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GitHub topics: lulc-classification

connorcrowe/to-lulc-aiml

Land use land cover (lulc) classification of aerial imagery using machine learning techniques including U-Net architecture Convolutional Neural Networks (CNNs).

Language: Jupyter Notebook - Size: 221 MB - Last synced at: 5 days ago - Pushed at: 5 days ago - Stars: 1 - Forks: 0

13wejay/LULC-Processor-Jupyter

Jupyter Notebook Python Script for Analyzing LULC Changes

Language: Jupyter Notebook - Size: 9.77 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

SubhangiSati/Land-Use-Land-Cover-Classification

This project focuses on land use and land cover classification using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The classification task aims to predict the category of land based on satellite or aerial images.

Language: Python - Size: 26.4 KB - Last synced at: 3 months ago - Pushed at: 11 months ago - Stars: 2 - Forks: 0

ivan-murano/LULC-BUILDER

This repository is intended to provide land use/land cover creators with a set of tools to facilitate their tasks.

Size: 2.93 KB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 0 - Forks: 0

DAWOODSKYM/Geospatial_DataScience

This repo contains javascript code used in Google Earth engine to perform various Geospatial Data analysis tasks on satellite data. The code utilizes Google earth engines own archive of Satellite data.

Size: 21.5 KB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

pratyaymishra589/Urban-Prediction-Model

Language: Jupyter Notebook - Size: 18.6 KB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

iamtekson/deep-learning-for-earth-observation

Application of deep learning for earth observation.

Language: Jupyter Notebook - Size: 225 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 77 - Forks: 25

rohitgandikota/Land-Use-Land-Cover-Classification-of-Satellite-Images-using-Deep-Learning

This work discusses how high resolution satellite images are classified into various classes like cloud, vegetation, water and miscellaneous, using feed forward neural network. Open source python libraries like GDAL and keras were used in this work. This work is generic and can be used for satellite images of any resolution, but with MX band sensors.

Language: Python - Size: 11.7 KB - Last synced at: over 1 year ago - Pushed at: over 5 years ago - Stars: 2 - Forks: 3

storm-platform/experiment-rep-cbersdatacube

Executable Research Compendium para a geração de mapas de Uso e Cobertura da Terra utilizando Cubos de dados de imagens de Satélite

Language: Jupyter Notebook - Size: 176 KB - Last synced at: 2 days ago - Pushed at: almost 4 years ago - Stars: 3 - Forks: 0