GEOG498I: Algorithms for Geospatial Computing
For GIS Majors, counts as upper level technical requirement.
For GEOG Majors, counts as an elective.
For GIS Minors, counts as upper level technical requirement.
Course Description
• Introduction to fundamental geometric algorithms for spatiotemporal data processing and analysis.
• Managing and clustering point clouds for processing and analysis of LiDAR data.
• Terrain modeling: representations, query algorithms, visibility and morphological analysis.
• Applications: terrain reconstruction, urban modeling, forest management and coastal data management and analysis.
• Algorithms for road network analysis and reconstruction.
• Scalable algorithms and representations for big geospatial data.
• Introduction to fundamental geometric algorithms for spatiotemporal data processing and analysis.
• Managing and clustering point clouds for processing and analysis of LiDAR data.
• Terrain modeling: representations, query algorithms, visibility and morphological analysis.
• Applications: terrain reconstruction, urban modeling, forest management and coastal data management and analysis.
• Algorithms for road network analysis and reconstruction.
• Scalable algorithms and representations for big geospatial data.
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INST208A: How NASA Sees the Earth (Taught by a GEOG Professor!)
For GIS Majors, counts as a supporting sequence requirement.
For GEOG Majors, counts as a supporting sequence requirement.
For GIS Minors, counts as an elective.
Course Description
The world of Earth science data is complex and can be overwhelming with a wide range of data sources and formats, hefty downloads (Big Data!) and the need for complicated analytical tools. In order to make use of enormous volume of available data and geoinformation products, one has to know where and how to search and obtain the data, how to analyze the data and extract useful information and knowledge. For example, what is the spatial distribution and temporal variation of Earth science variables (ESVs), such as temperature, precipitation, soil moisture, sea ice cover, aerosols, cloud cover, and vegetation cover? How to calculate climatology and anomalies of ESVs and identify long-term trends? What are spatial-temporal relationships between ESVs?
In this course, you will learn about the state-of-the-art Web-based tools that allow you to efficiently display and analyze a large number of datasets in a way many professionals working in the Earth science domain would. You will learn how to visualize multiple Earth science datasets produced by NASA in a variety of ways directly on the Internet, without the need to download, manage and store them. Students will be introduced to comprehensive functions to analyze the data and generate customized maps, animations, multi-variable correlations, regional subsetting, etc. Not only will students will acquire theoretical and practical skills necessary to analyze the data, but they will also learn how to interpret the data, extract knowledge and connect it to socio-economic information.
In this course, you will learn about the state-of-the-art Web-based tools that allow you to efficiently display and analyze a large number of datasets in a way many professionals working in the Earth science domain would. You will learn how to visualize multiple Earth science datasets produced by NASA in a variety of ways directly on the Internet, without the need to download, manage and store them. Students will be introduced to comprehensive functions to analyze the data and generate customized maps, animations, multi-variable correlations, regional subsetting, etc. Not only will students will acquire theoretical and practical skills necessary to analyze the data, but they will also learn how to interpret the data, extract knowledge and connect it to socio-economic information.
To look at course syllabi for these classes, please follow this link: https://drive.google.com/drive/folders/10_X12DrSFJcE9fQ5QLmbMIBfKyQcp1z8?usp=sharing
