A curated list of resources focused on Machine Learning in Geospatial Data Science.

CosmiQ Works Geospatial Machine Learning Analysis Toolkit

Spatial Representations for Artificial Intelligence - a Python library toolkit for geospatial machine learning focused on creating embeddings for downstream tasks

pyGIS is an online textbook covering all the core geospatial functionality available in Python. This includes handling vector and raster data, satellite remote sensing, machine learning and deep learning applications.

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

A geospatial raster processing library for machine learning

Identifying Suitable Dam Sites Using Geospatial Data and Machine Learning: A Case Study of the Katsina-Ala River in Benue State, Nigeria" explores the integration of geospatial data and machine learning techniques to locate optimal sites for dam construction along the Katsina-Ala River.

Assessing the impact of climate change on flood patterns in downstream Nigeria using machine learning and geospatial techniques (2018–2024)

Mapping Philippine Poverty using Machine Learning, Satellite Imagery, and Crowd-sourced Geospatial Information

A python package for end-to-end geospatial machine learning using multispectral earth observation data such as NASA HLS and ESA Sentinel-2.

Air pollution constitutes a critical public health challenge in rapidly urbanising sub-Saharan African cities, where industrial activity, vehicular emissions, and meteorological dynamics converge to produce complex spatiotemporal pollution patterns. This study presents the first comprehensive geospatial machine learning analysis of multivariate

A package to train machine learning models on geospatial data, mainly for weather and climate. Used to run ArchesWeather and ArchesWeatherGen

Synthetic modeling of Urban Heat Islands (UHI) using satellite-like data. Generates spectral bands, vegetation and urban indices, and land surface temperature (LST) for testing machine learning models, validating geospatial workflows, and exploring UHI dynamics without requiring real satellite imagery.

Urban Heat Island (UHI) analysis combines satellite, climate, and urban data to model city-scale temperature anomalies. We assemble open data sources, geospatial Python libraries, and machine learning to predict UHI intensity.

RR is a data-driven research project that integrates geospatial analysis, machine learning, and environmental monitoring to analyze historical trends, predict future scenarios, and deliver actionable insights for climate resilience, sustainability, and policy decision-making.

This project provides a complete workflow for mapping, visualizing, and analyzing noise pollution levels in urban areas using geospatial and machine learning techniques.

Machine learning in geospatial data

A machine learning project for learning geospatial topology for the paper on https://arxiv.org/abs/1806.03857

My machine learning capstone projects that predicts rental rates and property values in San Francisco using two datasets (Airbnb and SF Assessor data). I incorporate publicly available geospatial data to improve the accuracy of the models.

A Python-based project for processing and analyzing geospatial data using machine learning techniques, leveraging libraries like GeoPandas, Scikit-learn, and Folium. Includes examples for spatial analysis, visualization, and predictive modeling with real-world geographic datasets.

This is a real-world business use case, often tackled with data analysis, machine learning, and geospatial visualization. working on a store placement prediction project where the goal is to visualize and predict ideal locations for placing a new store, using a map generated on your system.

An ensemble Framework for Explainable Geospatial Analysis Machine Learning Models

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CosmiQ Works Geospatial Processing Toolkit for Machine Learning

The repository contains data for the paper titled"High-resolution flood susceptibility mapping and exposure assessment in Pakistan: An integrated artificial intelligence, machine learning and geospatial framework"

Crime and Criminal Analysis System integrating geospatial, temporal, and demographic analytics for predictive modeling of criminal activities. It employs machine learning for optimizing police resource allocation and incorporates real-time social media scraping for proactive crime detection.

The ecosystem of geospatial machine learning tools in the Pangeo world.

🛰️ Python-powered remote sensing toolkit for Earth observation! From satellite image processing to feature extraction, explore advanced raster analysis techniques and unlock geospatial insights using machine learning and computer vision. Level up your remote sensing skills with hands-on tutorials and practical workflows. 🌍

The project aims to profile stocks with similar weekly percentage returns using K-Means Clustering. The project calculates realized volatility for each stock and predicts realized volatility for each stock using classical volatility models and machine learning models and comparing their performance. This is a capstone project for CIVE 7100 Time Series and Geospatial Data Sciences.

A project for the development of rich geospatial data from the city of São Paulo for use in Machine Learning models.