High Quality Annotations of Power Infrastructure in Rural Ontario

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. The images in the dataset are paired: one image is forward-facing in relation to the road while it's compliment is rear-facing. These are denoted by a unique ID followed by \"A\" or \"B\". Contents: - ontario_images.tar.gz : Compressed file containing the 18,883 images used in our analysis. - classification_results.csv : The latitude, longitude, heading, and 5-class annotation results from five Amazon MTurk workers. - mapping.csv : A mapping file relating the unique image ID to the image file name. - 5 x Example_Image : Randomly selected images from the dataset