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...