[HOT] What I'd like for next Christmas is...

[john whelan]

Some sort of notification of when a tile is completed on a selected project.

Currently I open up four projects each day and check for completed tiles so
I can validate them.  Three are moving very slowly currently, it can be a
couple of weeks before another batch of tiles gets done.

Problem is at the moment the HOT tile server is overloaded and checking for
done tiles is a load that really shouldn't be placed on the server unless a
tile has been completed.

So please Santa sort it out for me.

Thanks John
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Re: [HOT] Building Detection using Machine Learning

[Blake Girardot HOT/OSM]

On Thu, Jan 5, 2017 at 8:43 PM, Stefan Keller  wrote:

>But even if would be 99.8 percent it's important for OSM
> that a human integrates the data into the database.

Just for the record, I agree with this totally. I only see a scenario
where any automatically identified feature would have a human look at
it and the imagery, correct anything as needed and then upload it to
osm.

Cheers
blake

Blake Girardot
Humanitarian OpenStreetMap Team, TM3 Project Manager
skype: jblakegirardot
HOT Core Team Contact: i...@hotosm.org
Live OSM Mapper-Support channel - https://hotosm-slack.herokuapp.com/
BE A PART OF HOT'S MICRO GRANTS: https://donate.hotosm.org/

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Re: [HOT] Building Detection using Machine Learning

[Stefan Keller]

Hi Philip

On Mon, Dec 19, 2016 at 3:17 PM, Philip Hunt  wrote:
> 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.

Looks nice!

Extracting buildings from satellite imagery has been approached quite
some times before. I think that object recognition algos. now get
close to 98%. But even if would be 99.8 percent it's important for OSM
that a human integrates the data into the database.

Your project is very similar to my OSMDeepOD project [1]. We use OSM
and Bing satellite imagery which we feed to the open source software
library TensorFlow with a retrained inception V3 convolution neural
network ("deep learning").

We concentrated on missing crosswalks and are now switching to
building outline, roof orientation and building levels.

As proposed by Rory we take the output and hand it over to MapRoulette
[2]. Actually there are currently 14 MapRoulette challenges ongoing
like e.g. the "Missing Crosswalks of Lucerne in Switzerland". Even if
you don't read German, you can see the progress here [3].

I'm keen to see how your Viola+Jones/Haar-Cascade algorithm performs
versus our deep learning!

We're open to collaborate and accept GitHub pull requests.

:Stefan


[1] https://github.com/geometalab/OSMDeepOD
[2] http://maproulette.org/
[3] http://zebrastreifen-safari.osm.ch/

2017-01-05 16:58 GMT+01:00 Denis Carriere :
> I agree with Rory with the rotated buildings.
>
> However, these building outlines would be great to detect missing buildings
> within an area. It's usually very hard to find "missing" buildings when your
> HOT Tasking Manager is at 100% completed.
>
> This workflow/tool can definitely be used in the validation process of a HOT
> task or adding buildings to an entire city by comparing the existing OSM
> data with the machine learning buildings and find the ones that don't
> overlap.
>
> I don't ever see this ever being imported, but it's a great tool and you
> should continue developing this!
>
> Cheers,
>
> ~~
> Denis Carriere
> GIS Software & Systems Specialist
>
> On Thu, Jan 5, 2017 at 10:41 AM, Rory McCann  wrote:
>>
>> You've noticed how your algoritm isn't able to get properly rotated
>> buildings. And this might be an advantage! All buildings being
>> non-rotated is *obviously* incorrect, so people aren't gonna want to
>> import them, they'll realise that they have to have a human to
>> review/correct it.
>>
>> Perhaps you should load all your "buildings" into MapRoullette or ToFix
>> and people can hope from one spot to the next, mapping buildings? Your
>> "buildings" will ensure that some of the harder work is done, and people
>> can map much faster. It also provides a good measure of how many
>> buildings are in an area, and hence whether the OSM building mapping is
>> complete or some is missing.
>>
>>
>> On 19/12/16 15:17, 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 

Re: [HOT] Building Detection using Machine Learning

[Denis Carriere]

I agree with Rory with the rotated buildings.

However, these building outlines would be great to detect missing buildings
within an area. It's usually very hard to find "missing" buildings when
your HOT Tasking Manager is at 100% completed.

This workflow/tool can definitely be used in the *validation* process of a
HOT task or adding buildings to an entire city by comparing the existing
OSM data with the machine learning buildings and find the ones that don't
overlap.

I don't ever see this ever being imported, but it's a great tool and you
should continue developing this!

Cheers,

*~~*
*Denis Carriere*
*GIS Software & Systems Specialist*

On Thu, Jan 5, 2017 at 10:41 AM, Rory McCann  wrote:

> You've noticed how your algoritm isn't able to get properly rotated
> buildings. And this might be an advantage! All buildings being
> non-rotated is *obviously* incorrect, so people aren't gonna want to
> import them, they'll realise that they have to have a human to
> review/correct it.
>
> Perhaps you should load all your "buildings" into MapRoullette or ToFix
> and people can hope from one spot to the next, mapping buildings? Your
> "buildings" will ensure that some of the harder work is done, and people
> can map much faster. It also provides a good measure of how many
> buildings are in an area, and hence whether the OSM building mapping is
> complete or some is missing.
>
>
> On 19/12/16 15:17, 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 

Re: [HOT] Building Detection using Machine Learning

[Rory McCann]

You've noticed how your algoritm isn't able to get properly rotated
buildings. And this might be an advantage! All buildings being
non-rotated is *obviously* incorrect, so people aren't gonna want to
import them, they'll realise that they have to have a human to
review/correct it.

Perhaps you should load all your "buildings" into MapRoullette or ToFix
and people can hope from one spot to the next, mapping buildings? Your
"buildings" will ensure that some of the harder work is done, and people
can map much faster. It also provides a good measure of how many
buildings are in an area, and hence whether the OSM building mapping is
complete or some is missing.


On 19/12/16 15:17, 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 

[HOT] Final HOT Summit 2016 videos now available!

[Nate Smith]

Hello HOTOSM Community -

Happy 2017! All HOT Summit 2016 videos are now online. You can find all the
videos on the HOT Summit 2016 playlist on our YouTube channel:

https://www.youtube.com/playlist?list=PLb9506_-6FMFoS6_LqOFS-tCD__1rpmKN

Thanks for everyone’s patience as we received the final videos over the
last couple of months. Most of all, thank you to all the speakers who
shared at last year’s Summit. We had top-notch presentations that covered
the breadth of work the entire community does on a daily basis. If anyone
has questions about the videos, please send an email directly to the HOT
Summit Working Group: sum...@hotosm.org.

We look forward to the next Summit for new community members and projects
to be shared. As a reminder, the HOT Summit working group needs volunteers
to be able to plan and implement our annual meeting. If you’re interested
in helping or want to see the next summit be a success, please send an
email to sum...@hotosm.org and let us know you would like to get involved.

Nate and HOT Summit Working Group


Nate Smith
@nas_smith 
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[HOT] 2 min video on value of maps to help end FGM in Tanzania

[Janet Chapman]

Thank you to everyone that has helped with projects 1788 and 2261.  There is a 
great video posted on AJ+ about our work here 
https://www.facebook.com/ajplusenglish/videos/873491619459013/

Thanks, Janet

AJ+ - These volunteers are mapping rural Tanzania to help... | 
Facebook
www.facebook.com
These volunteers are mapping rural Tanzania to help girls escape female genital 
cutting.




 Janet Chapman - Campaigns Manager and Project Officer


http://hiaragirlpower.blogspot.com/


TANZANIA DEVELOPMENT TRUST

Registered Charity no 270462

Every pound given to TDT goes directly to projects in Tanzania

www.TanzDevTrust.org



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