Geospatial Data from Scratch to Advanced
Starting from how to download satellite images, what best Python packages to use, images processing, indices needed for applications like agriculture, improving the image's quality, and labeling, to a complete real-world application pipeline.
-Start by downloading raster/tif files, how to load shapefiles, and how to visualize satellite images in a friendly Guide to Folium and Rasterio (all code needed is included). https://omdena.com/blog/geospatial-data-analytics/
- Then how to build a satellite imagery dataset with these 7 steps for a quality dataset. Learn satellite imagery sources, how to decide on the best source, different distribution, indices, and image quality needed. https://omdena.com/blog/satellite-imagery-dataset/
- Now it's time to enhance the quality of the images, learn how and what tools to use by going through exploring different ways to enhancing satellite imagery to get the best quality images using Deep Learning. https://omdena.com/blog/enhancing-satellite-imagery/
- Apply what you learned on a real-world case study, learn how to detect rooftops, and give understandable rooftops classification by following a complete pipeline of an ML problem from data pre-processing and labeling to applying different neural networks models to detect rooftops. https://omdena.com/blog/rooftops-classification/