Estimating the global distribution of field size using crowdsourcing von Lesiv, Myroslava, Laso Bayas, Juan Carlos, See, Linda, Duerauer, Martina, Dahlia, Domian, Durando, Neal, Hazarika, Rubul, Kumar Sahariah, Parag, Vakolyuk, Mar'yana, Blyshchyk, Volodymyr, Bilous, Andrii, Perez‐Hoyos, Ana, Gengler, Sarah, Prestele, Reinhard, Bilous, Svitlana, Akhtar, Ibrar ul Hassan, Singha, Kuleswar, Choudhury, Sochin Boro, Chetri, Tilok, Malek, Žiga, Bungnamei, Khangsembou, Saikia, Anup, Sahariah, Dhrubajyoti, Narzary, William, Danylo, Olha, Sturn, Tobias, Karner, Mathias, McCallum, Ian, Schepaschenko, Dmitry, Moltchanova, Elena, Fraisl, Dilek, Moorthy, Inian, Fritz, Steffen

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How many people need to classify the same image? A method for optimizing volunteer contributions in binary geographical classifications von Salk, Carl, Moltchanova, Elena, See, Linda, Sturn, Tobias, McCallum, Ian, Fritz, Steffen

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Lessons learned in developing reference data sets with the contribution of citizens: the Geo-Wiki experience von See, Linda, Bayas, Juan Carlos Laso, Lesiv, Myroslava, Schepaschenko, Dmitry, Danylo, Olga, McCallum, Ian, Dürauer, Martina, Georgieva, Ivelina, Domian, Dahlia, Fraisl, Dilek, Hager, Gerid, Karanam, Santosh, Moorthy, Inian, Sturn, Tobias, Subash, Anto, Fritz, Steffen

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Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in a Land-Cover Classification Task von Salk, Carl, Sturn, Tobias, See, Linda, Fritz, Steffen

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Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data von Lesiv, Myroslava, See, Linda, Laso Bayas, Juan Carlos, Sturn, Tobias, Schepaschenko, Dmitry, Karner, Mathias, Moorthy, Inian, McCallum, Ian, Fritz, Steffen

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Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology von Bayas, Juan Carlos Laso, See, Linda, Fritz, Steffen, Sturn, Tobias, Perger, Christoph, Duerauer, Martina, Karner, Mathias, Moorthy, Inian, Schepaschenko, Dmitry, Domian, Dahlia

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Collecting volunteered geographic information from the Global Navigation Satellite System (GNSS): experiences from the CAMALIOT project von See, Linda, Soja, Benedikt, Kłopotek, Grzegorz, Sturn, Tobias, Weinacker, Rudi, Karanam, Santosh, Georgieva, Ivelina, Pan, Yuanxin, Crocetti, Laura, Rothacher, Markus, Navarro, Vicente, Fritz, Steffen, McCallum, Ian

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Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App von Laso Bayas, Juan Carlos, See, Linda, Bartl, Hedwig, Sturn, Tobias, Karner, Mathias, Fraisl, Dilek, Moorthy, Inian, Busch, Michaela, van der Velde, Marijn, Fritz, Steffen

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Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information von Orduna-Cabrera, Fernando, Sandoval-Gastelum, Marcial, McCallum, Ian, See, Linda, Fritz, Steffen, Karanam, Santosh, Sturn, Tobias, Javalera-Rincon, Valeria, Gonzalez-Navarro, Felix F.

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A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia von Hadi, Yowargana, Ping, Zulkarnain, Muhammad Thoha, Mohamad, Fathir, Goib, Bunga K., Hultera, Paul, Sturn, Tobias, Karner, Mathias, Dürauer, Martina, See, Linda, Fritz, Steffen, Hendriatna, Adis, Nursafingi, Afi, Melati, Dian Nuraini, Prasetya, F. V. Astrolabe Sian, Carolita, Ita, Kiswanto, Firdaus, Muhammad Iqbal, Rosidi, Muhammad, Kraxner, Florian

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An Experimental Framework for Integrating Citizen and Community Science into Land Cover, Land Use, and Land Change Detection Processes in a National Mapping Agency von Olteanu-Raimond, Ana-Maria, Jolivet, Laurence, Van Damme, Marie-Dominque, Royer, Timothée, Fraval, Ludovic, See, Linda, Sturn, Tobias, Karner, Mathias, Moorthy, Inian, Fritz, Steffen

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A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project von Kłopotek, Grzegorz, Pan, Yuanxin, Sturn, Tobias, Weinacker, Rudi, See, Linda, Crocetti, Laura, Awadaljeed, Mudathir, Rothacher, Markus, McCallum, Ian, Fritz, Steffen, Navarro, Vicente, Soja, Benedikt

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Harnessing the power of volunteers, the internet and Google Earth to collect and validate global spatial information using Geo-Wiki von See, Linda, Fritz, Steffen, Perger, Christoph, Schill, Christian, McCallum, Ian, Schepaschenko, Dmitry, Duerauer, Martina, Sturn, Tobias, Karner, Mathias, Kraxner, Florian, Obersteiner, Michael

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A global dataset of crowdsourced land cover and land use reference data von Fritz, Steffen, See, Linda, Perger, Christoph, McCallum, Ian, Schill, Christian, Schepaschenko, Dmitry, Duerauer, Martina, Karner, Mathias, Dresel, Christopher, Laso-Bayas, Juan-Carlos, Lesiv, Myroslava, Moorthy, Inian, Salk, Carl F., Danylo, Olha, Sturn, Tobias, Albrecht, Franziska, You, Liangzhi, Kraxner, Florian, Obersteiner, Michael

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Assessing quality of volunteer crowdsourcing contributions: lessons from the Cropland Capture game von Salk, Carl F., Sturn, Tobias, See, Linda, Fritz, Steffen, Perger, Christoph

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Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data von Pan, Yuanxin, Klopotek, Grzegorz, Crocetti, Laura, Weinacker, Rudi, Sturn, Tobias, See, Linda, Dick, Galina, Möller, Gregor, Rothacher, Markus, McCallum, Ian, Navarro, Vicente, Soja, Benedikt

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Limitations of Majority Agreement in Crowdsourced Image Interpretation von Salk, Carl F, Sturn, Tobias, See, Linda, Fritz, Steffen

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Using Citizen Science to Help Monitor Urban Landscape Changes and Drive Improvements von Wannemacher, Karin, Birli, Barbara, Sturn, Tobias, Stiles, Richard, Moorthy, Inian, See, Linda, Fritz, Steffen

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Volunteer classifications of images from the Cropland Capture game von Sturn, Tobias, Fritz, Steffen, McCallum, Ian, See, Linda, Salk, Carl

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Crowdsourcing public perceptions of urban green space quality: A case study of Rembrandt park in Amsterdam von Schrammeijer, Bep, Sturn, Tobias, Moorthy, Inian

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