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Forward Support—Cloud Based Mosaic Imagery for Emergency Operations

DOI: 10.4236/jgis.2020.122005, PP. 84-95

Keywords: Cloud Storage, Mosaic, Raster, Tiling, Pyramids, OptimizeRasters, MDCS

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Abstract:

The Institute for Creative Technologies (ICT) has pursued the creation of One World Terrain (OWT), which aims to provide a set of 3D global terrain capabilities and services that can replicate the coverage and complexities of the operational environment. Research was conducted in support of One World Terrain through development of best practices for the delivery of a raster mosaic via cloud hosting service, created using OptimizeRasters Geoprocoessing Toolbox and the Mosaic Dataset Configuration Script. Though ultimately successful in developing the raster mosaic and hosting it online; JPEG compression lossiness was a key issue with the larger Rose Bowl dataset. Additionally, hosting the imagery via ArcGIS Online was found to increase the compressed file size; making it comparable to the original file size of the data. Future testing should consider usage of an enterprise server to avoid this issue. MRF_LERC compression was identified as the ideal file configuration; and ArcGIS Online was identified as a poor enterprise hosting medium. We have also identified a variety of ways to improve the MDCS script in order to automate the whole process more efficiently.

References

[1]  Institute for Creative Technologies. One World Terrain.
http://ict.usc.edu/prototypes/one-world-terrain-owt/
[2]  Benkelman, C. ArcGIS Workflows for Optimizing Image Management Services in the Cloud.
https://proceedings.esri.com/library/userconf/devsummit17/papers/dev_int_13.pdf
[3]  ESRI. Mosaic Dataset. ArcMap.
https://doc.arcgis.com/en/imagery/workflows/standard-workflow/overview/overview-mosaic-datasets.htm
[4]  Xu, H. and Becker, P. (2012) ArcGIS Data Models for Managing and Processing Imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B4.
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B4/97/2012/isprsarchives-XXXIX-B4-97-2012.pdf
https://doi.org/10.5194/isprsarchives-XXXIX-B4-97-2012
[5]  Huang, W., Wang, C. and Tang, D. (2018) Design and Applications of Rapid Image Tile Producing Software Based on Mosaic Dataset. The International Archives of the Photo-Grammetry, Remote Sensing, and Spatial Information Sciences, XLII-3.
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/2225/2018/isprs-archives-XLII-3-2225-2018.pdf
https://doi.org/10.5194/isprs-archives-XLII-3-2225-2018
[6]  ESRI. Core Geoprocessing Tools for Raster Data.
http://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/core-geoprocessing-tools-for-raster-data.htmsa
[7]  ESRI. ArcGIS Server.
https://enterprise.arcgis.com/en/server/latest/get-started/windows/what-is-arcgis-image-server-.htm
[8]  GitHub. OptimizeRasters.
https://github.com/Esri/OptimizeRasters
[9]  ESRI (2018) OptimizeRasters: AWS Lambda Implementation. User Documentation, Redlands.
https://github.com/Esri/OptimizeRasters/blob/master/Documentation/OptimizeRasters
_UserDoc.pdf
[10]  Norton, J. (2019) Cloud Optimized GeoTIFF vs the Meta Raster Format. Element 8423 RSS.
https://www.element84.com/blog/cloud-optimized-geotiff-vs-the-meta-raster-format
[11]  Becker, P. (2016) MRF as a Cloud Optimized Raster Format and LERC Compression. ESRI White Paper, Redlands.
https://pdfs.semanticscholar.org/8267/f0b26a0b9f21c2c7d9ea3fdcc59903ac3157.pdf
[12]  ESRI. Raster Pyramids.
http://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/raster-pyramids.htm
[13]  GitHub. Mosaic Dataset Creation Scripts.
https://github.com/Esri/mdcs-py
[14]  ArcGIS Enterprise. Share Imagery as a Tiled Map Service.
https://enterprise.arcgis.com/en/server/latest/get-started/windows/share-imagery-as-an-arcgis-online-tiled-map-service.htm
[15]  ESRI (2019) Downloading Data from an Image Service.
https://desktop.arcgis.com/en/arcmap/latest/map/web-maps-and-services/downloading-from-an-image-services.htm
[16]  Harvey, C. and Patrizio, A. (2019) AWS vs. Azure vs. Google: Cloud Comparison.
https://www.datamation.com/cloud-computing/aws-vs-azure-vs-google-cloud-comparison.html

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