This repository contains a collection of Jupyter Notebooks developed as part of the course “Geospatial Raster Data Analytics in Python” by Milan Janosov, Ph.D. on LinkedIn Learning

The notebooks demonstrate practical applications of Python for working with raster and geospatial datasets, including data import, reprojection, resampling, raster calculations, zonal statistics, and multi-band analysis.

Tools & Libraries: Python 3.x Jupyter Notebook Rasterio Geopandas Numpy / Matplotlib GDAL

Learning Focus: These notebooks form part of a structured upskilling path in Remote Sensing and GIS for Conservation. The focus is on understanding raster data workflows that can later be applied to Earth Observation (EO), vegetation monitoring (NDVI), and climate-related spatial analysis.

Source & Attribution: The structure of these exercises was inspired by the course “Geospatial Raster Data Analytics in Python” on LinkedIn Learning. All code, modifications, and annotations in this repository were written and executed by Sara Lund for educational and portfolio purposes.