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License: MIT License
might make sense to have multiple models. eg easy/hard is a second question
maybe spin up an osm export and query postgis?
Must have:
Must not have:
Only train on images that pass all the other filters for a realistic set
Assign an order to the answers in each region. Store the current number per region in localstorage. Also display challenge #n in the header
assign a shortcode to each region. could just be a single letter for now
once we've worked out all the issues we could just have a single process that does all the steps in order as it indexes regions
maybe find a region-independent difficulty metric, maybe make region a feature
we could use it to draw a circle around the point, and maybe calculate the score distance based on it? maybe also in the scoring stage? it depends on the distribution in our dataset. see https://www.hackerfactor.com/blog/index.php?/archives/722-GPS-Mapping.html
flickr.photos.search returns a maximum of 4,000. Right now I request in 300 days chunks assuming for my regions that'll probably be enough and if it isn't making the interval not line up with seasons means it'll wrap over the time period giving me an ok distribution.
This probably isn't that much of an issue in practice as my regions are maxing out around 40,000 pictures, or an average of ~1400 per chunk.
But a better approach would be to break out of the inner loop at 4,000 pictures and then start at the last seen.
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