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update courses and books
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robmarkcole committed Mar 31, 2021
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* [godal](https://github.com/airbusgeo/godal) -> golang wrapper for GDAL
* [Write rasterio to xarray](https://github.com/robintw/XArrayAndRasterio/blob/master/rasterio_to_xarray.py)

## Python general utilities
* [PyShp](https://github.com/GeospatialPython/pyshp) -> The Python Shapefile Library (PyShp) reads and writes ESRI Shapefiles in pure Python
* [s2p](https://github.com/cmla/s2p) -> a Python library and command line tool that implements a stereo pipeline which produces elevation models from images taken by high resolution optical satellites such as Pléiades, WorldView, QuickBird, Spot or Ikonos
* [EarthPy](https://github.com/earthlab/earthpy) -> A set of helper functions to make working with spatial data in open source tools easier. read[Exploratory Data Analysis (EDA) on Satellite Imagery Using EarthPy](https://towardsdatascience.com/exploratory-data-analysis-eda-on-satellite-imagery-using-earthpy-c0e186fe4293)
* [pygeometa](https://geopython.github.io/pygeometa/) -> provides a lightweight and Pythonic approach for users to easily create geospatial metadata in standards-based formats using simple configuration files
* [pesto](https://airbusdefenceandspace.github.io/pesto/) -> PESTO is designed to ease the process of packaging a Python algorithm as a processing web service into a docker image. It contains shell tools to generate all the boiler plate to build an OpenAPI processing web service compliant with the Geoprocessing-API. By [Airbus Defence And Space](https://github.com/AirbusDefenceAndSpace)
* [GEOS](https://geos.readthedocs.io/en/latest/index.html) -> Google Earth Overlay Server (GEOS) is a python-based server for creating Google Earth overlays of tiled maps. Your can also display maps in the web browser, measure distances and print maps as high-quality PDF’s.
* [GeoDjango](https://docs.djangoproject.com/en/3.1/ref/contrib/gis/) intends to be a world-class geographic Web framework. Its goal is to make it as easy as possible to build GIS Web applications and harness the power of spatially enabled data. [Some features of GDAL are supported.](https://docs.djangoproject.com/en/3.1/ref/contrib/gis/gdal/)
* [rasterstats](https://pythonhosted.org/rasterstats/) -> summarize geospatial raster datasets based on vector geometries
* [turfpy](https://turfpy.readthedocs.io/en/latest/index.html) -> a Python library for performing geospatial data analysis which reimplements turf.js

## Python low level numerical & data formats
* [xarray](http://xarray.pydata.org/en/stable/) -> N-D labeled arrays and datasets. Read [Handling multi-temporal satellite images with Xarray](https://medium.com/@bonnefond.virginie/handling-multi-temporal-satellite-images-with-xarray-30d142d3391). Checkout [xarray_leaflet](https://github.com/davidbrochart/xarray_leaflet) for tiled map plotting
* [xarray-spatial](https://github.com/makepath/xarray-spatial) -> Fast, Accurate Python library for Raster Operations. Implements algorithms using Numba and Dask, free of GDAL
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* [tiler](https://github.com/nuno-faria/tiler) -> split images into tiles and merge tiles into a large image
* [felicette](https://github.com/plant99/felicette) -> Satellite imagery for dummies. Generate JPEG earth imagery from coordinates/location name with publicly available satellite data.

## Python general utilities
* [PyShp](https://github.com/GeospatialPython/pyshp) -> The Python Shapefile Library (PyShp) reads and writes ESRI Shapefiles in pure Python
* [s2p](https://github.com/cmla/s2p) -> a Python library and command line tool that implements a stereo pipeline which produces elevation models from images taken by high resolution optical satellites such as Pléiades, WorldView, QuickBird, Spot or Ikonos
* [EarthPy](https://github.com/earthlab/earthpy) -> A set of helper functions to make working with spatial data in open source tools easier. read[Exploratory Data Analysis (EDA) on Satellite Imagery Using EarthPy](https://towardsdatascience.com/exploratory-data-analysis-eda-on-satellite-imagery-using-earthpy-c0e186fe4293)
* [pygeometa](https://geopython.github.io/pygeometa/) -> provides a lightweight and Pythonic approach for users to easily create geospatial metadata in standards-based formats using simple configuration files
* [pesto](https://airbusdefenceandspace.github.io/pesto/) -> PESTO is designed to ease the process of packaging a Python algorithm as a processing web service into a docker image. It contains shell tools to generate all the boiler plate to build an OpenAPI processing web service compliant with the Geoprocessing-API. By [Airbus Defence And Space](https://github.com/AirbusDefenceAndSpace)
* [GEOS](https://geos.readthedocs.io/en/latest/index.html) -> Google Earth Overlay Server (GEOS) is a python-based server for creating Google Earth overlays of tiled maps. Your can also display maps in the web browser, measure distances and print maps as high-quality PDF’s.
* [GeoDjango](https://docs.djangoproject.com/en/3.1/ref/contrib/gis/) intends to be a world-class geographic Web framework. Its goal is to make it as easy as possible to build GIS Web applications and harness the power of spatially enabled data. [Some features of GDAL are supported.](https://docs.djangoproject.com/en/3.1/ref/contrib/gis/gdal/)
* [rasterstats](https://pythonhosted.org/rasterstats/) -> summarize geospatial raster datasets based on vector geometries
* [turfpy](https://turfpy.readthedocs.io/en/latest/index.html) -> a Python library for performing geospatial data analysis which reimplements turf.js

## Python deep learning toolsets
* [TorchSat](https://github.com/sshuair/torchsat) is an open-source deep learning framework for satellite imagery analysis based on PyTorch.
* [torchvision-enhance](https://github.com/sshuair/torchvision-enhance) -> Enhance PyTorch vision for semantic segmentation, multi-channel images and TIF file
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# Courses
* [Manning: Monitoring Changes in Surface Water Using Satellite Image Data](https://liveproject.manning.com/course/106/monitoring-changes-in-surface-water-using-satellite-image-data?)
* [Automating GIS processes](https://automating-gis-processes.github.io/2016/index.html) includes a lesson on automating raster data processing
* For deep learning checkout the [fastai course](https://course.fast.ai/) which uses the fast.ai library & pytorch
* [pyimagesearch.com](https://www.pyimagesearch.com/) hosts courses and plenty of material using opencv and keras
* Official [opencv](https://opencv.org/courses/) courses
* [TensorFlow Developer Professional Certificate](https://www.coursera.org/professional-certificates/tensorflow-in-practice)

# Books with code
# Books
* [Image Analysis, Classification and Change Detection in Remote Sensing With Algorithms for Python, Fourth Edition, By Morton John Canty](https://www.routledge.com/Image-Analysis-Classification-and-Change-Detection-in-Remote-Sensing-With/Canty/p/book/9781138613225) -> code [here](https://github.com/mortcanty/CRC4Docker)
* I highly recommend [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python-second-edition) by François Chollet

# Online communities
* [fast AI geospatial study group](https://forums.fast.ai/t/geospatial-deep-learning-resources-study-group/31044)
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