High quality annotations of power infrastructure in Rural Ontario

Abstract

We used the Systematic Street View Sampler (S^3) to acquire 18,883 sampled Google Street View images throughout the rural areas of southern Ontario. We then leveraged the Amazon Mechanical Turk annotation environment to obtain high confidence annotations of whether the images contain power-related infrastructure or not. This data exemplifies the joint use of S^ 3 and the Amazon MTurk framework for machine vision-related applications.

Publication
Carleton University Biomedical Informatics DataVerse