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Article

A Framework for Cloud-Based Spatially-Explicit Uncertainty and Sensitivity Analysis in Spatial Multi-Criteria Models

1
Department of Geoinformatics, University of Salzburg, 5020 Salzburg, Austria
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Department of Engineering & IT, Spatial Information Management, Carinthia University of Applied Sciences, 9524 Villach, Austria
3
Department of Geography, San Diego State University, San Diego, CA 92182-4493, USA
4
Institute of Geoecology and Geoinformation, Adam Mickiewicz University, 61-680 Poznań, Poland
*
Author to whom correspondence should be addressed.
Academic Editors: Ali Mansourian and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(4), 244; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040244
Received: 19 February 2021 / Revised: 1 April 2021 / Accepted: 6 April 2021 / Published: 7 April 2021
Global sensitivity analysis, like variance-based methods for massive raster datasets, is especially computationally costly and memory-intensive, limiting its applicability for commodity cluster computing. The computational effort depends mainly on the number of model runs, the spatial, spectral, and temporal resolutions, the number of criterion maps, and the model complexity. The current Spatially-Explicit Uncertainty and Sensitivity Analysis (SEUSA) approach employs a cluster-based parallel and distributed Python–Dask solution for large-scale spatial problems, which validates and quantifies the robustness of spatial model solutions. This paper presents the design of a framework to perform SEUSA as a Service in a cloud-based environment scalable to very large raster datasets and applicable to various domains, such as landscape assessment, site selection, risk assessment, and land-use management. It incorporates an automated Kubernetes service for container virtualization, comprising a set of microservices to perform SEUSA as a Service. Implementing the proposed framework will contribute to a more robust assessment of spatial multi-criteria decision-making applications, facilitating a broader access to SEUSA by the research community and, consequently, leading to higher quality decision analysis. View Full-Text
Keywords: Spatially-Explicit Uncertainty and Sensitivity Analysis; parallel and distributed computing; SEUSA as a Service; spatial cloud computing; microservices; Spatial Multi-Criteria Decision Analysis; Python–Dask; gRPC; RasDaMan; Kubernetes Spatially-Explicit Uncertainty and Sensitivity Analysis; parallel and distributed computing; SEUSA as a Service; spatial cloud computing; microservices; Spatial Multi-Criteria Decision Analysis; Python–Dask; gRPC; RasDaMan; Kubernetes
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MDPI and ACS Style

Erlacher, C.; Anders, K.-H.; Jankowski, P.; Paulus, G.; Blaschke, T. A Framework for Cloud-Based Spatially-Explicit Uncertainty and Sensitivity Analysis in Spatial Multi-Criteria Models. ISPRS Int. J. Geo-Inf. 2021, 10, 244. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040244

AMA Style

Erlacher C, Anders K-H, Jankowski P, Paulus G, Blaschke T. A Framework for Cloud-Based Spatially-Explicit Uncertainty and Sensitivity Analysis in Spatial Multi-Criteria Models. ISPRS International Journal of Geo-Information. 2021; 10(4):244. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040244

Chicago/Turabian Style

Erlacher, Christoph; Anders, Karl-Heinrich; Jankowski, Piotr; Paulus, Gernot; Blaschke, Thomas. 2021. "A Framework for Cloud-Based Spatially-Explicit Uncertainty and Sensitivity Analysis in Spatial Multi-Criteria Models" ISPRS Int. J. Geo-Inf. 10, no. 4: 244. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040244

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