Re: [OSM-talk] Building Detection using Machine Learning

[Oleksiy Muzalyev]

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

[Martin Koppenhoefer]

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

[Oleksiy Muzalyev]

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

[Christian Quest]

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

[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 
 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

[john whelan]

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

[Frederik Ramm]

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