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 geotiff by using tiles2py in folder
structure.

But in your case you can use Tileserver-gl what it does is it can host
vector tiles but serve them as raster.

You can check config file and change as per your requirements such as
256x256 or 512x512.


let me know if you still face issue.

Note - Go by vector tiles its fast and you can give 3D effect  as you have
seen in our app.


Thanks and Regards

On Thu, 26 May, 2022, 6:39 pm Nikhil VJ, <nikhil...@gmail.com> wrote:

> 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 generated a 1.4GB
> .mbtiles file and it didn't even take too much RAM.
>
> Then, with the help of https://github.com/maptiler/tileserver-php:
> This vector tiles URL is working at my end in QGIS:
>
> https://server.nikhilvj.co.in/buildings/tileserver-php/tileserver.php?/index.json?/buildings-z13/{z}/{x}/{y}.pbf
>
> I was looking for options to generate raster .png etc tiles, but haven't
> found one yet.
>
> I also saw an option in tippecanoe to output .pbf tiles to a folder
> instead of a single .mbtiles file. Did a couple of runs, but that hasn't
> worked out.
>
> Meanwhile, Sreeram-K posted in OSM-India telegram group: folks at
> mapwith.ai have incorporated the data and are now showing it in their
> rapID editor alongwith the AI roads data, as suggestions for openstreetmap
> editing. One link:
>
> https://mapwith.ai/rapid#background=none&datasets=fbRoads,msBuildings&disable_features=boundaries&map=16.17/13.09066/80.27361
>
>
> --
> Cheers,
> Nikhil VJ
> https://nikhilvj.co.in
>
>
>
> On Tue, May 24, 2022 at 1:48 PM Deepak Sharda <deepsha...@gmail.com>
> wrote:
>
>> 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 creation of Mbtiles
>> second Hosting of Mbtiles
>> Third  Styling at client side.
>>
>>
>> Thanks
>>
>>
>>
>> On Tue, 24 May, 2022, 1:03 pm Nikhil VJ, <nikhil...@gmail.com> wrote:
>>
>>> 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) on my webserver.
>>> Started yesterday and it's 10 million shapes in; from size comparisons
>>> it'll probably be around 79 million shapes, so should be imported in a
>>> week's time.
>>>
>>> I've set an auto-increment primary key column in the DB table, so once
>>> imported at least the data can be pulled out in batches.
>>>
>>> There are 2 paths ahead once I've imported it:
>>> -------
>>>
>>> *1. Serving vector tiles*
>>> I want to serve MVT vector tiles from this DB - and then have a map
>>> display the same. That way one can directly have this stuff render on a map
>>> as they browse it - fetching only one small region at a time and hopefully
>>> not being too slow.
>>>
>>> Finding some references here:
>>> https://blog.jawg.io/how-to-make-mvt-with-postgis/
>>> https://www.crunchydata.com/blog/dynamic-vector-tiles-from-postgis
>>>
>>> https://medium.com/@frederic.rodrigo/web-mapping-comparing-vector-tile-servers-from-postgres-postgis-405055e69084
>>>
>>> I'm new to this MVT business, so if anybody wants to collaborate pls
>>> feel free to jump in.
>>>
>>> ------
>>>
>>> *2. Tag with region ids*
>>> Add columns to the DB table and tag each shape by state, district ids
>>> etc using Within operations either in the DB or in python-geopandas.
>>>
>>>
>>> --
>>> Cheers,
>>> Nikhil VJ
>>> https://nikhilvj.co.in
>>>
>>>
>>> On Tue, May 24, 2022 at 10:14 AM Bibhash Nath <bibhas...@gmail.com>
>>> wrote:
>>>
>>>> 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 <nikhil...@gmail.com> wrote:
>>>>
>>>>> 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", "properties": {},"geometry": {"type":
>>>>> "Polygon","coordinates": [[[83.06380515611697, 25.34167404697847],
>>>>> [83.06380909901775, 25.341635591519122], [83.06386494585949,
>>>>> 25.341640268589657], [83.06386100295869, 25.341678724047526],
>>>>> [83.06380515611697, 25.34167404697847]]]}}
>>>>> {"type": "Feature", "properties": {},"geometry": {"type":
>>>>> "Polygon","coordinates": [[[87.87555977691633, 22.397660095199],
>>>>> [87.8754256865811, 22.397658576750985], [87.87542690191553,
>>>>> 22.397566835386712], [87.87556099225075, 22.39756835383578],
>>>>> [87.87555977691633, 22.397660095199]]]}}
>>>>> ...
>>>>> So we have just basic polygons, one for each building, no properties
>>>>> or categorization.
>>>>>
>>>>> Here are some ideas on what to do with this:
>>>>>
>>>>> 1. Postgresql DB:
>>>>> - Load all of these into a PostGreSQL DB
>>>>> - Setup an api that will take a lat/lon and give all shapes within 1km
>>>>> radius
>>>>> - Next possible api: send a bounding polygon and get all buildings in
>>>>> that
>>>>>
>>>>> 2. Split up by district or lower level admin boundaries:
>>>>> - Load the admin boundaries into a Postgresql DB
>>>>> - Loop through each line (ie each building)
>>>>> - Find out which place it's in by ST_Within query
>>>>> - Dump it into a separate .geojsonl for that place
>>>>> - We've now split it into multiple smaller files that are more usable,
>>>>> can be loaded up by OSM mappers etc.
>>>>>
>>>>> --
>>>>> Cheers,
>>>>> Nikhil VJ
>>>>> https://nikhilvj.co.in
>>>>>
>>>>>
>>>>> On Wed, May 18, 2022 at 3:51 AM Justin <justinelliotmey...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> [image: lg.png]
>>>>>>
>>>>>> https://github.com/microsoft/GlobalMLBuildingFootprints
>>>>>>
>>>>>>
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