Tutorial: Geospatial data processing, analysis and visualization using Python


The satellite data is one of the biggest source of external data. Satellite data consists of raster images which also contains latent information about the geographic location the image is taken from.
Processing this data in Python gives access to a lot of latest insights and visualizations that can be proved useful in decision making. This workshop will be covering the acquisition of Sentinel-2 data over, Taiwan and processing it and then finding the landcover classification of Taiwan using machine learning models.

The workshop would be in Python and tools like Geopandas, Shapely, Rasterio and Scikit-learn will be used.


Geospatial data is data containing a spatial component – describing objects with a reference to the planet's surface. This data usually consists of a spatial component, of various attributes, and sometimes of a time reference (where, what, and when). Efficient processing and visualization of small to large-scale spatial data is a challenging task. This workshop describes how to process and visualize geospatial vector and raster data using Python and the Jupyter Notebook. There are numerous modules available which help using geospatial data in using low- and high-level interfaces. We will look at shapely, which is used for manipulation and analysis of geometric objects. Then we go further to Fiona – a module which handles geospatial vector data in a very pythonic way. We move on to raster data processing using the rasterio module and briefly look at the pyproj module which is used for transforming spatial reference systems. After that we look at GeoPandas which is basically an extended pandas module with support for geodata. At the end we will see how maps are created using the cartopy and folium modules. At the end of the talk some examples are shown how to use deep learning for raster analysis using a GPU cluster.




Prakhar Srivastava

Greetings to everyone,

I'm Prakhar Srivastava, researcher, open source lover, and a student. I have worked on Deep learning models for 3 years now and mentor the deeplearning.ai course on Coursera. I've researched with India's leading research college, IIIT Delhi (http://midas.iiitd.edu.in/team/prakhar.html). I've worked as a student developer in GSoC'18 under OpenAstronomy and as a team leader at Stanford Scholar initiative. I've hosted complete lecture sessions on Deep learning in my college under IEEE. I'm currently working as a Machine learning and research intern at SocialCops, one of India's leading startups.