6 ms·

AI is supercharging the creation of maps around the world

167 points by infodocket 7y ago

michannne 7y ago
Projects like these highlight that as ML becomes more and more complex, a gap grows larger and larger which must be filled by manual or semi-manual labor. If it isn't a team of volunteers combing the rendered results for errors, it's users who have succumbed to those errors and leave feedback in the hopes the system improves. That gets me wondering - is the future of AI really just a semi-autonomous twilight zone where cheap / free labor augments an already faulty system? If not, what possible application is there for an expensive and closed automated system which works 100% requiring no human input, when other options are cheaper and leave clear directions for improvement?
- mikepurvis 7y ago
  Surely there's a spectrum here? Conventional, manually-made maps are full of errors too, and there's no army of low-paid workers combing over those— nope, they're published as they are, and it's up to end users to report problems. The better the map maker is (whether it's an ML black box or a team of human experts), the fewer errors there are, and either way, one expects an upward quality trend over time.
- tschwimmer 7y ago
  Yes and no. There is a huge explosion of 'Ops Plus' startups that take an existing manual process and then build some basic tooling around it (with or without any substantial ML component). There are mild to moderate efficiency gains coming from this, but a lot of their valuation is coming from a bet that in the future they'll be able to fully automate the system and reap efficiency gains. In practice, almost nobody is even thinking about building a fully automated process for every case. The reason is simple: automating the first 60% of work takes x effort, automating the next 30% takes 10x, and automating the next 9% takes 100x and operating the final 1% is essentially impossible. So if you came to the table with the goal of 100% automation right out the gate you'd spent 10 years developing something with little to show. I think full automation of some systems is possible, but is actually blocked by generational norms. By and large the systems that "Ops Plus" startups are attempting to automate were designed by people who are not digital natives. They're not illiterate, but things like instant messaging, async communication and and structured data are not natural primitives for them. I'm not saying everyone in the Fortnite generation is a master data modeller, but I think that when they join the workforce they'll set up systems that are much more feasible to automate.
  - nicolaskruchten 7y ago
    I’m interested in learning more about this “Ops Plus” term but it doesn’t seem very google friendly... Is this a term of art in some circles?
    - tschwimmer 7y ago
      I actually just made it up. Companies that come to mind are Scale.ai (doing data labelling stuff for ML that historically would have been done by outsourcing companies, etc), Flexport (freight forwarding, traditionally done via spreadsheets and emails), Checkr (background checks), Atrium (legal services), Oscar/Clover (health insurance), Cadre (real estate investing). There are tons and tons of them in the recruiting (really sourcing) space too: I'd say Triplebyte is an Ops Plus company, as is Sourceress and a couple more.
  - iguy 7y ago
    What you describe is pretty much exactly parallel to the automation of physical work. Having a steam engine gets you a long way, then the first few robots a bit further... there's no particular reason to want a factory with zero employees (except the watchman and his dog...) unless you want it to work on Mars or something.
- tomrod 7y ago
  > If it isn't a team of volunteers combing the rendered results for errors, it's users who have succumbed to those errors and leave feedback in the hopes the system improves. This is the idea behind GANs, as it stands.
rihegher 7y ago
They say it uses a deep neural network but we don't know this is a convnet or another type of NN. Anyone has more technical infos about it?
- gwern 7y ago
  This is semantic segmentation, so it has to be a convnet. I've never heard of trying to do semantic segmentation any other way, and a prototype of another approach wouldn't go into production at scale. OP links to https://ai.facebook.com/blog/mapping-roads-through-deep-learning-and-weakly-supervised-training https://ai.facebook.com/blog/mapping-roads-through-deep-lear... which says that it's D-LinkNet specifically: http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w4/Zhou_D-LinkNet_LinkNet_With_CVPR_2018_paper.pdf http://openaccess.thecvf.com/content_cvpr_2018_workshops/pap... (more or less a Unet).
  - joshvm 7y ago
    I don't disagree, but I'd be careful with that statement. A huge amount of landcover segmentation in remote sensing still relies on simple models - either linear regression (thresholds) or classical machine learning like random forests or SVMs. For a lot of cases, these techniques will get you 90% of the way and it's very rare to have ground truth data that is accurate enough that you can measure the difference with any real degree of confidence. A big problem in the field is the lack of good (public) ground truth. There's so little hand labelled data to work with that without humans in the loop it's extremely difficult to validate the results meaningfully (unless you have an army of staff to do it). With something like roads you could also have heuristics about what a road looks like and where it goes (e.g. it's a continuous thin line), which can help condition things. I've seen a lot of papers which are applying deep learning for semantic segmentation for satellite mapping, but they evaluate on very limited datasets, they attempt to regress to simpler models without realising it (e.g. trying to predict a linear model), or they leak train and test data and report amazing results because they randomly split data from the same region. I'm not saying that convnets aren't better than simpler models, but particularly for satellite imaging I'd take them with a pinch of salt and see what the improvement from a baseline method is. If you look at a random sampling of papers from the DeepGlobe competition, almost none of them provide the results from e.g. a cheap linear SVM. Fun side note - several existing "famous" datasets generalise poorly to the developing world because most of the imagery is from the developed world (and even more specifically the West) and infrastructure looks totally different.
yorwba 7y ago
OpenStreetMap contributors weren't happy about the quality of tags when the import started: https://forum.openstreetmap.org/viewtopic.php?id=63456 https://forum.openstreetmap.org/viewtopic.php?id=63456 Facebook wasn't the only company adding low-quality data: https://forum.openstreetmap.org/viewtopic.php?id=64430 https://forum.openstreetmap.org/viewtopic.php?id=64430 (Previous discussion on HN: https://news.ycombinator.com/item?id=18723138 https://news.ycombinator.com/item?id=18723138 ) This article's quotes of what appear to be OpenStreetMap representatives are generally positive, so maybe that means they fixed all the problems they caused.
- thomersch_ 7y ago
  Nope, they just cherry-picked someone who is positive, which is not the case for most mappers.
- tmcw 7y ago
  OpenStreetMap contributors haven't been happy with anything, for the last 15 years.
  - tuukkah 7y ago
    There are many positive developments such as good quality satellite imagery and goverment geographic data being offered to OpenStreetMap contributors for integration. An up-to-date aerial image of a city does wonders to the mapping experience. Mapillary's service complements OSM nicely as well as provides an invaluable on-the-ground data source. There's so many happy things in the community I can't possibly list them all.
    - habnds 7y ago
      Agreed, Tranport for London recently built an enormous database of cycling infrastructure to be integrated into OSM. It's an exciting project/collaboration. https://wiki.openstreetmap.org/wiki/TfL_Cycling_Infrastructure_Database https://wiki.openstreetmap.org/wiki/TfL_Cycling_Infrastructu...
  - andygates 7y ago
letcree 7y ago
Very cool that they are integrating it with HOT tasking manager and making it easy for anyone to use the editor with the ML-generated proposed objects in several countries. I think the ML has been there for a couple years but currently it's not easy to take advantage of it. Hopefully they eventually release the ML pipeline itself as well. Their RapiD editor has some similarities to a research project I was involved with: https://mapster.csail.mit.edu/maid.html https://mapster.csail.mit.edu/maid.html (https://www.youtube.com/watch?v=i-6nbuuX6NY https://www.youtube.com/watch?v=i-6nbuuX6NY vs https://tech.fb.com/wp-content/uploads/2019/07/add_ML_road.gif.-1.gif https://tech.fb.com/wp-content/uploads/2019/07/add_ML_road.g...)
workingpatrick 7y ago
My first thought is wondering what data FB hopes to acquire from making this tool available.
- rainyMammoth 7y ago
  exactly my thought as well. Whenever Facebook releases something we should all directly worry about what their real motives are.
  - photojosh 7y ago
    They need good quality maps. I worry that they'll try to embrace and extend in some way.
- letcree 7y ago
  Their motivation is self-serving but I don't think it's so nefarious. They use OpenStreetMap in check-in posts to display map data around locations that Facebook users have visited, so improving OpenStreetMap in turn improves the quality of this feature.
- zyang 7y ago
  User location data is useless without the substrate of a proper map.
- Groxx 7y ago
  Training data from OSM volunteers, at the very least. Probably both cheaper and higher quality than e.g. mechanical turk.
- gwern 7y ago
  FB is far from the only big tech company which has sponsored OSM as a competitor to Google Maps (Microsoft/Facebook/DigitalGlobe/Telenav/FourSquare/Craigslist have all sponsored OSM to some degree; Apple, of course, went its own way and created Apple Maps). It's a reaction to Google Maps: a monopoly on high-quality up-to-date global maps with business location is dangerous to everyone else, as a chokepoint on mobile applications. It's less about 'acquiring data' and more about not being extorted by GM. Classic 'commoditize your complement' dynamics: https://www.gwern.net/Complement https://www.gwern.net/Complement
Avamander 7y ago
I guess it's nice in those areas with low contribution precentage. But I suspect in many cases and definetly in Estonia there's a national high quality map database that could be used to augment existing maps instead. I'm wondering though, has anyone attempted to do so, quick Google search reveals nothing.
- photojosh 7y ago
  Depends on whether the owner of said high quality map database will license it for inclusion in OSM. My understanding, from following the Australian OSM mailing list, is that it takes an individual to pursue this with a government agency, which is a ton of work, and often you'll just get a 'no'.
- rmc 7y ago
  It is possible to "import" other data into OSM. However it's very tricky. Licencing/legal issues aside, it can be very technologically challenging to merge another database into OSM. I suggest posting to the OSM talk@ mailing list, or if there's a local estonian one too
m4eta 7y ago
Maybe AI can help with Lyft/Uber. I'm sort of tired of being dropped off in alleyways.
- jdblair 7y ago
  The great thing with Lyft/Uber is that I'm in the car and I can say "please take me around front to the main entrance, not here in the alleyway."
  - andygates 7y ago
    That works for regular cabs too! /s
- rmc 7y ago
  This is the point about OSM. Local people can just fix it. We don't need _artifical_ intellegence, when we have real intelligence.
dr_faustus 7y ago
Is it open source? It is somehow implied, but I can't find the source.

167 points by infodocket 7y ago

Projects like these highlight that as ML becomes more and more complex, a gap grows larger and larger which must be filled by manual or semi-manual labor. If it isn't a team of volunteers combing the rendered results for errors, it's users who have succumbed to those errors and leave feedback in the hopes the system improves. That gets me wondering - is the future of AI really just a semi-autonomous twilight zone where cheap / free labor augments an already faulty system? If not, what possible application is there for an expensive and closed automated system which works 100% requiring no human input, when other options are cheaper and leave clear directions for improvement?

mikepurvis 7y ago
Surely there's a spectrum here? Conventional, manually-made maps are full of errors too, and there's no army of low-paid workers combing over those— nope, they're published as they are, and it's up to end users to report problems. The better the map maker is (whether it's an ML black box or a team of human experts), the fewer errors there are, and either way, one expects an upward quality trend over time.
tschwimmer 7y ago
Yes and no. There is a huge explosion of 'Ops Plus' startups that take an existing manual process and then build some basic tooling around it (with or without any substantial ML component). There are mild to moderate efficiency gains coming from this, but a lot of their valuation is coming from a bet that in the future they'll be able to fully automate the system and reap efficiency gains. In practice, almost nobody is even thinking about building a fully automated process for every case. The reason is simple: automating the first 60% of work takes x effort, automating the next 30% takes 10x, and automating the next 9% takes 100x and operating the final 1% is essentially impossible. So if you came to the table with the goal of 100% automation right out the gate you'd spent 10 years developing something with little to show. I think full automation of some systems is possible, but is actually blocked by generational norms. By and large the systems that "Ops Plus" startups are attempting to automate were designed by people who are not digital natives. They're not illiterate, but things like instant messaging, async communication and and structured data are not natural primitives for them. I'm not saying everyone in the Fortnite generation is a master data modeller, but I think that when they join the workforce they'll set up systems that are much more feasible to automate.
- nicolaskruchten 7y ago
  I’m interested in learning more about this “Ops Plus” term but it doesn’t seem very google friendly... Is this a term of art in some circles?
  - tschwimmer 7y ago
    I actually just made it up. Companies that come to mind are Scale.ai (doing data labelling stuff for ML that historically would have been done by outsourcing companies, etc), Flexport (freight forwarding, traditionally done via spreadsheets and emails), Checkr (background checks), Atrium (legal services), Oscar/Clover (health insurance), Cadre (real estate investing). There are tons and tons of them in the recruiting (really sourcing) space too: I'd say Triplebyte is an Ops Plus company, as is Sourceress and a couple more.
- iguy 7y ago
  What you describe is pretty much exactly parallel to the automation of physical work. Having a steam engine gets you a long way, then the first few robots a bit further... there's no particular reason to want a factory with zero employees (except the watchman and his dog...) unless you want it to work on Mars or something.
tomrod 7y ago
> If it isn't a team of volunteers combing the rendered results for errors, it's users who have succumbed to those errors and leave feedback in the hopes the system improves. This is the idea behind GANs, as it stands.

rihegher 7y ago

They say it uses a deep neural network but we don't know this is a convnet or another type of NN. Anyone has more technical infos about it?

gwern 7y ago
This is semantic segmentation, so it has to be a convnet. I've never heard of trying to do semantic segmentation any other way, and a prototype of another approach wouldn't go into production at scale. OP links to https://ai.facebook.com/blog/mapping-roads-through-deep-learning-and-weakly-supervised-training https://ai.facebook.com/blog/mapping-roads-through-deep-lear... which says that it's D-LinkNet specifically: http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w4/Zhou_D-LinkNet_LinkNet_With_CVPR_2018_paper.pdf http://openaccess.thecvf.com/content_cvpr_2018_workshops/pap... (more or less a Unet).
- joshvm 7y ago
  I don't disagree, but I'd be careful with that statement. A huge amount of landcover segmentation in remote sensing still relies on simple models - either linear regression (thresholds) or classical machine learning like random forests or SVMs. For a lot of cases, these techniques will get you 90% of the way and it's very rare to have ground truth data that is accurate enough that you can measure the difference with any real degree of confidence. A big problem in the field is the lack of good (public) ground truth. There's so little hand labelled data to work with that without humans in the loop it's extremely difficult to validate the results meaningfully (unless you have an army of staff to do it). With something like roads you could also have heuristics about what a road looks like and where it goes (e.g. it's a continuous thin line), which can help condition things. I've seen a lot of papers which are applying deep learning for semantic segmentation for satellite mapping, but they evaluate on very limited datasets, they attempt to regress to simpler models without realising it (e.g. trying to predict a linear model), or they leak train and test data and report amazing results because they randomly split data from the same region. I'm not saying that convnets aren't better than simpler models, but particularly for satellite imaging I'd take them with a pinch of salt and see what the improvement from a baseline method is. If you look at a random sampling of papers from the DeepGlobe competition, almost none of them provide the results from e.g. a cheap linear SVM. Fun side note - several existing "famous" datasets generalise poorly to the developing world because most of the imagery is from the developed world (and even more specifically the West) and infrastructure looks totally different.

yorwba 7y ago

OpenStreetMap contributors weren't happy about the quality of tags when the import started: https://forum.openstreetmap.org/viewtopic.php?id=63456 https://forum.openstreetmap.org/viewtopic.php?id=63456 Facebook wasn't the only company adding low-quality data: https://forum.openstreetmap.org/viewtopic.php?id=64430 https://forum.openstreetmap.org/viewtopic.php?id=64430 (Previous discussion on HN: https://news.ycombinator.com/item?id=18723138 https://news.ycombinator.com/item?id=18723138 ) This article's quotes of what appear to be OpenStreetMap representatives are generally positive, so maybe that means they fixed all the problems they caused.

thomersch_ 7y ago
Nope, they just cherry-picked someone who is positive, which is not the case for most mappers.
tmcw 7y ago
OpenStreetMap contributors haven't been happy with anything, for the last 15 years.
- tuukkah 7y ago
  There are many positive developments such as good quality satellite imagery and goverment geographic data being offered to OpenStreetMap contributors for integration. An up-to-date aerial image of a city does wonders to the mapping experience. Mapillary's service complements OSM nicely as well as provides an invaluable on-the-ground data source. There's so many happy things in the community I can't possibly list them all.
  - habnds 7y ago
    Agreed, Tranport for London recently built an enormous database of cycling infrastructure to be integrated into OSM. It's an exciting project/collaboration. https://wiki.openstreetmap.org/wiki/TfL_Cycling_Infrastructure_Database https://wiki.openstreetmap.org/wiki/TfL_Cycling_Infrastructu...
- andygates 7y ago

letcree 7y ago

Very cool that they are integrating it with HOT tasking manager and making it easy for anyone to use the editor with the ML-generated proposed objects in several countries. I think the ML has been there for a couple years but currently it's not easy to take advantage of it. Hopefully they eventually release the ML pipeline itself as well. Their RapiD editor has some similarities to a research project I was involved with: https://mapster.csail.mit.edu/maid.html https://mapster.csail.mit.edu/maid.html (https://www.youtube.com/watch?v=i-6nbuuX6NY https://www.youtube.com/watch?v=i-6nbuuX6NY vs https://tech.fb.com/wp-content/uploads/2019/07/add_ML_road.gif.-1.gif https://tech.fb.com/wp-content/uploads/2019/07/add_ML_road.g...)

workingpatrick 7y ago

My first thought is wondering what data FB hopes to acquire from making this tool available.

rainyMammoth 7y ago
exactly my thought as well. Whenever Facebook releases something we should all directly worry about what their real motives are.
- photojosh 7y ago
  They need good quality maps. I worry that they'll try to embrace and extend in some way.
letcree 7y ago
Their motivation is self-serving but I don't think it's so nefarious. They use OpenStreetMap in check-in posts to display map data around locations that Facebook users have visited, so improving OpenStreetMap in turn improves the quality of this feature.
zyang 7y ago
User location data is useless without the substrate of a proper map.
Groxx 7y ago
Training data from OSM volunteers, at the very least. Probably both cheaper and higher quality than e.g. mechanical turk.
gwern 7y ago
FB is far from the only big tech company which has sponsored OSM as a competitor to Google Maps (Microsoft/Facebook/DigitalGlobe/Telenav/FourSquare/Craigslist have all sponsored OSM to some degree; Apple, of course, went its own way and created Apple Maps). It's a reaction to Google Maps: a monopoly on high-quality up-to-date global maps with business location is dangerous to everyone else, as a chokepoint on mobile applications. It's less about 'acquiring data' and more about not being extorted by GM. Classic 'commoditize your complement' dynamics: https://www.gwern.net/Complement https://www.gwern.net/Complement

Avamander 7y ago

I guess it's nice in those areas with low contribution precentage. But I suspect in many cases and definetly in Estonia there's a national high quality map database that could be used to augment existing maps instead. I'm wondering though, has anyone attempted to do so, quick Google search reveals nothing.

photojosh 7y ago
Depends on whether the owner of said high quality map database will license it for inclusion in OSM. My understanding, from following the Australian OSM mailing list, is that it takes an individual to pursue this with a government agency, which is a ton of work, and often you'll just get a 'no'.
rmc 7y ago
It is possible to "import" other data into OSM. However it's very tricky. Licencing/legal issues aside, it can be very technologically challenging to merge another database into OSM. I suggest posting to the OSM talk@ mailing list, or if there's a local estonian one too

m4eta 7y ago

Maybe AI can help with Lyft/Uber. I'm sort of tired of being dropped off in alleyways.

jdblair 7y ago
The great thing with Lyft/Uber is that I'm in the car and I can say "please take me around front to the main entrance, not here in the alleyway."
- andygates 7y ago
  That works for regular cabs too! /s
rmc 7y ago
This is the point about OSM. Local people can just fix it. We don't need _artifical_ intellegence, when we have real intelligence.

dr_faustus 7y ago

Is it open source? It is somehow implied, but I can't find the source.