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

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

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...

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Autors principals: Christoph Erlacher, Karl-Heinrich Anders, Piotr Jankowski, Gernot Paulus, Thomas Blaschke
Format: Article
Idioma:English
Publicat: MDPI AG 2021-04-01
Col·lecció:ISPRS International Journal of Geo-Information
Matèries:
Spatially-Explicit Uncertainty and Sensitivity Analysis
parallel and distributed computing
SEUSA as a Service
spatial cloud computing
microservices
Spatial Multi-Criteria Decision Analysis
Accés en línia:https://www.mdpi.com/2220-9964/10/4/244

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https://www.mdpi.com/2220-9964/10/4/244

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