07-13, 10:45–11:15 (America/Chicago), Grand Salon C
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.
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, such as 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 presentation introduces Earth Engine and highlights the key features of geemap for interactive mapping and geospatial analysis with Earth Engine, such as
- Searching and loading datasets from the Earth Engine Data Catalog
- Visualizing raster and vector datasets interactively
- Using Cloud Optimized GeoTIFFs (COG) and SpatioTemporal Asset Catalogs (STAC)
- Visualizing the Dynamic World global land cover datasets
- Creating satellite timelapse animations
This presentation is intended for scientific programmers, data scientists, geospatial analysts, and concerned citizens of Earth. Attendees should have a basic understanding of Python and Jupyter Notebook. 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.
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.