Citizen Science Land Cover Classification Based on Ground and Aerial Imagery

If citizen science is to be used in the context of environmental research, there needs to be a rigorous evaluation of humans' cognitive ability to interpret and classify environmental features. This research, with a focus on land cover, explores the extent to which citizen science can be used to sense and measure the environment and contribute to the creation and validation of environmental data. We examine methodological differences and humans' ability to classify land cover given different information sources: a ground-based photo of a landscape versus a ground and aerial based photo of the same location. Participants are solicited from the online crowdsourcing platform Amazon Mechanical Turk. Results suggest that across methods and in both ground-based, and ground and aerial based experiments, there are similar patterns of agreement and disagreement among participants across land cover classes. Understanding these patterns is critical to form a solid basis for using humans as sensors in earth observation.

[1]  Steffen Fritz,et al.  A campaign to collect volunteered geographic information and land cover and human impact , 2012 .

[2]  Peter F. Fisher,et al.  Integrating land-cover data with different ontologies: identifying change from inconsistency , 2004, Int. J. Geogr. Inf. Sci..

[3]  J. F. Coeterier,et al.  Dominant attributes in the perception and evaluation of the Dutch landscape , 1996 .

[4]  J. Wickham,et al.  Accuracy assessment of NLCD 2006 land cover and impervious surface , 2013 .

[5]  The Impact of Contributor Confidence, Expertise and Distance on the Crowdsourced Land Cover Data Quality , 2014 .

[6]  Z. Popovic,et al.  Crystal structure of a monomeric retroviral protease solved by protein folding game players , 2011, Nature Structural &Molecular Biology.

[7]  Giles M. Foody,et al.  Using Volunteered Data in Land Cover Map Validation: Mapping West African Forests , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Y. Yamagata,et al.  Validating land cover maps with Degree Confluence Project information , 2006 .

[9]  M. Bertolotto,et al.  Conceptualizing Landscapes : A Comparative Study of Landscape Categories with Navajo and English-speaking Participants , 2015 .

[10]  Carl F. Salk,et al.  Comparing the Quality of Crowdsourced Data Contributed by Expert and Non-Experts , 2013, PloS one.

[11]  Kevin A. Sparks,et al.  Crowdsourcing Landscape Perceptions to Validate Land Cover Classifications , 2018, Land Use and Land Cover Semantics.

[12]  D. Clery.  Galaxy evolution. Galaxy zoo volunteers share pain and glory of research. , 2011, Science.

[13]  Steffen Fritz,et al.  Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover , 2009, Remote. Sens..

[14]  Kevin Curran,et al.  OpenStreetMap , 2012, Int. J. Interact. Commun. Syst. Technol..

[15]  P. Robbins.  Beyond Ground Truth: GIS and the Environmental Knowledge of Herders, Professional Foresters, and Other Traditional Communities , 2003 .

[16]  O. Ahlqvist.  Semantic issues in Land Use and Land Cover Studies – Foundations , Application and Future Directions , 2013 .

[17]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[18]  Richard A. Wadsworth,et al.  What is Land Cover? , 2005 .

[19]  David M. Mark,et al.  Conceptualizing Landscap - A Comparative Study of Landscape Categories with Navajo and English-Speaking Participants , 2015, COSIT.

[20]  A. Klippel.  Spatial Information Theory Meets Spatial Thinking: Is Topology the Rosetta Stone of Spatio-temporal Cognition? , 2012 .

[21]  Ola Ahlqvist.  Semantic Issues in Land-Cover Analysis: Representation, Analysis, and Visualization , 2012 .

[22]  A. Klippel,et al.  Conceptualizing Landscapes , 2015, COSIT 2015.

[23]  Dominic Habron,et al.  Visual perception of wild land in Scotland , 1998 .

[24]  Ola Ahlqvist,et al.  In Search of Classification that Supports the Dynamics of Science: The FAO Land Cover Classification System and Proposed Modifications , 2008 .

[25]  Martin Rudbeck Jepsen,et al.  Semantically based reclassification of Danish land-use and land-cover information , 2013, Int. J. Geogr. Inf. Sci..

[26]  Steffen Fritz,et al.  Assessing the Accuracy of Volunteered Geographic Information arising from Multiple Contributors to an Internet Based Collaborative Project , 2013, Trans. GIS.

[27]  Alexander Zipf,et al.  Toward mapping land-use patterns from volunteered geographic information , 2013, Int. J. Geogr. Inf. Sci..

[28]  Christopher A. Barnes,et al.  Completion of the 2006 National Land Cover Database for the conterminous United States. , 2011 .

[29]  Steffen Fritz,et al.  Comparing Expert and Non-expert Conceptualisations of the Land: An Analysis of Crowdsourced Land Cover Data , 2013, COSIT.