semantic_segmentation_satellite_image
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msahamed
This repository contains all the codes and data for semantic geologic segmentation of Landsat 8 images
semantic_segmentation_satellite_image
Basic Information:
In this project, I develop machine learning models to detect geological features from Landsat 8 images. In this project, I aim to develop machine learning models that can detect different geologic features especially faults from satellite images. I have used a deep neural network and random forest algorithms to identify features from Landsat 8 images. The model is powerful that it can even detect recently developed river bar, and sharp fault bends. I am still working on the model to make it better predictor for geoscientists.
For inquiry:
For anything about the implementations, please feel free to write me an email :
Sabber Ahamed [email protected] Github: msahamed medium: @sabber LinkedIn: sabber-ahamed
Notebook files/folder :
Codes and libraries
This project requires Python 3+. I have Used python 3.6.4. The following Python libraries are also required:
Datasets
Landsat Images can be downloaded from USGS earth explorer
Bug reports
Bug reports, comments, and suggestions are always welcome. The best the channel is to create an issue on the Issue Tracker here at the repository : https://github.com/msahamed/semantic_segmentation_satellite_image/blob/master/image_classification.ipynb
License
This program is free software: you can redistribute it and modify it under the terms of the MIT.
msahamed