07-11, 08:00–12:00 (America/Chicago), Classroom 104
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.
The Earth is constantly changing, which creates significant challenges for the environment and human society. To tackle these challenges on a global scale, the Earth science community relies heavily on geospatial datasets that are collected through various means, such as satellite, aerial, and mobile sensors. However, the explosive growth of geospatial datasets over the past few decades has overwhelmed the Earth science community's capacity for storage, analysis, and visualization. Fortunately, the advent of cloud-computing platforms (e.g., Google Earth Engine) has made it possible to access, manipulate, and analyze large volumes of geospatial data on-the-fly. In recent years, Earth Engine has become increasingly popular in the geospatial community and has enabled numerous Earth science applications at local, regional, and global scales.
The geemap Python package is built upon the Earth Engine Python API and open-source mapping libraries. It allows Earth Engine users to interactively manipulate, analyze, and visualize geospatial big data in a Jupyter environment. Since its creation in April 2020, geemap has received over 2,500 GitHub stars and is being used by over 800 projects on GitHub. More than 130 Jupyter notebook examples and an open-access book are available for learning geemap.
This tutorial consists of seven 30-minute sessions and three 10-minute breaks. During each hands-on session, the attendees will walk through Jupyter notebook examples on Google Colab with the instructors. At the end of each session, they will complete a hands-on exercise to apply the knowledge they have learned. The topics that will be covered in this tutorial include: (1) Introduction to Earth Engine and geemap; (2) Using Earth Engine data; (3) Visualizing Earth Engine data; (4) Analyzing Earth Engine data; (5) Exporting Earth Engine data; (6) Creating satellite timelapse animations; and (7) Developing and deploying interactive Earth Engine web apps.
This tutorial is intended for scientific programmers, data scientists, geospatial analysts, and concerned citizens of Earth. Attendees should have a basic understanding of Python and the Jupyter ecosystem. Familiarity with Earth science and geospatial datasets is not necessary, but it will be helpful. For more information about Earth Engine and geemap, visit https://earthengine.google.com and https://geemap.org.
The attendees are required to have a Google Earth Engine account. Sign up for an account for free at https://code.earthengine.google.com/register.
Installation Instructions –https://geemap.org/workshops/SciPy_2023 | Attendees can run the notebook directly on Google Colab by clicking on the 'Open in Colab' badge at the top or download it to their computer by clicking on the download icon in the upper right corner. The notebook is also hosted on GitHub here. Attendees also need to register for an Earth Engine account and create a Cloud Project before the conference. Detailed instructions can be found at https://geemap.org/workshops/SciPy_2023/#prerequisites. Email [email protected] for help.
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.
Steve is passionate about using machine learning and remote sensing technology to tackle the climate and sustainability crises. He leads the Developer Relations team for Google Earth Engine. Earth Engine is a geospatial analysis platform advancing planetary sustainability and resilience to climate change. His team helps remote sensing professionals, data scientists and machine learning engineers analyze petabytes of satellite imagery to understand and protect the earth. Earth Engine is provided free-of-charge for noncommercial and research purposes.
From 2016 through 2021, Steve led Developer Relations for BigQuery, Vertex AI and other Machine Learning and Data Analytics products in Google Cloud Platform, where he focused on improving the experience for users of scikit-learn, XGBoost and TensorFlow.
Steve also co-leads Google's largest grassroots sustainability group - organizing Googlers to incubate new climate initiatives. Three of the climate areas he's worked on - wind energy prediction, real-time precipitation modeling and sustainable building design - have graduated into full-time projects at Google. Prior to joining Google in 2016, Steve led engineering at a Seattle startup helping governments be more accountable to their citizens with public data. Before 2012, Steve was a Program Manager working on various data efforts in Microsoft's Office team.