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