07-12, 10:45–11:15 (US/Pacific), Room 316
Google Earth Engine's new data extraction interfaces seamlessly transfer geospatial data into familiar Python formats provided by NumPy, Pandas, GeoPandas, and Xarray. This integration empowers you to harness Earth Engine's vast data catalog and compute power directly within your preferred Python workflows. For example, the Xee library leverages Xarray's lazy evaluation and Dask to streamline the extraction and analysis of Earth Engine data, offering a more Pythonic alternative to traditional image exports. Earth Engine's new data extraction interfaces unlock fresh geospatial analysis potential by leveraging the unique strengths of both the scientific Python ecosystem and Earth Engine.
The rapid growth of open-access geospatial datasets has presented both a wealth of opportunities and daunting challenges for the Earth science community. The storage, analysis, and visualization of these vast amounts of data strain traditional resources. Google Earth Engine has emerged as a powerful solution, offering a cloud-computing platform that is freely available for research, education, and non-profit use. Its popularity in geospatial analysis continues to grow, enabling applications across scales from local to global. Earth Engine's JavaScript and Python APIs facilitate versatile processing and visualization of large-scale geospatial datasets. The geemap Python package further simplifies analysis and interactive visualization within Jupyter environments, requiring minimal coding. However, a persisting challenge lies in efficiently exporting large volumes of data from Earth Engine for integration into external data pipelines.
Earth Engine's new data extraction interfaces expand the ways you can leverage the Earth Engine cloud-computing platform. For example, by seamlessly integrating Earth Engine's vast data catalog with Xarray, a popular Python package for dealing with n-dimensional arrays, Xee empowers you to work with geospatial data using your preferred array management tool. Through Xarray's powerful lazy evaluation and Dask integration, Xee enables you to analyze Earth Engine data directly without the need for time-consuming exports. This bridge between Earth Engine and Xarray unlocks new analysis potential and paves the way for seamless integration with the broader SciPy and Pangeo Data ecosystems.
The new extraction interfaces allow for smooth interaction with the standard Python client, ensuring you can leverage the strengths of both Earth Engine's powerful compute platform and Python's specialized data analysis and visualization capabilities. These interfaces streamline geospatial analysis workflows, significantly reducing the need for cumbersome data exports. Developers gain direct access to the rich and diverse scientific Python ecosystem, enabling the extraction of deeper insights from Earth Engine data using a wide range of tools and techniques.
Dr. Qiusheng 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 and a Senior Research Fellow at the United Nations University. 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 https://github.com/opengeos.
Justin is a developer relations engineer supporting the Google Earth Engine project. Justin has a background in satellite image data processing for ecological applications and a passion for educating and inspiring people to use geospatial technologies to tackle environmental challenges.
A Google Developer Expert for Google Earth Engine and Senior Product Manager at MAXAR, I lead Developer Relations and champion open data access apart from working on core APIs and infrastructure. I leverage geospatial expertise as an affiliate Faculty at the University of Hawaiʻi at Mānoa and I am a Designated Campus Colleague at the University of Arizona. Passionate about community building, I created the "Awesome Google Earth Engine Community Catalog," a thriving data commons. My research explores big data analysis and geospatial applications, while I advocate for science communication and empower researchers through collaborative platforms and speaking engagements.