• Introduction to Geospatial Analysis

The "Introduction to Geospatial Analysis" provides foundational knowledge on geospatial data manipulation and analysis techniques. This resource is ideal for beginners looking to understand the basics of geospatial concepts and tools. 

• Geospatial Tabular Machine Learning

This Colab notebook, "Geospatial Tabular Machine Learning," delves into applying machine learning techniques to geospatial tabular data. It covers the end-to-end process from data preparation to model training and evaluation. This resource is suited for data scientists who are venturing into geospatial machine learning. 

• Geospatial Deep Learning: Let us map Africa! Intro to Geospatial Machine Learning

For those interested in leveraging deep learning for geospatial analysis, the "Geospatial Deep Learning" Colab notebook introduces practical approaches to solving geospatial problems using neural networks. It includes examples and exercises to facilitate hands-on learning. 

• Interactive Geospatial Mapping

"Interactive Geospatial Mapping" explores the task of mapping a region of interest using interactive tools, open data, and affordable models. This GitHub repository offers tools and scripts to enhance the visual representation of geospatial information, making it a valuable resource for data visualization enthusiasts. 

• https://arxiv.org/abs/2306.00114

• https://data.apps.fao.org/catalog/iso/f1d0a319-bb49-4b45-97f0-fc7ccb931f48

• https://data.apps.fao.org/catalog/iso/69be3461-320f-40a6-93d7-fa4ed3db77d1

• https://openreview.net/forum?id=JtjzUXPEaCu

• https://www.sciencedirect.com/science/article/pii/S1569843223000158

• https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset

• https://arxiv.org/abs/2306.00114

• https://beta.source.coop/rti/rwanda-crop-type/

• https://github.com/dl4sits/BreizhCrops/tree/master

• https://sustainlab-group.github.io/sustainbench/docs/datasets/sdg2/crop_type_mapping_ghana-ss.html