As John is saying, quality can vary a lot. You should look at the thread where
I presented geometry quality analysis
recently.https://lists.openstreetmap.org/pipermail/talk/2018-September/081392.html
You need a more rigourus procedure where you can compare AI with contributors
that operate in the same conditions, with the same imagery.
Pierre
Le mardi 20 novembre 2018 09 h 44 min 59 s HNE, John Whelan
<[email protected]> a écrit :
I've done a lot of validation in HOT. Some is done by people with little
experience so I don't think validated tiles would be much use to you. Also be
aware that some imagery can be three years out of date so the imagery can vary.
The sort of problems you'll run into are much of the HOT mapping is done by
inexperienced mappers using iD and the buildings are more approximate than you
might like especially on size and shape. It takes four times longer to correct
a building than to remap it so as a rule of thumb validators tend to stay away
from projects with buildings.
Having said that have a look at the hot projects for one that is 100% complete
and 100% validated. You should be able to work out the coordinates from the
project and if you are lucky it should also give you the imagery used when
mapping for HOT. Beware this OpenStreetMap and some mapping may have taken
place on the ground showing buildings etc that are not on the image.
You might be better off working with an experienced mapper who can both map and
check the sources. They should also check for duplicates, there are tools
available. I suggest an area that hasn't been mapped before and map it
correctly with an experienced mapper using JOSM and things like the
building_tool plugin.
If you're just trying the algorithm out Ottawa in Canada has accurate buildings
in OpenStreetMap.
Have fun
Cheerio John
Georgy Potapov wrote on 2018-11-20 9:25 AM:
Dear John,
the question isn't about imports. Seems guys are preparing dataset to train and
test models and they need to download data that's been already validated within
Missing Maps, as a "ground truth". Is there a way to get the precise
coordinates of the areas where this job is done or projected to be done?Me too
was asking if it makes sense to apply algorithm to detect damaged buildings on
imagery to leverage the mapping speed in disaster affected areas?
All the best,Georgy
On Tue, Nov 20, 2018 at 3:29 PM john whelan <[email protected]> wrote:
Essentially you are proposing an import. There are OpenStreetMap rules about
how this should be done. It can be done, Microsoft has released building
outlines for the US which were created in this manner but the import itself is
being done in sections by conventional mappers.
You will need the cooperation of the local mappers on the ground and imports of
dubious quality tend to get a fair chunk of flack. I would suggest you Google
OSM imports before doing anything.
Cheerio John
On Mon, 19 Nov 2018, 10:47 pm Serkan Karakulak <[email protected] wrote:
Hi,I am a graduate student in a Data Science program, and I wanted to get in
touch with you because me and my two other friends are interested in working on
a machine learning project to map areas using their satellite images and
produce their labels. If we obtain a high accuracy, we thought it could be of
use to the hotosm and the missingmaps initiatives.
I have first heard about Missing Maps when I came across a mapathlon event two
years ago in Istanbul. We were in search for a class project for one of our
courses and I remembered about the mapathlon event and we would be very happy
if we could contribute to the this wonderful initiative. There are already some
previous successful works on this subject, so we are very hopeful that we could
come up with an algorithm with high accuracy.
In order to develop the model, we need to extract bounding boxes of the
validated tiles we see at hotosm. Then we will extract the satellite images and
their labels using the label-maker API which is developed by DevSeed. Is there
a method to extract the coordinates of these validated tiles?
Thank you for your time and help.
Best,
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