PratyushTripathy/COINS

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Python
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MIT License
Created over 4 years ago
Updated over 1 year ago

PratyushTripathy/COINS

PratyushTripathy

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This repository contains the source code of the COINS tool that allows to deduce natural continuity of street network.

global_flood_mapper

This repository contains links to the Global Flood Mapper (GFM). Usage instructions are given here. For more details, please check the journal article titled "Global Flood Mapper: A novel Google Earth Engine application for rapid flood mapping using Sentinel-1 SAR."

Landsat-Classification-Using-Neural-Network

All the files mentioned in the article on Towards Data Science Neural Network for Landsat Classification Using Tensorflow in Python | A step-by-step guide.

QGIS-Plugin-Produce-Training-Samples-For-Deep-Learning

Landsat-Classification-Using-Convolution-Neural-Network

Source code and files mentioned in the medium post titled "Is CNN equally shiny on mid-resolution satellite data?" available at https://towardsdatascience.com/is-cnn-equally-shiny-on-mid-resolution-satellite-data-9e24e68f0c08

pyrsgis

This repository cointains the source code of the 'pyrsgis' Python package.

python_gdal_automated_windows

This repository contains the script for automated download, installation and set-up of Python and GDAL.

Land-Cover-Using-Machine-Learning

This repository contains links to resources for land cover classification.

Kathmandu_urban_growth_gwr

Jupyter Notebook

pixel_level_land_classification

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

Jupyter Notebook