Qiusheng Wu
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.
Sessions
Image segmentation plays a crucial role in extracting valuable insights from geospatial data. While traditional segmentation methods can be laborious, deep learning offers automation but often demands extensive training and resources. Meta AI's Segment Anything Model (SAM) presents a compelling solution, segmenting objects without additional training. Our open-source Python package, samgeo, streamlines the use of SAM for geospatial data, offering various segmentation methods. Experiments confirm SAM's accuracy and efficiency as a powerful tool for remote sensing analysis. The samgeo package simplifies the adoption of automated image segmentation, facilitating better geospatial insights and decision-making across multiple domains.
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 Google Earth Engine (GEE) Community Catalog is a geospatial data catalog with user-contributed geospatial datasets, which are processed by the maintainers and then made available for the larger geospatial community to use. This talk delves into the origins of the catalog, exploring its capabilities and showcasing its impact on various scientific and environmental applications. We will explore some of the datasets available, covering topics like land cover change and forest health using the ipyleaflet based geemap package and the Earth Engine Code Editor. We will also highlight why contributing to the catalog fosters a spirit of open collaboration and knowledge sharing.