BUILDING INVENTORY INFORMATION EXTRACTION FROM REMOTE SENSING DATA AND STATISTICAL MODELS
In this paper, algorithms for extracting building attribute information from remotely sensed data are presented. In particular, a methodology for rapidly extracting spatial and structural information from a single highresolution satellite image, using rational polynomial coefficients (RPCs) as a camera replacement model is introduced. Geometric information defining satellites sensor orientation is used in conjunction with the RPC projection model to generate an accurate digital elevation model (DEM). Additionally, a methodology for inferring engineering attributes of the built-environment, i.e. structural type and occupancy type of buildings, from 3-D building models is formulated. A dataset collected for Southern California, USA, is used to train multinomial logistic regression models and establish inference rules in order to predict the regional engineering parameters of the buildings. Classification error and prediction power of these models are then presented in the paper and an example of the marginal probability distribution computed for a sample building is shown.
Building Inventory Remote Sensing Rational Function Model Height Extraction 3-D Building Modeling Statistical Modeling Inference Rules Multinomial Logistic Regression.
Pooya Sarabandi Anne S. Kiremidjian
Risk Management Solutions, Inc., Newark, CA, USA;Department of Civil and Environmental Engineering, Department of Civil and Environmental Engineering, Stanford University, CA, USA
国际会议
14th World Conference on Earthquake Engineering(第十四届国际地震工程会议)
北京
英文
2008-10-12（万方平台首次上网日期，不代表论文的发表时间）