PratyushTripathy/Kathmandu_urban_growth_gwr

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PratyushTripathy/Kathmandu_urban_growth_gwr

PratyushTripathy

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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.

COINS

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

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.

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