@article {Khoshelham2010123,
	title = {Performance evaluation of automated approaches to building detection in multi-source aerial data},
	journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
	volume = {65},
	number = {1},
	year = {2010},
	note = {cited By 25},
	pages = {123-133},
	abstract = {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 different spatial resolutions. Five methods are tested in two study areas using features extracted at both pixel level and object level, but with the strong prerequisite of using the same training set for all methods. The evaluation of the methods is based on error measures obtained by superimposing the results on a manually generated reference map of each area. The results in both study areas show a better performance of the Dempster-Shafer and the AdaBoost methods, although these two methods also yield a number of unclassified pixels. The method of thresholding a normalized DSM performs well in terms of the detection rate and reliability in the less vegetated Mannheim study area, but also yields a high rate of false positive errors. The Bayesian methods perform better in the Memmingen study area where buildings have more or less the same heights. {\textcopyright} 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).},
	doi = {10.1016/j.isprsjprs.2009.09.005},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-72949110177\&partnerID=40\&md5=016e5e523686951f9fa75cccf7957c8d},
	author = {Khoshelham, K. and C. Nardinocchi and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@conference {Zingaretti2007273,
	title = {Automatic extraction of LIDAR data classification rules},
	booktitle = {Proceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007},
	year = {2007},
	note = {cited By 4},
	pages = {273-278},
	abstract = {LIDAR (Light Detection And Ranging) data are a primary data source for digital terrain model (DTM) generation and 3D city models. This paper presents an AdaBoost algorithm for the identification of rules for the classification of raw LIDAR data mainly as buildings, ground and vegetation. First raw data are filtered, interpolated over a grid and segmented. Then geometric and topological relationships among regions resulting from segmentation constitute the input to the tree-structured classification algorithm. Results obtained on data sets gathered over the town of Pavia (Italy) are compared with those obtained by a rule-based approach previously presented by the authors for the classification of the regions. {\textcopyright} 2007 IEEE.},
	doi = {10.1109/ICIAP.2007.4362791},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-48149101919\&partnerID=40\&md5=6578b3da6a7d08ffa58b7e9b97f98d95},
	author = {Primo Zingaretti and Emanuele Frontoni and G. Forlani and C. Nardinocchi}
}
@article {Forlani2006357,
	title = {Complete classification of raw LIDAR data and 3D reconstruction of buildings},
	journal = {Pattern Analysis and Applications},
	volume = {8},
	number = {4},
	year = {2006},
	note = {cited By 68},
	pages = {357-374},
	abstract = {LIDAR (LIght Detection And Ranging) data are a primary data source for digital terrain model (DTM) generation and 3D city models. This paper presents a three-stage framework for a robust automatic classification of raw LIDAR data as buildings, ground and vegetation, followed by a reconstruction of 3D models of the buildings. In the first stage the raw data are filtered and interpolated over a grid. In the second stage, first a double raw data segmentation is performed and then geometric and topological relationships among regions resulting from segmentation are computed and stored in a knowledge base. In the third stage, a rule-based scheme is applied for the classification of the regions. Finally, polyhedral building models are reconstructed by analysing the topology of building outlines, building roof slopes and eaves lines. Results obtained on data sets with different ground point density, gathered over the town of Pavia (Italy) with Toposys and Optech airborne laser scanning systems, are shown to illustrate the effectiveness of the proposed approach.},
	doi = {10.1007/s10044-005-0018-2},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-31944451659\&partnerID=40\&md5=55cd07722f1f7f4286ba71b656517b77},
	author = {G. Forlani and C. Nardinocchi and M. Scaioni and Primo Zingaretti}
}
