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    Extracting Spatiotemporal Objects From Raster Data To Represent Physical Features and Analyze Related Processes

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    Author
    Zollweg, James A.
    Keyword
    Feature Extraction
    Remote Sensing
    Object-Oriented Modelling
    Storm Elements
    Journal title
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Date Published
    2017-01-01
    Publication Volume
    IV-4/W2
    
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/20.500.12648/2196
    Abstract
    Numerous ground-based, airborne, and orbiting platforms provide remotely-sensed data of remarkable spatial resolution at short time intervals. However, this spatiotemporal data is most valuable if it can be processed into information, thereby creating meaning. We live in a world of objects: cars, buildings, farms, etc. On a stormy day, we don’t see millions of cubes of atmosphere; we see a thunderstorm ‘object’. Temporally, we don’t see the properties of those individual cubes changing, we see the thunderstorm as a whole evolving and moving. There is a need to represent the bulky, raw spatiotemporal data from remote sensors as a small number of relevant spatiotemporal objects, thereby matching the human brain’s perception of the world. This presentation reveals an efficient algorithm and system to extract the objects/features from raster-formatted remotely-sensed data. The system makes use of the Python object-oriented programming language, SciPy/NumPy for matrix manipulation and scientific computation, and export/import to the GeoJSON standard geographic object data format. The example presented will show how thunderstorms can be identified and characterized in a spatiotemporal continuum using a Python program to process raster data from NOAA’s High-Resolution Rapid Refresh v2 (HRRRv2) data stream.
    Citation
    Zollweg, J. (2017). Extracting Spatiotemporal Objects From Raster Data To Represent Physical Features And Analyze Related Processes. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences , (pp. 87-92). Cambridge.
    DOI
    https://doi.org/10.5194/isprs-annals-IV-4-W2-87-2017
    Description
    This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-4-W2-87-2017 | © Authors 2017. CC BY 4.0 License.
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.5194/isprs-annals-IV-4-W2-87-2017
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