Qiusheng Wu
Qiusheng Wu is an Associate Professor in the Department of Geography & Sustainability at the University of Tennessee, Knoxville. He is also an Amazon Visiting Academic and a Google Developer Expert (GDE) for Earth Engine. His research focuses on Geographic Information Science, remote sensing, and open-source software development. Dr. Wu is an advocate of open science and reproducible research. He has developed several open-source packages that have been widely used by the geospatial community, such as geemap and leafmap. For more information about his research, visit https://wetlands.io.
Sessions
This tutorial is an introduction to cloud-based geospatial analysis with Earth Engine and the geemap Python package. We will cover the basics of Earth Engine data types and how to visualize, analyze, and export Earth Engine data in a Jupyter environment using geemap. We will also demonstrate how to develop and deploy interactive Earth Engine web apps. Throughout the session, practical examples and hands-on exercises will be provided to enhance learning. The attendees should have a basic understanding of Python and Jupyter Notebooks. Familiarity with Earth science and geospatial datasets is not required, but will be useful.
Google Earth Engine is a cloud-computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. Built upon the Earth Engine Python API and open-source mapping libraries, geemap enables Earth Engine users to interactively manipulate, analyze, and visualize geospatial big data in a Jupyter environment. This presentation introduces Earth Engine and highlights the key features of geemap for interactive mapping and geospatial analysis with Earth Engine. Attendees can utilize geemap to create satellite timelapse animations for any location on Earth within 60 seconds. Additional resources will be provided to the attendees to learn more about geemap.
In this talk, we will provide an overview of the Open Data Program on AWS using real-world examples for how users can start building workflows today with minimal coding experience using open source geospatial visualization and analysis tools such as Leafmap, SpatioTemporal Asset Catalog (STAC) and AWS. We’ll leverage simple python notebook examples using SageMaker Studio Lab, a free machine learning (ML) development environment that provides the compute, storage and security for anyone to learn and experiment with Machine Learning.