Loading...
Extracting Spatiotemporal Objects From Raster Data To Represent Physical Features and Analyze Related Processes
Journal Title
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Readers/Advisors
Journal Title
Term and Year
Publication Date
2017-01-01
Book Title
Publication Volume
IV-4/W2
Publication Issue
Publication Begin
Publication End
Number of pages
Files
Research Projects
Organizational Units
Journal Issue
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.
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.