Fundamentals of Geospatial Data Analysis using Foundation Models

Developed in collaboration with IBM Research, UK

Self-Learning Course

Take this course at your own pace through pre-recorded video and online resources.

Whether it’s flood damage prediction or biodiversity monitoring, large datasets are involved in geospatial data and analysis. Foundation models are quickly becoming popular in this sector to help deal with these data challenges. The aim of this course is to upskill your working knowledge on using AI models for climate and weather data analysis. This course will comprise of practical Jupyter notebooks relating to fine-tuning models.

In this level course, you will:

• Understand the fundamental concepts of foundation models
• Compare traditional weather models with AI models
• Understand the use of foundation models in industry for geospatial analysis
• Understand what is fine-tuning and how it is used with geospatial analysis
• Practice using an open source fine tuning tool – terratorch
• Practice using a machine learning platform, HuggingFace, for model downloading and finetuning
• Practice the concept of inference for geospatial analysis
• Understand future capabilities, including low-code options for analysis

Pre-requisites:

• Understanding of geospatial data and analysis at a user level.
• Understanding of python and Jupyter notebooks.
• Understanding of using Github to clone repositories and open issues if required.
• Completion of Practice Guide to Geospatial Data is highly recommended but not compulsory

CPD Accreditation

This course has CPD accreditation and on successful completion of this course, you will achieve CPD hours and be awarded a certificate. 

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