Spatial Data Management with Google Earth Engine

About This Course

Spatial data are key ingredients for spatial analysis. In many GIS projects, you will need to find the spatial data suitable for your project needs, and manage them in an effective and efficient manner. Without suitable and well-managed spatial data, a GIS cannot exert its full power. In this course, we will delve into the world of spatial data models and explore various techniques for efficiently retrieving and managing spatial data. Throughout the semester, we will cover a range of topics that are central to spatial data management. One key aspect we will focus on is geospatial big data, discussing its characteristics, challenges, and potential applications. We will explore Google Earth Engine, a powerful cloud-based platform for analyzing geospatial data at scale.

What You Will Learn

Know the commonly used vector and raster formats

Understand the basics of Python (e.g., variables, data types, functions, loops, modules)

Use practical tools for data science (e.g., Jupyter notebook, Colab, Anaconda, VS Code)

Explain the Earth Engine data types (e.g., Image, ImageCollection, FeatureCollection)

Visualize local vector and raster datasets interactively in a Jupyter environment

Visualize Earth Engine vector and raster datasets interactively in a Jupyter environment

Perform geospatial analysis with Earth Engine datasets

Export Earth Engine datasets

Target Audience

The course on vector and raster formats, Python basics, and geospatial analysis with Earth Engine is designed for:

Students interested in learning about geospatial data science and analysis

Data analysts and scientists who want to enhance their skills in working with geospatial data

GIS professionals who want to expand their knowledge of geospatial analysis using Python and Earth Engine

Researchers and scientists in fields such as environmental science, agriculture, climate change, and urban planning who need to analyze and visualize geospatial data

Course Instructor

Qiusheng Wu

Dr. Qiusheng Wu is a Senior Research Fellow at the United Nations University, Institute for Water Environment and Health. As Environmental Change Data Visualization and Training Lead at UNU-INWEH, he is leading a new research and training initiative on topics such as environmental change, surface water dynamics, and geospatial data visualization. Dr. Wu is an Associate Professor in the Department of Geography & Sustainability at the University of Tennessee, Knoxville. In addition, he holds positions as an Amazon Visiting Academic. Specializing in geospatial data science and open-source software development, Dr. Wu is particularly focused on leveraging big geospatial data and cloud computing to study environmental changes, with an emphasis on surface water and wetland inundation dynamics. He is the creator of several open-source packages designed for advanced geospatial analysis and visualization, including geemap, leafmap, and segment-geospatial. For a closer look at his open-source contributions, please visit his GitHub repositories at opengeos.

Course Contact

Environmental Change Data Visualization and Training Lead, United Nations University Institute for Water, Environment and Health, Associate Professor in the Department of Geography and Sustainability at the University of Tennessee, Knoxville

Senior Researcher: Water Resources Management
United Nations University Institute for Water, Environment and Health

United Nations University

Institute for Water, Environment and Health

UNU-INWEH: INWEH-19 Spatial Data Management with Google Earth Engine

United Nations University