New blog post: Accessing Planetary Computer STAC files in DuckDB - see https://blog.rtwilson.com/accessing-planetary-computer-stac-files-in-duckdb/

This is a report of my experimentation on how to access STAC GeoParquet files provided by Planetary Computer in DuckDB - I struggle a bit with authentication and libraries, but get it working after a while.

Given the lack of outrage (outside my WhatsApp peers) on the demise of #PlanetaryComputer , I guess not many #earthobservation peeps used it. Such a shame.... Cool open technology!

So #Microsoft is retiring the #PlanetaryComputer Hub, which allowed you to do quite advanced analyses using standard #Python packages. I guess we can run it elsewhere, and this was always on the cards, that's why using a standard #pangeo setup was good, but as with all things cloud, it's infuriating!!!

Have you wanted to use segment-geospatial (https://samgeo.gishub.org/) on Microsoft Planetary Computer and struggled? My blog post shows the simple solution: https://blog.rtwilson.com/how-to-get-segment-geospatial-working-on-microsoft-planetary-computer/

USGS 3DEP LiDAR Collection @ Microsoft
--
https://planetarycomputer.microsoft.com/dataset/group/3dep-LiDAR <-- Microsoft data / use case portal
--
“To respond to growing needs for high-quality elevation data, the goal of 3DEP is to complete acquisition of nationwide lidar (IfSAR in AK) to provide the first-ever national baseline of consistent high-resolution topographic elevation data – both bare earth and 3D point clouds. This dataset provides the raw 3D point clouds in the Cloud Optimized Point Cloud format, and various derived products as Cloud Optimized GeoTIFFs…”
#GIS #spatial #mapping #gischat #3dep #elevation #opendata #planetarycomputer #LiDAR #ifsar #USA #nationwide #acquistion #topography #topographic #dem #bareearth #pointclouds #cloudoptimization #geotiffs #download #library #usgs #microsoft
@microsoft @usgs

day 23: 3d
with bonus animation.

same corner of Sussex used on the Day 17. Elevation data from Copernicus Gloabl DEM (ESA) via #PlanetaryComputer.

DEM --> TIN accomplished with by randomly sampling the DEM, and using `matplotlib.tri.Triangulation`

first time mucking about with a TIN. ditto animation. and #matplotlib can do them both.

code: https://github.com/tlohde/30DMC/tree/main/2023

day 20: outdoors
summit views

aggregating viewsheds for all peaks within the cairngorms national park. brighter areas can be seen from more hill tops.

Cac Càrn Beag (1156 m) in the south east of the park wins the most visible prize: (can be seen from 289 other summits)

peaks and cairngorms boundary from #OpenStreetMap. DEM from Copernicus Global 30 m DEM via #PlanetaryComputer. viewshed analysis run with #gdal.

code: https://github.com/tlohde/30DMC/tree/main/2023

If you haven't read our recent blog focused on the components of Microsoft's #PlanetaryComputer and how it connects with #STAC, what are you waiting for?

https://www.element84.com/blog/how-microsofts-planetary-computer-uses-stac

New blog alert!

In our latest from E84's Pete Gadomski, we're covering:
- The components of the #PlanetaryComputer
- Demonstrating how it's based on both #cloud-optimized formats & the #STAC spec
- & Discussing how this work benefits the larger community

Learn more: https://www.element84.com/blog/how-microsofts-planetary-computer-uses-stac

#Leafmap v0.16.0 has been released. It supports searching and visualizing open #geospatial datasets interactively from public #STAC API endpoint (e.g., #AWS, #PlanetaryComputer). Search results can be returned as a GeoDataFrame, list, dict.
Notebook: https://leafmap.org/notebooks/64_stac_search
GitHub: https://github.com/giswqs/leafmap

Searching and visualizing #PlanetaryComputer #geospatial datasets interactively with #leafmap. Image footprints with attribute data can be added to the map automatically and accessed through a GeoPandas GeoDataFrame
https://github.com/giswqs/leafmap/pull/347
#dataviz #geopython #opensource #stac

A comprehensive list of open #geospatial datasets on major cloud computing platforms (#AWS, #EarthEngine, #PlanetaryComputer) and STAC Index. The lists are provided in TSV and JSON formats, making it easier to find and use them programmatically. The list is updated daily.

GitHub: https://github.com/giswqs/geospatial-data-catalogs

Kicking the tires on Microsoft's #PlanetaryComputer. This is a cool demo working with their global building footprints dataset.
https://planetarycomputer.microsoft.com/dataset/ms-buildings#Example-Notebook

For all #xarray and #PlanetaryComputer folk, here's some nice #Sentinel 1 accessing and visualisation using #Python:
https://discourse.pangeo.io/t/wednesday-november-2nd-2022-jupyter-book-tutorials-demonstrating-xarray-based-workflows-for-cloud-hosted-remote-sensing-data/2834
#RemoteSensing #EarthObservation

I want to make a chill candid webcast stream comparing typical workflows between #PlanetaryComputer, #EarthEngine, @qgis@twitter.com and #Arcgis. Not about “winners”, but about pros and cons for each. Who should I bring in? What workflows? #geospatial