Quantifying Error in Training Data for Mapping and Monitoring the Earth System Workshop Proceedings

Given the growing use of image-interpreted training data, a dedicated effort to understand the error inherent in such data and its consequences for land cover mapping is timely. Under the auspices of a land cover mapping project supported by the Omidyar Network, the Graduate School of Geography at Clark University organized a two-day workshop - Given the growing use of image-interpreted training data, a dedicated effort to understand the error inherent in such data and its consequences for land cover mapping is timely. Under the auspices of a land cover mapping project supported by the Omidyar Network, the Graduate School of Geography at Clark University organized a two-day workshop - Jan. 8 and 9, 2019 - to address this topic. Below, are the presentations. The goals of the workshop were to: (1) Summarize the current state of knowledge on the quantification of training data errors and its impacts on machine learning-based methods for generating Earth Observation maps. (2) Identify potential sources of error in new training data streams; (3) Use case studies to quantify how training data errors impact the usability of downstream maps; (4) Define best practices for quantifying and reporting i) training data error and ii) its contribution to overall map error. The primary workshop outcome will be a peer-review paper. This site provides additional links to presentations and other resources resulting from the workshop. ...Show More

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