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**Harvard**

JOHANSSON, E. och Lundberg, J. (2016) *Distributed Viewshed Analysis An Evaluation of Distribution Frameworks for Geospatial Information Systems*. Göteborg : Chalmers University of Technology

** BibTeX **

@mastersthesis{

JOHANSSON2016,

author={JOHANSSON, EMIL and Lundberg, Jacob},

title={Distributed Viewshed Analysis An Evaluation of Distribution Frameworks for Geospatial Information Systems},

abstract={Viewshed analysis is the process of computing what areas of a terrain are visible from
a certain observation point. In this thesis we evaluated the performance of these
computations on cloud clusters using the distribution framework Apache Spark.
We implemented three commonly used viewshed algorithms; R3 which is slow but
highly accurate as well as R2 and van Kreveld which are faster but less accurate.
Two versions of each algorithm were implemented, one to run on a single multi-core
machine and one to run on a server cluster using Spark. We compared the accuracy
and running time of the different algorithms in order to determine when to use the
different algorithms. Our results show that viewshed analysis does not perform well
when implemented using Spark if real-time results are required. In fact the faster
algorithms performed consistently worse on the cluster, even for very large input
data. For the highly accurate, but slow, R3 algorithm we were able to achieve a
1.6x speedup using the distribution framework.},

publisher={Institutionen för data- och informationsteknik (Chalmers), Chalmers tekniska högskola},

place={Göteborg},

year={2016},

keywords={viewshed, GIS, distributed, cluster, line-of-sight, Apache Spark},

note={51},

}

** RefWorks **

RT Generic

SR Electronic

ID 238036

A1 JOHANSSON, EMIL

A1 Lundberg, Jacob

T1 Distributed Viewshed Analysis An Evaluation of Distribution Frameworks for Geospatial Information Systems

YR 2016

AB Viewshed analysis is the process of computing what areas of a terrain are visible from
a certain observation point. In this thesis we evaluated the performance of these
computations on cloud clusters using the distribution framework Apache Spark.
We implemented three commonly used viewshed algorithms; R3 which is slow but
highly accurate as well as R2 and van Kreveld which are faster but less accurate.
Two versions of each algorithm were implemented, one to run on a single multi-core
machine and one to run on a server cluster using Spark. We compared the accuracy
and running time of the different algorithms in order to determine when to use the
different algorithms. Our results show that viewshed analysis does not perform well
when implemented using Spark if real-time results are required. In fact the faster
algorithms performed consistently worse on the cluster, even for very large input
data. For the highly accurate, but slow, R3 algorithm we were able to achieve a
1.6x speedup using the distribution framework.

PB Institutionen för data- och informationsteknik (Chalmers), Chalmers tekniska högskola,PB Institutionen för data- och informationsteknik (Chalmers), Chalmers tekniska högskola,

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/238036/238036.pdf

OL 30