TY - JOUR

T1 - Aggregation of LoD 1 building models as an optimization problem

AU - Guercke, Richard

AU - Götzelmann, T.

AU - Brenner, Claus

AU - Sester, Monika

N1 - Funding information: Part of the research for this article was funded by the German Federal Ministry of Education and Research (BMBF) in the context of the GDI-Grid project.

PY - 2011/3/1

Y1 - 2011/3/1

N2 - 3D city models offered by digital map providers typically consist of several thousands or even millions of individual buildings. Those buildings are usually generated in an automated fashion from high resolution cadastral and remote sensing data and can be very detailed. However, not in every application such a high degree of detail is desirable. One way to remove complexity is to aggregate individual buildings, simplify the ground plan and assign an appropriate average building height. This task is computationally complex because it includes the combinatorial optimization problem of determining which subset of the original set of buildings should best be aggregated to meet the demands of an application. In this article, we introduce approaches to express different aspects of the aggregation of LoD 1 building models in the form of Mixed Integer Programming (MIP) problems. The advantage of this approach is that for linear (and some quadratic) MIP problems, sophisticated software exists to find exact solutions (global optima) with reasonable effort. We also propose two different heuristic approaches based on the region growing strategy and evaluate their potential for optimization by comparing their performance to a MIP-based approach.

AB - 3D city models offered by digital map providers typically consist of several thousands or even millions of individual buildings. Those buildings are usually generated in an automated fashion from high resolution cadastral and remote sensing data and can be very detailed. However, not in every application such a high degree of detail is desirable. One way to remove complexity is to aggregate individual buildings, simplify the ground plan and assign an appropriate average building height. This task is computationally complex because it includes the combinatorial optimization problem of determining which subset of the original set of buildings should best be aggregated to meet the demands of an application. In this article, we introduce approaches to express different aspects of the aggregation of LoD 1 building models in the form of Mixed Integer Programming (MIP) problems. The advantage of this approach is that for linear (and some quadratic) MIP problems, sophisticated software exists to find exact solutions (global optima) with reasonable effort. We also propose two different heuristic approaches based on the region growing strategy and evaluate their potential for optimization by comparing their performance to a MIP-based approach.

KW - Aggregation

KW - City models

KW - Generalization

KW - Optimization

UR - http://www.scopus.com/inward/record.url?scp=79951683479&partnerID=8YFLogxK

U2 - 10.1016/j.isprsjprs.2010.10.006

DO - 10.1016/j.isprsjprs.2010.10.006

M3 - Article

AN - SCOPUS:79951683479

VL - 66

SP - 209

EP - 222

JO - ISPRS Journal of Photogrammetry and Remote Sensing

JF - ISPRS Journal of Photogrammetry and Remote Sensing

SN - 0924-2716

IS - 2

ER -