Thanks Nikhil for doing good work for community.
You can make building in 3d view.
There are two ways to do that
1. Adding building height data from contour and using deck gl or three gs
to show in 3d.
2. 3d building view with vector styling. It gives 3d view but all building
will be similar
Hi All,
India data from Microsoft's ML Buildings Footprints data release is
deployed on both vector and raster tile layers:
https://server.nikhilvj.co.in/buildings1/
This is a TileServer GL instance.
Recipe for how this was done and the configs etc reqd is shared here:
To make folder structure mbtile you might need to add ''-e'' in the
recipe.
or tile is made you can use Mbutil to create folder structure from single
file.
To your second query to create building footprints with rater tiles there
are two ways two approach it.
You can create raster tiles from
Hi Deepak,
Thanks for the suggestion.
I came across tippecanoe again : https://github.com/mapbox/tippecanoe
and finally seeing a use case for it. So, while the DB is still being
loaded, started off tippecanoe program on the original .geojsonl.
The program was quite fast - in about 6 hrs it had
Hello Nikhil,
I would definitely love to collobrate. The best i would suggest is to make
Mbtiles and host them as data is not going to change a lot so most of the
time static.
Rendering from hosted Mbtiles will not only fast but easy to handle on
small web server.
How to proceed.
First
Hi Bhibhash,
Short answer: No.
There is no metadata. I took the top 1000 lines and visualized it : the
shapes were spread all over the country and there wasn't any order in them.
With an intern's help I've started import of them into a portable
postgresql DB (dockerized with a persistent volume)
Thanks for sharing.
Do we have state-wise datasets? It seems quite large to process on my
computer.
Best,
Bibhash
On Wed, May 18, 2022 at 4:58 AM Nikhil VJ wrote:
> Thanks Justin for sharing!
>
> I've downloaded and extracted the India.geojsonl on a webserver.
>
> ref about .geojsonl :
Thanks Justin for sharing!
I've downloaded and extracted the India.geojsonl on a webserver.
ref about .geojsonl : https://www.interline.io/blog/geojsonl-extracts/
-> pretty useful! One can loop through a huge file without having to load
it all into RAM.
top lines look like:
{"type": "Feature",
[image: lg.png]
https://github.com/microsoft/GlobalMLBuildingFootprints
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