@conference {Mancini2010448,
	title = {Road change detection from multi-spectral aerial data},
	booktitle = {Proceedings - International Conference on Pattern Recognition},
	year = {2010},
	note = {cited By 2},
	pages = {448-451},
	abstract = {The paper presents a novel approach to automate the Change Detection (CD) problem for the specific task of road extraction. Manual approaches to CD fail in terms of the time for releasing updated maps; in the contrary, automatic approaches, based on machine learning and image processing techniques, allow to update large areas in a short time with an accuracy and precision comparable to those obtained by human operators. This work is focused on the road-graph update starting from aerial, multi-spectral data. Georeferenced, ground data, acquired by a GPS and an inertial sensor, are integrated with aerial data to speed up the change detector. After roads extraction by means of a binary AdaBoost classifier, the old road-graph is updated exploiting a particle filter. In particular this filter results very useful to link (track) parts of roads not extracted by the classifier due to the presence of occlusions (e.g., shadows, trees). {\textcopyright} 2010 IEEE.},
	doi = {10.1109/ICPR.2010.118},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78149486517\&partnerID=40\&md5=6fcdcfdf8b76c0c7c79a8cd6f6eaef25},
	author = {Adriano Mancini and Emanuele Frontoni and Primo Zingaretti}
}
