A learning Dempster-Shafer model for automated building detection

This paper presents a learning Dempster-Shafer model for the detection of buildings in aerial image and range data. The process of evidence assignment in the Dempster-Shafer method is implemented through membership functions in an adaptive network-based fuzzy inference system, where a back propagation learning rule is employed to tune the...

Performance evaluation of automated approaches to building detection in multi-source aerial data

Automated approaches to building detection in multi-source aerial data are important in many applications, including map updating, city modeling, urban growth analysis and monitoring of informal settlements. This paper presents a comparative analysis of different methods for automated building detection in aerial images and laser data at...

A comparison of Bayesian and evidence-based fusion methods for automated building detection in aerial data

Automated approaches to building detection are of great importance in a number of different applications including map updating and monitoring of informal settlements. With the availability of multi-source aerial data in recent years, data fusion approaches to automated building detection have become more popular. In this paper, two data fusion...

Extending generalized hough transform to detect 3D objects in laser range data

Automated detection and 3D modelling of objects in laser range data is of great importance in many applications. Existing approaches to object detection in range data are limited to either 2.5D data (e.g. range images) or simple objects with a parametric form (e.g. spheres). This paper describes a new approach to the detection of 3D objects with...

Automated 3D modeling of buildings in suburban areas based on integration of image and height data