map-vectorizer

Indeed! Map vectorization is one the things NYPL Labs is actively working on: https://github.com/nypl/map-vectorizer It's designed to work primarily with old fire insurance atlases (e.g. Sanborns) and is a bunch of hand-tuned heuristics. But it powers Building Inspector (http://buildinginspector.nypl.org), which is where all the data is validated consensus crowdsourcing, providing ground truth for the data coming out. Unfortunately (or perhaps fortunately if you're trying to get a Computer Science PhD) I haven't come across anyone applying deep learning to map vectorization either. Frankly, it could open up a whole new field with respect to historical mapping (among other things). Would love to talk more about it and see how deep learning could be applied here. We always structured the outputs of Building Inspector so they'd be useful tra

Grant, I'm from the team at NYPL working on the Space/Time Directory effort. This is absolutely amazing. We'd love to have you (and everyone here) help out! We've been so surprised at the excitement over the response and we've had to bump up our timeline to catch up the energy of everyone who's gotten in touch. There's a few big things we're focused on right now: * Improving performance of CV recognition of historical buildings [https://github.com/nypl/map-vectorizer] from insurance atlases. * Parsing data from historical records. We love old documents that are basically databases in print form. Old playbills, city directories, tax records; lots of tabular data with locations, people, businesses - the names of history. Getting those names out and tied to locations are what become the searchable elements for places. * Rebuilding the underl