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Satellites auto detect buildings in OpenStreetMap

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Re: Satellites auto detect buildings in OpenStreetMap

#31

Post by maxerickson »

sp8962 wrote: post #25

The fallacy in the concept we're discussing here is assuming that OpenStreetMap is being held back by the speed with which we can add infrastructure geometry (I'm specifically excluding mass doodling of buildings as that is a topic on its own, and, hey, google does just fine without buildings nearly everywhere). Given reasonable quality aerial imagery (on which automatic detection a la Facebook depends too) the limit…

I would think a tool that can quickly do high quality building extraction would end up displacing the efforts to add buildings to OSM.

Such a tool would have a couple of advantages; the capture dates of the imagery used would be well known and the coverage would be consistent. The projects to add buildings are often hasty and involve lots of people of varying levels of experience (and diligence and so on).

Of course, there seems to be a widespread (and incorrect) mindset that working with OSM only is somehow vastly easier than working with OSM plus some external data. It's nice that data added to OSM automatically shows up in lots of places, but it is usually quite straightforward to use other data alongside the OSM data.

Re: Satellites auto detect buildings in OpenStreetMap

#32

Post by bpchaps »

nl wrote: post #26
bpchaps wrote: post #21

Earlier quoted context omitted.

unstructured blob: I'm working with data from a FOIA request for ~5yrs worth of parking tickets in Chicago. About 18m rows in total. The end goal is to be able to say whether a parking ticket was valid or not. It's been ongoing for about two years now, but when I have chunks of time I find myself getting back into the old code or just rewriting chunks. Part of the reason it's taking so long is that a personal require…

Given that you have a fixed area the tickets can be in, and you know every possible address, it seems like you could investigate string distance measures. It would solve your pl vs plaza vs place problem.

That's what using difflib does. Plaza vs pl vs place is just one example of many, though. There are about 1k different street names in chicago and about 18k different street names in the data. Many are missing street type and there are many street names which have different street types. So that tends to open up cans of worms. That, and street names that have 'street' in their names. Also, 'avenue' is a street name which screws things up left and right.

It's not a simple problem, and none of the popular machine learning libraries which use string distance for this sort of problem have been very effective.

Re: Satellites auto detect buildings in OpenStreetMap

#33

Post by maxerickson »

bpchaps wrote: post #32
nl wrote: post #26

Earlier quoted context omitted.

Given that you have a fixed area the tickets can be in, and you know every possible address, it seems like you could investigate string distance measures. It would solve your pl vs plaza vs place problem.

That's what using difflib does. Plaza vs pl vs place is just one example of many, though. There are about 1k different street names in chicago and about 18k different street names in the data. Many are missing street type and there are many street names which have different street types. So that tends to open up cans of worms. That, and street names that have 'street' in their names. Also, 'avenue' is a street name w…

Have you tried https://github.com/openvenues/libpostal?

Re: Satellites auto detect buildings in OpenStreetMap

#34

Post by bpchaps »

maxerickson wrote: post #33
bpchaps wrote: post #32

Earlier quoted context omitted.

That's what using difflib does. Plaza vs pl vs place is just one example of many, though. There are about 1k different street names in chicago and about 18k different street names in the data. Many are missing street type and there are many street names which have different street types. So that tends to open up cans of worms. That, and street names that have 'street' in their names. Also, 'avenue' is a street name w…

Have you tried https://github.com/openvenues/libpostal ?

Yep - it's actually been pretty invaluable in getting this project to where it is now. :) It definitely has its own flaws, but those are easy to work around.

Edit - looks like they have a similar problem from dealing with hand typed data. From their road map:

Ambiguous token classification (coming soon): e.g. "dr" => "doctor" or "drive" for an English address depending on the context. Multiclass logistic regression trained on OSM addresses, where abbreviations are discouraged, giving us many examples of fully qualified addresses on which to train

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