Deep learning for Geospatial data applications — Semantic Segmentation
A beginner’s guide and tutorial for Segmenting satellite images with Fastai
Semantic Segmentation is the process of labelling pixels or regions of the image. In Geospatial, labelling pixels for satellite images is essential in many applications including infrastructure planning, land cover, humanitarian crisis maps and environmental assessments.
Therefore, automatic segmentation, using deep learning and computer vision, can significantly help many tasks and add economic value in geospatial and earth observation domains.
In the first blog, we have covered multi-label classifications using deep learning for satellite imagery.
In this blog post, I will show the easiest way to use Deep Learning for Geospatial Applications. I will go through training a state-of-the-art deep learning model with Satellite image data. We use Fastai Version 2 built on top of Pytorch, to train our model. It will take less than ten lines of python code to accomplish this task. If you have read the first post of this series, we only need to change a few methods. That is the beautify of using Fastai.
You also do not need to worry about the Graphics Processing Unit (GPU) as we use the freely available GPU…
