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Meet #sophiaurista from #detroit #michigan killing #raceagainstthemachine follow her @sophiaurista.com click on bio for full link to video 🔝 https://www.instagram.com/p/BqA8CXgAqrM/?utm_source=ig_tumblr_share&igshid=tg25zwsqzz3h
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A quick throwback to when one of our students challenged the automated storage and retrieval system (the "mechanical hand") at Mansueto Library.
Read about the race, and the trash-talking adversaries, here!
When your phone is on 1% and u race home to charge it and WIN! #TeamSamsung #RaceAgainstTheMachine #TechnologyShallNeverBeatMe Snapchat me: becauseimfunny. YouTube coming soon!
This project aims to automate a manual process: geographic polygon and attribute data extraction from maps including those from insurance atlases published in the 19th and early 20th centuries. Here is some background on why we're doing this and here is one of the maps we're extracting polygons from. This example map layer shows what these atlases look like once geo-rectified, i.e. geographically normalized. The New York Public Library has hundreds of atlases with tens of thousands of these sheets and there is no way we can extract data manually in a reasonable amount of time. Just so you get an idea, it tool NYPL staff coordinating a small army of volunteers three years to produce 170,000 polygons with attributes (from just four of hundreds of atlases at NYPL). It will now take a day to produce a comparable number of polygons with some basic metadata.
