (ESP)
• Van Tricht Kristof, VITO Remote Sensing (BEL)
• Gilliams Sven, VITO Remote Sensing (BEL)
• Shelestov Andrii, National Technical University of Ukraine (UKR)
• Lavreniuk Mykola, National Technical University of Ukraine (UKR)
• Simoes Margareth, EMBRAPA (BRA)
• Ferraz Rodrigo P.D., EMBRAPA (BRA)
• Bellon De La Cruz Beatriz, CIRAD-ES-UMR TETIS (FRA)
• Bégué Agnès, CIRAD-ES-UMR TETIS (FRA)
• Hazeu Gerard, Wageningen Environmental Research (NLD)
• Stonacek Vaclav, Gisat (CZE)
• Kolomaznik Jan, Gisat (CZE)
• Misurec Jan, Gisat (CZE)
• Verón Santiago R., INTA (ARG)
• de Abelleyra Diego, INTA (ARG)
• Plotnikov Dmitry, Russian Academy of Sciences (RUS)
• Mingyong Li, CAAS (CHN)
• Singha Mrinal, CAAS (CHN)
• Patil Prashant, CAAS (CHN)
• Zhang Miao, Academy of Sciences (CHN)
• Defourny Pierre, UCL (BEL)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/590246/)

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