Application of deep learning for earth observation.

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 sen…

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

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.

A Google Earth Engine Land use (crops) classification workflow using Random Forest, one year of ground data, Sentinel-2, and Landsats; to produce multiyear annual 30-m crop maps

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

This Repository Houses the Code for our Capstone Thesis Titled : "A New Framework with Convoluted Oscillatory Neural Network (CONN) for Efficient Object-Based Land Use and Land Cover Classification on Remote Sensing Images"

Interactive Shiny Dashboard for monitoring multi-year Land Use / Land Cover (LULC) changes in Istanbul using spatial and statistical visualizations.

Pixel-based land cover classification in central Hanoi using Sentinel-2 imagery. Implements and compares SVM, Random Forest, and 1D CNN models to support urban planning and remote sensing applications.

Jupyter Notebook Python Script for Analyzing LULC Changes

Urban Land Use and Land Cover Classification on Google Earth Engine

This repository is intended to provide a set of QGIS tools to facilitate land use/land cover construction.

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.

This project revisits and rebuilds a previous Land Use and Land Cover (LULC) classification task on the EuroSAT dataset, using Vision Transformers (ViT) and modern PyTorch best practices.

Remote Sensing-Based ML Prediction of Dengue Fever in Indonesia