
NYC Space/Time Directory - jcolman
http://spacetime.nypl.org/
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GrantS
So for anyone interested in this, I did my PhD thesis in computer vision on
automatically reconstructing 3D models of cities as they change over time
purely from historical and modern photos -- you can see my results for lower
Manhattan (1928-2010) in these slides from CVPR 2010:
[http://www.cc.gatech.edu/~phlosoft/files/schindler10cvpr_sli...](http://www.cc.gatech.edu/~phlosoft/files/schindler10cvpr_slides.pdf)

We were also able to estimate the date of historical photos fairly accurately
by first incorporating them into a 3D reconstruction and then reasoning about
the visibility of structures in the scene.

Some videos of the time-varying 3D point cloud here:
[http://www.cc.gatech.edu/~phlosoft/](http://www.cc.gatech.edu/~phlosoft/)

That page also has an interactive demo of a unified 3D model of Atlanta from
1864 to the 2000s based purely on image-based reconstruction. Here's a photo
of Atlanta from the civil war (1864) with a modern skyline rendered into it by
projecting the reconstructed building geometry and textures from modern photos
into the recovered camera position of the civil war era photo:
[http://4d-cities.cc.gatech.edu/atlanta/img/atlanta1864_2008....](http://4d-cities.cc.gatech.edu/atlanta/img/atlanta1864_2008.jpg)

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tinkerdol
Really interesting! Could you further explain how you grouped points into
buildings (as shown on slide 10)? I would imagine that different sections of
the city have different distance thresholds, for instance. Were they
determined by hand or automatically?

~~~
GrantS
The distance threshold was determined by hand on a per scene basis, and then
we find connected components in the graph of neighboring 3D points, so it's a
bit of a hack, but there are two important things to note. First, we can get
away with an overly permissive threshold because we also require that any two
grouped points are also observed at the same time in at least N images --
meaning SIFT features were detected for both 3D points in the same image (so
they therefore exist at the same point in time). This filters out lots of
spurious groupings. Second, this approach was inspired by super-pixels, which
is an OVER-segmentation of an image into groups of pixels that are somewhat
coherent -- each super-pixel probably lives on the same semantic object in the
world, but they are by no means complete. Still, it's massively better than
reasoning about individual pixels (or individual points).

So we err on the side of dividing single buildings up into multiple semantic
objects. If our data included detailed reconstructions of the streets between
each building, then the whole thing might be connected and we'd need more
criteria to separate them out -- we do automatically estimate a ground plane
so that's one way: just ignore everything near the ground for grouping
purposes.

There's slightly more detail in the paper:
[http://www.cc.gatech.edu/~phlosoft/files/schindler10cvpr.pdf](http://www.cc.gatech.edu/~phlosoft/files/schindler10cvpr.pdf)

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rmxt
I find this fascinating, and I think it's a great reminder of what public
institutions are capable of, especially since they are oftentimes the sole
stewards of these sorts of information.

Aside: I am really interested in contributing to these sorts of efforts,
except, ideally, at a level higher than a mechanical turk. [0] Might anyone be
able to suggest self-directed learning resources (Coursera courses, ebooks,
websites, open source projects, etc.) in the realm of geographically-oriented
web development or the latest in geographic/GIS data wrangling? Any
suggestions for languages to learn or frameworks to become familiar with?
Thanks.

[0] [http://buildinginspector.nypl.org/](http://buildinginspector.nypl.org/)

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rememberlenny
One thing to keep in mind is that they recently won the Knight Foundation
prototype fund grant. These public institutions are only capable of these
amazing projects, when they are funded.

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Intermernet
Many (many) years ago I was briefly involved in this:
[http://sydney.edu.au/arts/timemap/](http://sydney.edu.au/arts/timemap/)

It was a larger scale, lower resolution version of this. The PoC demo was to
demonstrate the borders of the Mongol empire over time. It was, as far as I
can tell, a few years ahead of it's time in 1997(ish).

It was also the first time I got to use a proper SGI workstation. Damn, those
things could make every other computer you used feel like a Jalopy.

EDIT: This has since been bundled into the "Heurist" project:
[http://heuristnetwork.org/](http://heuristnetwork.org/)

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apaprocki
I always wondered if you could from the ground up visualize, delineate, and
track ethnic neighborhoods in NYC without human input. All of the historic
naturalization documents and ship manifests have been digitized and OCR'd now
and include country of origin and nationality. By tracking the addresses from
naturalization and census forms, a computer should be able to build a really
accurate map of how neighborhoods changed and shifted from the 19th century
through the early 20th up until the data becomes private.

For example, everyone in NYC knows that "Little Italy" is really a shadow of
its former self and is mostly "Chinatown" now. If all the data was processed,
you could visualize changes like that on a map over time and increase accuracy
by adding more data sets (e.g. business directories, also in City archives).

