Artificial Intelligence in Geographic Information Systems (GIS)
in Python2024 Offer for All Users
About this course
Summary
In this self-paced course, you will learn about the concept of Open Machine Learning in Earth Observation. It will take you about 18 hours to complete the course. The provision of these train materials are meant for self-directed independent learning. No certification is provided.
Who is this course for?
This course has been created for everyone with a background in data science, data engineering and machine learning (ML), with a strong interest in geospatial and remote sensing data as well as related business models.
What will you learn?
This course introduces the concepts Machine learning, GIS, Remote Sensing and GIS data collection methods.
How much time will you need?
It will take you around 18 hours to complete this course.
- Topics Overview: Introduction to GIS; Introduction to Remote Sensing; GIS data collection methods; Introduction to ML and Python
- Course Developer: This course has been developed by GFA Consulting Group GmbH in cooperation with GIZ, Fair Forward and Digital Transformation Center Rwanda.
- Content License: CC-BY-SA
- GitHub Repo: https://github.com/GFA-DIU/ml4eo_course.git
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WHAT is GIS?
WHY is GIS relevant/useful?
WHO uses GIS?
FOR WHAT purpose or FOR WHOM is GIS used?
In this session, we’re going to learn about several forms of GIS data (spatial data types) and associated properties. We’re also going to learn about different sources of spatial data and instruments to collect them with consideration of data quality requirements such as raster data resolutions, GPS receiver accuracies.
Now that you know more about GIS, the types and models of data and how to collect them, we will dive into Mapping references.
Our planet is round so we can’t project it on a plane map without deformations.
People in the past used different projections and developed different coordinate systems to display data.
In this session you will learn more about geographic coordinate system and projections.
Before we can try this all out, it will be good to get a more in-depth understanding of Spatial Data Analysis. This session will help you get through the main concepts and tools to make the necessary analysis with the data. By using spatial data analysis, you will work on transforming raw data into valuable insights. While working on data, there’s always a question that you need to answer: “which analysis function you want to use to solve the problem”.
Let’s dig into next session and find out!
Congrats! You made it through first part of theory – introduction to GIS!
Now that you have expanded your knowledge, we will do an exercise on that topic.
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