Automatic classification of building types in 3D city models

Authors:
André Henn;Christoph Römer;Gerhard Gröger;Lutz Plümer
Affiliations:
Institute for Geodesy and Geoinformation, University of Bonn, Bonn, Germany 53115;Institute for Geodesy and Geoinformation, University of Bonn, Bonn, Germany 53115;Institute for Geodesy and Geoinformation, University of Bonn, Bonn, Germany 53115;Institute for Geodesy and Geoinformation, University of Bonn, Bonn, Germany 53115
Venue:
Geoinformatica
Year:
2012

Citing 18
Cited 0

Parameter estimation and hypothesis testing in linear models

Parameter estimation and hypothesis testing in linear models
LOF: identifying density-based local outliers

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Convex Optimization

Convex Optimization
Introduction to Machine Learning (Adaptive Computation and Machine Learning)

Introduction to Machine Learning (Adaptive Computation and Machine Learning)
YALE: rapid prototyping for complex data mining tasks

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Aerial LiDAR Data Classification Using Support Vector Machines (SVM)

3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
An improved SVM method P-SVM for classification of remotely sensed data

International Journal of Remote Sensing
SemaPlorer-Interactive semantic exploration of data and media based on a federated cloud infrastructure

Web Semantics: Science, Services and Agents on the World Wide Web
Support vector machines for urban growth modeling

Geoinformatica
Getting context on the go: mobile urban exploration with ambient tag clouds

Proceedings of the 6th Workshop on Geographic Information Retrieval
Semi-Supervised Learning

Semi-Supervised Learning
Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance

Computers and Electronics in Agriculture
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
An introduction to kernel-based learning algorithms

IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article presents a classifier based on Support Vector Machines (SVMs), an advanced machine learning method for semantic enrichment of coarse 3D city models by deriving the building type. The information on the building type (detached building, terraced building, etc.) is essential for a variety of relevant applications of 3D city models like spatial marketing, real estate management and marketing, and for visualization. The derivation of the building type from coarse data (mainly 2D footprints, building heights and functions) seems impossible at first sight. However it succeeds by incorporating the spatial context of a building. Since the input data can be derived easily and at very low cost, this method is widely applicable. Nevertheless, as with all supervised learning algorithms, obtaining labelled training data is very time-consuming. Herewith, we provide a method which uses outlier detection and clustering methods to support users in efficiently and rapidly obtaining adequate training data.