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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Bibliografiske detaljer

Principais autores:	Christoph Erlacher, Karl-Heinrich Anders, Piotr Jankowski, Gernot Paulus, Thomas Blaschke
Format:	Artigo
Sprog:	Inglês
Udgivet:	MDPI AG 2021-04-01
Serier:	ISPRS International Journal of Geo-Information
Fag:	Spatially-Explicit Uncertainty and Sensitivity Analysis parallel and distributed computing SEUSA as a Service spatial cloud computing microservices Spatial Multi-Criteria Decision Analysis
Online adgang:	https://www.mdpi.com/2220-9964/10/4/244
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