Hi Phillip, I think Andrew, John, etc. have well covered the practical relevance to OSM. On a technical note you may consider looking into what the Facebook data team is doing: https://code.facebook.com/posts/1676452492623525/connecting-the-world-with-better-maps/
I believe a lot of it is based on building detection via machine learning. Robert On Wed, Dec 21, 2016 at 7:07 PM Andrew Buck <andrew.r.b...@gmail.com> wrote: > I agree with what the others have said so far... this probably won't be > used to feed into the main database but could have other uses. > > One place where it could be good is in very sparsely populated areas > like northern Africa, where there are huge areas of empty space with a > few isolated settlements. In the past we used micro tasking to have > users quickly scan though and find village locations and then manually > mapped the buildings using the microtask results to find the villages to > map. This worked very well and your algorithm looks robust enough to > find the majority of villages. Also in that case false positives are no > problem at all as the mapper just won't map anything when they look > there and false negatives are not such a big deal (as long as you > identify at least one building in the village the mapper can map the > rest themselves anyway). > > Lastly, this could be used to re-check already mapped villages to see if > they need to be revisited to map new buildings, etc. You could scan an > area and for each village spit out a set of numbers of how many > buildings are in both datasets, how many are only in yours, and how many > are only in OSM. Based on that we can prioritize areas to be re-looked > at by human mappers to update to the new imagery. > > So all in all this is a very useful tool to have in our toolbox, but > just not for direct inclusion into the main database. > > -AndrewBuck > > > > > On 12/19/2016 08:17 AM, Philip Hunt wrote: > > Hi all, > > > > I attended my first Humanitarian OpenStreetMap Team (HOT) mapping event > a few months ago and was interested to see how successful machine learning > would be at detecting buildings in satellite images. The results look > promising but I wanted to know if it could be useful to the community and > if it’s worth pursuing further. I thought I would post a sample of the > results and then quickly explain the process and issues. > > > > > > Results > > ——— > > > > These are the results of a test I ran on project 2101 (Rongo, Kenya - > PMI/USAID) on 1 November 2016. These images show the buildings detected by > the algorithm on the first six unstarted tasks from the project. Potential > buildings are marked with green rectangles: > > > > https://s3-eu-west-1.amazonaws.com/hot-osm-ml-test-data/2101_4.png > > https://s3-eu-west-1.amazonaws.com/hot-osm-ml-test-data/2101_5.png > > https://s3-eu-west-1.amazonaws.com/hot-osm-ml-test-data/2101_9.png > > https://s3-eu-west-1.amazonaws.com/hot-osm-ml-test-data/2101_12.png > > https://s3-eu-west-1.amazonaws.com/hot-osm-ml-test-data/2101_13.png > > https://s3-eu-west-1.amazonaws.com/hot-osm-ml-test-data/2101_14.png > > > > As you can see the initial results look promising - most of the > buildings have been detected and the false positive rate is pretty low. > > > > > > Process > > ———— > > > > I’ve been using the Viola–Jones machine learning algorithm, which > requires training to know what is and isn’t a building. Once the algorithm > is trained, it can be used to detect buildings in new images in a few > seconds. > > > > The whole process looks like this: > > > > - Get the HOT project and task data using the HOT API > > - Get the satellite imagery of the area from OSM > > - Get the nearby existing buildings from the OSM API > > - Find the existing buildings in the satellite imagery and use these to > train the algorithm > > - Run through each incomplete task in the HOT project and detect > buildings > > - Output the results as OSM XML > > - Load the output into JOSM, validate and upload to OSM > > > > > > Issues > > ——— > > > > I loaded the output of the algorithm into JOSM and completed tasks 1 and > 2 of project 2101. However it still took a bit of work to make sure the > data is good enough for OSM and I think an experienced mapper would have > taken roughly the same amount of time starting from scratch. > > > > The main issue is the algorithm can’t rotate the detected rectangle to > fit the building shape (as you can see from the example images above, none > of the rectangles are rotated). I’ve tried using methods such as line > detection to detect the building and rotate and crop the rectangle around > the edges - this worked well some of the time and other times went horribly > wrong. > > > > The second issue is false positives. While the examples above we’re > generally clean, sometimes the algorithm would think a field was a > building. Because data uploaded to OSM needs to be accurate it can take > some time checking each potential building in JOSM. > > > > Another potential issue could be training samples. When testing I > trained a new algorithm for each project, using local existing building > data from OSM as training data. The assumption here is that nearby > buildings will look like buildings in the project area and that nearby > building data is available and accurate. > > > > > > Next Steps > > ————— > > > > The Viola–Jones objection detection research paper was first published > in 2001 so the algorithm has been around for a while. Machine learning has > improved since then and neural networks are showing a lot of promise - > using these could increase the reliability and also allow the detected > rectangle to be fixed around the edge of the building - meaning a lot less > editing in JOSM. I’m also aware of similar projects, but I haven’t found > anything that’s able to detect buildings or ready for use yet: > > > > https://github.com/trailbehind/DeepOSM (find misconfigured roads in OSM) > > https://github.com/patrick-dd/landsat-landstats (predicts population > size) > > https://github.com/larsroemheld/OSM-HOT-ConvNet > > > > > > I'm not suggesting this could replace volunteers (since algorithms will > never be completely accurate), but maybe this could help speed things up or > be used to quickly estimate building locations over large areas. > > > > Please let me know your thoughts. Could this could be a useful tool for > HOT or any other volunteer organisations and is it worth taking any further? > > > > Thanks, > > > > Philip Hunt > > > > > > _______________________________________________ > > HOT mailing list > > HOT@openstreetmap.org > > https://lists.openstreetmap.org/listinfo/hot > > > > > _______________________________________________ > HOT mailing list > HOT@openstreetmap.org > https://lists.openstreetmap.org/listinfo/hot >
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