#rasterio — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #rasterio, aggregated by home.social.
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RE: https://wisskomm.social/@ioer/115899330915763542
I really took a deep dive into #datashader with this map: Locals & Tourists in Germany, as derived from 67 Million Geo-Social Media Posts (2007-2022) in Germany. The data includes public shared posts from Instagram, Flickr, Twitter and iNaturalist.
I always wanted to create such a map, following the footsteps of Eric Fisher's Locals & Tourists dataset from 2011 [1].
I shared the code for producing this map here [2]. The repository is available here [3]. This includes some neat methods for various #geospatial processing tasks in #Python, such as exporting a datashader map to a #GeoTiff [4] with the help of #Xarray and #Rasterio.
Finally, all of this was created in a privacy-preserving way using #HyperLogLog, which allowed me to share the code and abstracted data publicly for full reproducibility and transparency. [6] #FAIR
Below you'll find the link to the (quite succinct) publication in Natur und Landschaft in Karten (#NuL).
[1]: https://www.flickr.com/photos/walkingsf/albums/72157624209158632
[2]: https://code.ad.ioer.info/wip/digital_traces_map/html/03_visualization.html
[3]: https://gitlab.hrz.tu-chemnitz.de/ad/digital_traces_map/
[4]: https://gitlab.hrz.tu-chemnitz.de/s7398234--tu-dresden.de/base_modules/-/blob/main/raster.py?ref_type=heads#L78
[5]: https://www.nul-online.de/article-7301410-1111/landschaft-und-natur-in-karten-.html
[6]: https://doi.org/10.71830/VDMUWW -
RE: https://wisskomm.social/@ioer/115899330915763542
I really took a deep dive into #datashader with this map: Locals & Tourists in Germany, as derived from 67 Million Geo-Social Media Posts (2007-2022) in Germany. The data includes public shared posts from Instagram, Flickr, Twitter and iNaturalist.
I always wanted to create such a map, following the footsteps of Eric Fisher's Locals & Tourists dataset from 2011 [1].
I shared the code for producing this map here [2]. The repository is available here [3]. This includes some neat methods for various #geospatial processing tasks in #Python, such as exporting a datashader map to a #GeoTiff [4] with the help of #Xarray and #Rasterio.
Finally, all of this was created in a privacy-preserving way using #HyperLogLog, which allowed me to share the code and abstracted data publicly for full reproducibility and transparency. [6] #FAIR
Below you'll find the link to the (quite succinct) publication in Natur und Landschaft in Karten (#NuL).
[1]: https://www.flickr.com/photos/walkingsf/albums/72157624209158632
[2]: https://code.ad.ioer.info/wip/digital_traces_map/html/03_visualization.html
[3]: https://gitlab.hrz.tu-chemnitz.de/ad/digital_traces_map/
[4]: https://gitlab.hrz.tu-chemnitz.de/s7398234--tu-dresden.de/base_modules/-/blob/main/raster.py?ref_type=heads#L78
[5]: https://www.nul-online.de/article-7301410-1111/landschaft-und-natur-in-karten-.html
[6]: https://doi.org/10.71830/VDMUWW -
Current project: Automatic pipeline for downloading and processing Sentinel 2 imagery. Once that works, automatic upload to a TAK server as datapackage...
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Current project: Automatic pipeline for downloading and processing Sentinel 2 imagery. Once that works, automatic upload to a TAK server as datapackage...
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Current project: Automatic pipeline for downloading and processing Sentinel 2 imagery. Once that works, automatic upload to a TAK server as datapackage...
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Current project: Automatic pipeline for downloading and processing Sentinel 2 imagery. Once that works, automatic upload to a TAK server as datapackage...
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Current project: Automatic pipeline for downloading and processing Sentinel 2 imagery. Once that works, automatic upload to a TAK server as datapackage...
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Utilisation de #Python et #RasterIO pour lire les metadonnées des images géographiques. https://youtu.be/VfKTQj54Fzg?si=hx3EdSvOJqWTx30X
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Utilisation de #Python et #RasterIO pour lire les metadonnées des images géographiques. https://youtu.be/VfKTQj54Fzg?si=hx3EdSvOJqWTx30X
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Utilisation de #Python et #RasterIO pour lire les metadonnées des images géographiques. https://youtu.be/VfKTQj54Fzg?si=hx3EdSvOJqWTx30X
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Utilisation de #Python et #RasterIO pour lire les metadonnées des images géographiques. https://youtu.be/VfKTQj54Fzg?si=hx3EdSvOJqWTx30X
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👀 Is anyone of you aware of a python solution for creating a bivariate map from a raster?
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👀 Is anyone of you aware of a python solution for creating a bivariate map from a raster?
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👀 Is anyone of you aware of a python solution for creating a bivariate map from a raster?
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👀 Is anyone of you aware of a python solution for creating a bivariate map from a raster?
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👀 Is anyone of you aware of a python solution for creating a bivariate map from a raster?
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Been working on bringing back an old Python project, to get OBJ files from GeoTIFFs using #RasterIo.
Mostly for my own amusement, and to get back to doing some Python after a break.
A panorama of #Edinburgh old town, camera is positioned above the spire of the Hub in LawnMarket.
The odd dark spike to the south was the crane being used to build the Virgin Hotel in Cowgate, the DSM is from a couple of years back.
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Been working on bringing back an old Python project, to get OBJ files from GeoTIFFs using #RasterIo.
Mostly for my own amusement, and to get back to doing some Python after a break.
A panorama of #Edinburgh old town, camera is positioned above the spire of the Hub in LawnMarket.
The odd dark spike to the south was the crane being used to build the Virgin Hotel in Cowgate, the DSM is from a couple of years back.
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Been working on bringing back an old Python project, to get OBJ files from GeoTIFFs using #RasterIo.
Mostly for my own amusement, and to get back to doing some Python after a break.
A panorama of #Edinburgh old town, camera is positioned above the spire of the Hub in LawnMarket.
The odd dark spike to the south was the crane being used to build the Virgin Hotel in Cowgate, the DSM is from a couple of years back.
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Been working on bringing back an old Python project, to get OBJ files from GeoTIFFs using #RasterIo.
Mostly for my own amusement, and to get back to doing some Python after a break.
A panorama of #Edinburgh old town, camera is positioned above the spire of the Hub in LawnMarket.
The odd dark spike to the south was the crane being used to build the Virgin Hotel in Cowgate, the DSM is from a couple of years back.
-
Been working on bringing back an old Python project, to get OBJ files from GeoTIFFs using #RasterIo.
Mostly for my own amusement, and to get back to doing some Python after a break.
A panorama of #Edinburgh old town, camera is positioned above the spire of the Hub in LawnMarket.
The odd dark spike to the south was the crane being used to build the Virgin Hotel in Cowgate, the DSM is from a couple of years back.
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I've been struggling with adding a colormap to a landcover raster. There is https://gdal.org/programs/gdalattachpct.html but apparently it isn't included in the GDAL that comes with QGIS or osgeo4win.
I finally tried using #rasterio, and and am happy to report that it works like a charm! https://rasterio.readthedocs.io/en/stable/topics/color.html#writing-colormaps
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I've been struggling with adding a colormap to a landcover raster. There is https://gdal.org/programs/gdalattachpct.html but apparently it isn't included in the GDAL that comes with QGIS or osgeo4win.
I finally tried using #rasterio, and and am happy to report that it works like a charm! https://rasterio.readthedocs.io/en/stable/topics/color.html#writing-colormaps
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I've been struggling with adding a colormap to a landcover raster. There is https://gdal.org/programs/gdalattachpct.html but apparently it isn't included in the GDAL that comes with QGIS or osgeo4win.
I finally tried using #rasterio, and and am happy to report that it works like a charm! https://rasterio.readthedocs.io/en/stable/topics/color.html#writing-colormaps
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I've been struggling with adding a colormap to a landcover raster. There is https://gdal.org/programs/gdalattachpct.html but apparently it isn't included in the GDAL that comes with QGIS or osgeo4win.
I finally tried using #rasterio, and and am happy to report that it works like a charm! https://rasterio.readthedocs.io/en/stable/topics/color.html#writing-colormaps
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I've been struggling with adding a colormap to a landcover raster. There is https://gdal.org/programs/gdalattachpct.html but apparently it isn't included in the GDAL that comes with QGIS or osgeo4win.
I finally tried using #rasterio, and and am happy to report that it works like a charm! https://rasterio.readthedocs.io/en/stable/topics/color.html#writing-colormaps
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I got to bed quite late yesterday because I was fixing some #Rasterio code of mine...
And then I dreamt about the goddamn stuff...
I think I'll need to make a "stop doing raster data" meme at some point!
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I have been working on a set of hierarchical hexagonal European population projections based on @WorldPopProject using @qgis @geopandas and #rasterio
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I have been working on a set of hierarchical hexagonal European population projections based on @WorldPopProject using @qgis @geopandas and #rasterio
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I have been working on a set of hierarchical hexagonal European population projections based on @WorldPopProject using @qgis @geopandas and #rasterio
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I have been working on a set of hierarchical hexagonal European population projections based on @WorldPopProject using @qgis @geopandas and #rasterio
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I have been working on a set of hierarchical hexagonal European population projections based on @WorldPopProject using @qgis @geopandas and #rasterio
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This is then a population density render using @qgis @geopandas and #rasterio 1km² #population data at an #European scale including a reasonable chunk of #EasternEurope
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This is then a population density render using @qgis @geopandas and #rasterio 1km² #population data at an #European scale including a reasonable chunk of #EasternEurope
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This is then a population density render using @qgis @geopandas and #rasterio 1km² #population data at an #European scale including a reasonable chunk of #EasternEurope
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This is then a population density render using @qgis @geopandas and #rasterio 1km² #population data at an #European scale including a reasonable chunk of #EasternEurope
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Just 😍
I couldn't wait, so here's a work-in-progress #mosaic of #SwissTOPO aerial imagery. All thanks to #Python, #RasterIO, #GDAL, and a few late night coding sessions.
I can't get over the quality of these images, especially that water. ✨
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Just 😍
I couldn't wait, so here's a work-in-progress #mosaic of #SwissTOPO aerial imagery. All thanks to #Python, #RasterIO, #GDAL, and a few late night coding sessions.
I can't get over the quality of these images, especially that water. ✨
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Just 😍
I couldn't wait, so here's a work-in-progress #mosaic of #SwissTOPO aerial imagery. All thanks to #Python, #RasterIO, #GDAL, and a few late night coding sessions.
I can't get over the quality of these images, especially that water. ✨
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Just 😍
I couldn't wait, so here's a work-in-progress #mosaic of #SwissTOPO aerial imagery. All thanks to #Python, #RasterIO, #GDAL, and a few late night coding sessions.
I can't get over the quality of these images, especially that water. ✨
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Just 😍
I couldn't wait, so here's a work-in-progress #mosaic of #SwissTOPO aerial imagery. All thanks to #Python, #RasterIO, #GDAL, and a few late night coding sessions.
I can't get over the quality of these images, especially that water. ✨
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Has anyone in the maphive used #GDAL or #rasterio to sieve *a targeted value* in a #raster
I'm having some success with these tools to weed out small polygons globally, but I'm actually aiming to affect one DN/class and not the others.
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Has anyone in the maphive used #GDAL or #rasterio to sieve *a targeted value* in a #raster
I'm having some success with these tools to weed out small polygons globally, but I'm actually aiming to affect one DN/class and not the others.
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Has anyone in the maphive used #GDAL or #rasterio to sieve *a targeted value* in a #raster
I'm having some success with these tools to weed out small polygons globally, but I'm actually aiming to affect one DN/class and not the others.
-
Has anyone in the maphive used #GDAL or #rasterio to sieve *a targeted value* in a #raster
I'm having some success with these tools to weed out small polygons globally, but I'm actually aiming to affect one DN/class and not the others.
-
Has anyone in the maphive used #GDAL or #rasterio to sieve *a targeted value* in a #raster
I'm having some success with these tools to weed out small polygons globally, but I'm actually aiming to affect one DN/class and not the others.
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After fighting #GDAL, #RasterIO, and a million other tools for a couple evenings, I finally have a pipeline that can create unified Slippy #map tiles from a mix of #GeoTIFFs and other tiles, all across multiple projections!
It feels like a huge win on the way to a scalable raster pipeline.
I'm sure this is child's play for experienced raster data wranglers, but it feels so good for my first major success!
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After fighting #GDAL, #RasterIO, and a million other tools for a couple evenings, I finally have a pipeline that can create unified Slippy #map tiles from a mix of #GeoTIFFs and other tiles, all across multiple projections!
It feels like a huge win on the way to a scalable raster pipeline.
I'm sure this is child's play for experienced raster data wranglers, but it feels so good for my first major success!
-
After fighting #GDAL, #RasterIO, and a million other tools for a couple evenings, I finally have a pipeline that can create unified Slippy #map tiles from a mix of #GeoTIFFs and other tiles, all across multiple projections!
It feels like a huge win on the way to a scalable raster pipeline.
I'm sure this is child's play for experienced raster data wranglers, but it feels so good for my first major success!
-
After fighting #GDAL, #RasterIO, and a million other tools for a couple evenings, I finally have a pipeline that can create unified Slippy #map tiles from a mix of #GeoTIFFs and other tiles, all across multiple projections!
It feels like a huge win on the way to a scalable raster pipeline.
I'm sure this is child's play for experienced raster data wranglers, but it feels so good for my first major success!