List of resources for mineral exploration and machine learning, generally with useful code and examples.

This repository hosts the code behind our research paper "A Multi-Dimensional Deep Hierarchical Approach Towards Aerial Hyperspectral Image Classification" (2021)

Techniques for deep learning with satellite & aerial imagery

A Python package for Remote Sensing Data Analysis

18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

Python for GIS and Geoscience - specialist course Doctoral schools of Ghent University

A Python package for installing commonly used packages for geospatial analysis and data visualization with only one command.

All course materials for the Zero to Mastery Machine Learning and Data Science course.

How to Learn More? Exploring the Possibility of Kolmogorov-Arnold Networks for Hyperspectral Image Classification

Repository for Jupyter Notebook examples associated with the NASA ARSET Training, "Fundamentals of Machine Learning for Earth Science"

The official repo for the paper "HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model"

A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments

Agricultural Classification and Estimation Service

Python-based extractor of vegetation metrics from satellite-based vegetation time-series imagery.

this repo contains notebooks that were used in class each week. it will sync with the hub.

End-to-end workflow for generating high resolution cropland maps

Visualizer for neural network, deep learning and machine learning models

An open-source toolbox for spectral geology

The official repository for the EuroCrops dataset.

Crop type mapping of small holder farms in Ghana, and South Sudan

Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023

Simple LaTeX template for thesis report for IAVH2 students

Code examples for the book chapter "Supervised, Semi-Supervised and Unsupervised Learning for Hyperspectral Regression".

This repository contains codes for the paper entitled "A CNN-RNN Framework for Crop Yield Prediction"

Examples of Earth observation workflows that extract valuable information from satellite imagery, giving you hints and ideas how to use the EO data.

Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.