Re: [OSM-talk] Building Detection using Machine Learning
On 25.12.16 00:58, Martin Koppenhoefer wrote: sent from a phone On 24 Dec 2016, at 19:57, Oleksiy Muzalyev wrote: example, I would like to be able to hide in the JOSM existing already power-lines, roads, paths, etc. in order to map farlmland, woods, grassland, etc. Perhaps, it is possible in JOSM, but I could not find it yet. if you hold ctrl while clicking (when drawing a new way), josm won't connect to existing ways like powerlines or roads. You can also hide specific features, it's called "filter" cheers, Martin Filter is exactly what I wanted. I can select now power=line, power=tower, boundary=administrative, etc., and then hide them. And consequently I can work on landuse & natural without all these ways interfering. Thank you. Best regards, Oleksiy ___ talk mailing list talk@openstreetmap.org https://lists.openstreetmap.org/listinfo/talk
Re: [OSM-talk] Building Detection using Machine Learning
sent from a phone > On 24 Dec 2016, at 19:57, Oleksiy Muzalyev > wrote: > > example, I would like to be able to hide in the JOSM existing already > power-lines, roads, paths, etc. in order to map farlmland, woods, grassland, > etc. Perhaps, it is possible in JOSM, but I could not find it yet. if you hold ctrl while clicking (when drawing a new way), josm won't connect to existing ways like powerlines or roads. You can also hide specific features, it's called "filter" cheers, Martin ___ talk mailing list talk@openstreetmap.org https://lists.openstreetmap.org/listinfo/talk
Re: [OSM-talk] Building Detection using Machine Learning
It reminds me how in 60s and 70s it was widely believed that the computers will be doing text translation instead of human translators. We realize now, fifty years later, that is actually a hard problem. And it is still impossible to translate a novel or a poem by a computer program alone. I would be very surprised if digitizing landuse from satellite images could be done by a robot. Even an automatic extraction of an image from a background in the product photography does not work reliably. And it is an easier task as in product photography it is possible to control light, background color, etc. In fact it is mostly done manually even though there are numerous programs and Photoshop plugins for it, which kind of work in some circumstances. New products for product photography & e-commerce will be appearing endlessly. But we do not have that much landuse. The Earth surface will not be growing, and there will be no other habitable planets in the near future. In my opinion, a human, especially who knows the land, should be participating in mapping landuse. But certainly, if a breakthrough happens in a self-learning neural network technology then the situation will change, and not only in mapping and translation; it will be a new brave world. What I would like to have however now is the better tools for landuse & natural. For example, I would like to be able to hide in the JOSM existing already power-lines, roads, paths, etc. in order to map farlmland, woods, grassland, etc. Perhaps, it is possible in JOSM, but I could not find it yet. Best regards, Oleksiy On 24.12.16 18:21, Christian Quest wrote: One example: OpenSolarMap... We first start by crowdsourcing building roof orientations using a very simple webapp (no need to register, open to anybody). When enough contribution match they are considered OK (at least 3 more than all other contributions). Then, these contributions were used to train a neural network. Then the nueral network was used to classify other roofs... and the result has been put back as robot contribution to the crowdsourcing webapp counting for 1 or 2 contributions depending on the level of confidence (raw data is also available for download). In all cases, there is always at least one human contribution, before putting anything back to OSM. It is also interesting to compare when human and robot do not agree ;) Links... http://opensolarmap.org/ https://github.com/opensolarmap Next step is to use the same technique on other kind of challenges, like: - landuse boundaries (to speedup/simplify Corine Land cover import improvements) - check road alignment with aerial imagery on "old" OSM traced contributions etc... The potential of deep learning mixed with human contributions can give very good things if done properly. -- Christian Quest - OpenStreetMap France ___ talk mailing list talk@openstreetmap.org https://lists.openstreetmap.org/listinfo/talk ___ talk mailing list talk@openstreetmap.org https://lists.openstreetmap.org/listinfo/talk
Re: [OSM-talk] Building Detection using Machine Learning
One example: OpenSolarMap... We first start by crowdsourcing building roof orientations using a very simple webapp (no need to register, open to anybody). When enough contribution match they are considered OK (at least 3 more than all other contributions). Then, these contributions were used to train a neural network. Then the nueral network was used to classify other roofs... and the result has been put back as robot contribution to the crowdsourcing webapp counting for 1 or 2 contributions depending on the level of confidence (raw data is also available for download). In all cases, there is always at least one human contribution, before putting anything back to OSM. It is also interesting to compare when human and robot do not agree ;) Links... http://opensolarmap.org/ https://github.com/opensolarmap Next step is to use the same technique on other kind of challenges, like: - landuse boundaries (to speedup/simplify Corine Land cover import improvements) - check road alignment with aerial imagery on "old" OSM traced contributions etc... The potential of deep learning mixed with human contributions can give very good things if done properly. 2016-12-22 17:48 GMT+01:00 Mikel Maron : > > Frederik, all > > > an editor plugin were to help the mapper trace buildings that the > mapper identifies or at least individually verifies, that would probably > be ok > > This feels like the consensus across the board -- machine learning has > potential to be useful when integrated into a human editor workflow. Maybe > we can work on guidelines that encapsulates this. With something written > up, we'll be able to stop "spinning wheels" on whether this is useful or > not, and focus on experimenting and implementing promising approaches. > > -Mikel > > * Mikel Maron * +14152835207 @mikel s:mikelmaron > > > On Wednesday, December 21, 2016 7:59 PM, Frederik Ramm < > frede...@remote.org> wrote: > > > > Hi, > > On 12/22/2016 01:10 AM, john whelan wrote: > > Do we have any guidelines in the wiki etc? > > Nothing specific, no. > > Automated editing and/or import guidelines would apply to any such > process and I would ask everyone who overhears discussions about > "uploading" machine-detected data to OSM to point this out to those > discussing. We've already had to revert a couple hundred thousand such > edits (roads though, not buildings). > > If, OTOH, an editor plugin were to help the mapper trace buildings that > the mapper identifies or at least individually verifies, that would > probably be ok, at least until HOT trains an army of monkeys with > typewriters, er keyboards, to rubber-stamp everything the algorithm puts > out ;) > > More generally speaking, in my opinion the human-centered aspect of > mapping is a key property that sets us apart from other map databases. > You can safely assume that any algorithm we can run to detect buildings, > Google can run 1000 times faster and with a fraction of the error rate, > leading to 1000 times more and 10 times better data of that kind than we > can accumulate. This is not a field in which we can, or should attempt > to, compete. > > Bye > Frederik > > -- > Frederik Ramm ## eMail frede...@remote.org ## N49°00'09" E008°23'33" > > > ___ > talk mailing list > talk@openstreetmap.org > https://lists.openstreetmap.org/listinfo/talk > > > > ___ > talk mailing list > talk@openstreetmap.org > https://lists.openstreetmap.org/listinfo/talk > > -- Christian Quest - OpenStreetMap France ___ talk mailing list talk@openstreetmap.org https://lists.openstreetmap.org/listinfo/talk
Re: [OSM-talk] Building Detection using Machine Learning
Frederik, all > an editor plugin were to help the mapper trace buildings that the mapper >identifies or at least individually verifies, that would probably be ok This feels like the consensus across the board -- machine learning has potential to be useful when integrated into a human editor workflow. Maybe we can work on guidelines that encapsulates this. With something written up, we'll be able to stop "spinning wheels" on whether this is useful or not, and focus on experimenting and implementing promising approaches. -Mikel * Mikel Maron * +14152835207 @mikel s:mikelmaron On Wednesday, December 21, 2016 7:59 PM, Frederik Ramm wrote: Hi, On 12/22/2016 01:10 AM, john whelan wrote: > Do we have any guidelines in the wiki etc? Nothing specific, no. Automated editing and/or import guidelines would apply to any such process and I would ask everyone who overhears discussions about "uploading" machine-detected data to OSM to point this out to those discussing. We've already had to revert a couple hundred thousand such edits (roads though, not buildings). If, OTOH, an editor plugin were to help the mapper trace buildings that the mapper identifies or at least individually verifies, that would probably be ok, at least until HOT trains an army of monkeys with typewriters, er keyboards, to rubber-stamp everything the algorithm puts out ;) More generally speaking, in my opinion the human-centered aspect of mapping is a key property that sets us apart from other map databases. You can safely assume that any algorithm we can run to detect buildings, Google can run 1000 times faster and with a fraction of the error rate, leading to 1000 times more and 10 times better data of that kind than we can accumulate. This is not a field in which we can, or should attempt to, compete. Bye Frederik -- Frederik Ramm ## eMail frede...@remote.org ## N49°00'09" E008°23'33" ___ talk mailing list talk@openstreetmap.org https://lists.openstreetmap.org/listinfo/talk ___ talk mailing list talk@openstreetmap.org https://lists.openstreetmap.org/listinfo/talk
Re: [OSM-talk] Building Detection using Machine Learning
and that makes a lot of sense. Thanks John On 21 December 2016 at 19:58, Frederik Ramm wrote: > Hi, > > On 12/22/2016 01:10 AM, john whelan wrote: > > Do we have any guidelines in the wiki etc? > > Nothing specific, no. > > Automated editing and/or import guidelines would apply to any such > process and I would ask everyone who overhears discussions about > "uploading" machine-detected data to OSM to point this out to those > discussing. We've already had to revert a couple hundred thousand such > edits (roads though, not buildings). > > If, OTOH, an editor plugin were to help the mapper trace buildings that > the mapper identifies or at least individually verifies, that would > probably be ok, at least until HOT trains an army of monkeys with > typewriters, er keyboards, to rubber-stamp everything the algorithm puts > out ;) > > More generally speaking, in my opinion the human-centered aspect of > mapping is a key property that sets us apart from other map databases. > You can safely assume that any algorithm we can run to detect buildings, > Google can run 1000 times faster and with a fraction of the error rate, > leading to 1000 times more and 10 times better data of that kind than we > can accumulate. This is not a field in which we can, or should attempt > to, compete. > > Bye > Frederik > > -- > Frederik Ramm ## eMail frede...@remote.org ## N49°00'09" E008°23'33" > > ___ > talk mailing list > talk@openstreetmap.org > https://lists.openstreetmap.org/listinfo/talk > ___ talk mailing list talk@openstreetmap.org https://lists.openstreetmap.org/listinfo/talk
Re: [OSM-talk] Building Detection using Machine Learning
Hi, On 12/22/2016 01:10 AM, john whelan wrote: > Do we have any guidelines in the wiki etc? Nothing specific, no. Automated editing and/or import guidelines would apply to any such process and I would ask everyone who overhears discussions about "uploading" machine-detected data to OSM to point this out to those discussing. We've already had to revert a couple hundred thousand such edits (roads though, not buildings). If, OTOH, an editor plugin were to help the mapper trace buildings that the mapper identifies or at least individually verifies, that would probably be ok, at least until HOT trains an army of monkeys with typewriters, er keyboards, to rubber-stamp everything the algorithm puts out ;) More generally speaking, in my opinion the human-centered aspect of mapping is a key property that sets us apart from other map databases. You can safely assume that any algorithm we can run to detect buildings, Google can run 1000 times faster and with a fraction of the error rate, leading to 1000 times more and 10 times better data of that kind than we can accumulate. This is not a field in which we can, or should attempt to, compete. Bye Frederik -- Frederik Ramm ## eMail frede...@remote.org ## N49°00'09" E008°23'33" ___ talk mailing list talk@openstreetmap.org https://lists.openstreetmap.org/listinfo/talk
[OSM-talk] Building Detection using Machine Learning
Do we have any guidelines in the wiki etc? I'm not intending doing any but the topic has come up once or twice and has currently been raised in the HOT mailing list. I'm almost certain that as the accuracy improves so the topic will come up again and we should at least have some guidelines in the wiki about it if they aren't already there. Thoughts? Thanks Cheerio John Quote from the HOT mailing list. > 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 ___ talk mailing list talk@openstreetmap.org https://lists.openstreetmap.org/listinfo/talk