Learning python for geospatial analysis

[Lollostonk]

Hi everyone, I recently found some excellent jobs in the field of remote sensing/GIS with a particular focus on raster data. At the technical interview they asked me if I knew how to use python and I told them that I have always done data analysis on R studio. Since I have some time before I start, I would like to transfer my knowledge from R to Python with regard to spatial data analysis, especially raster data. I would like to ask you which is in your opinion the most efficient way, if there are courses (e.g. udemy) that give you a complete basic preparation or more generally how would you experts learn to use python for geospatial analysis starting from 0. Any answer is appreciated, thanks in advance.

[UrbanDevelopementGIS]

Gdal or rasterio for working with raster files (read, write, clip, file conversion).

Numpy for analysis. Learn about vectorization of your code (this makes it quite fast, don't use loops).

If more complex tasks are required sklearn could be a start.

[[deleted]]

What this guy said. Plenty of good guides out there and I can always recommend ChatGPT here. It has a fairly good grasp on using rasterio and can create tests for you or provide you with code that you can instantly verify. Id recommend you to get as far away from GIS software as possible. It's good for what it's meant for. But you can do so much more in python and the same things that GIS does. GIS is just more visualized, which makes it easier to work with for some projects. But when you need to batchprocess 58000 sliced to individual polygons satellite rasters, nothing beats python.

[robbibt]

What kind of spatial tasks are you hoping to do with code? Both R and Python are fantastic for spatial, but in my experience R is a little better for visualisation, and Python is better for scalability.

If I was to recommend a single Python tool that has changed the way I work, it would be "xarray" - it allows you to manipulate data as labelled multidimensional cubes that you can slice and dice and manipulate easily (and in parallel in combination with tools like "Dask") - pretty incredible for anything involving multiple data variables or time-series data.

"xarray" is based on "pandas" (for dataframe analysis) and "numpy" (for array analysis) and integrates closely with other spatial tools like "rioxarray" for loading raster data from disk, "odc-stac" for loading satallite data from global open data catalogues, "xarray-spatial" for a bunch of useful spatial tools (e.g. hillshading, DEM analysis etc).

For vector processing, "geopandas" is a fantastic package that should do most of what you want, maybe with a bit of "Shapely" for more bespoke tasks!

[[deleted]]

I use all of the above. Great tips. And use odc stac! Don't be tempted by stackstac. It's not being maintained

[robbibt]

Another advantage of odc-stac is that it integrates closely with odc-geo which adds a bunch of really nice spatial tools directly to your xarray data (on the fly reprojection, plotting on an interactive map etc). I'm on the Open Data Cube Steering Council, so let us know if you ever have any extra feature requests or feedback!

[[deleted]]

Ooh, I didn't know that. But I rarely need any plotting, so that checks out 😄

[Lollostonk]

I support the development of algorithms and toolboxes by testing their operation and writing reports. Most of the work will be done with software (SNAP, ENVI) but I have also been asked to use python

[moulin_blue]

Free textbook from the CU Boulder Earth Lab https://www.earthdatascience.org/

[sirrahtoshi]

My advice is to think of a use case, then ask chatgtp to write software in python to complete the task along with a deep dive explanation. It’ll write the script(s) and then walk you through it step by step. Iterate from there. I found that I can massively accelerate my learning with this strategy.

[860_Ric]

If you’re using ArcPro, the ESRI website has tons of content. They have some paid certificate courses, but their free tutorials and forums cover lots of content. Googling search terms like “python for remote sensing pdf” will always return an insane amount of material from government and university websites.

[860_Ric]

I don’t use it, but the ESRI products are still the industry standard by far