Paper Info
Title
Citizen Science Land Cover Classification Based on Ground and Aerial Imagery.
Abstract
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.
Year
DOI
Venue
2015
10.1007/978-3-319-23374-1_14
COSIT
Keywords
Field
DocType
Land cover,Citizen science,Classification
Data science,Crowdsourcing,Remote sensing,Amazon rainforest,Artificial intelligence,Citizen science,Earth observation,Environmental data,Land cover,Geography,Aerial imagery,Machine learning
Conference
Volume
ISSN
Citations 
9368
0302-9743
2
PageRank 
References 
Authors
0.48
8
4
Name
Order
Citations
PageRank
Kevin Sparks120.48
Alexander Klippel248349.70
Jan Oliver Wallgrün323319.29
David M. Mark4963130.62