@article {luchetti2017whistland,
	title = {Whistland: An Augmented Reality Crowd-Mapping System for Civil Protection and Emergency Management},
	journal = {ISPRS International Journal of Geo-Information},
	volume = {6},
	number = {2},
	year = {2017},
	pages = {41},
	publisher = {Multidisciplinary Digital Publishing Institute},
	author = {Luchetti, Gioele and Adriano Mancini and Mirco Sturari and Emanuele Frontoni and Primo Zingaretti}
}
@conference {Ascani2010415,
	title = {Wireless sensor network for exhausted oil collection management},
	booktitle = {Proceedings of 2010 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2010},
	year = {2010},
	note = {cited By 1},
	pages = {415-420},
	abstract = {Sensor networks have a large diffusion in several areas, using different kind of sensors, different line for data transmission and different ways for their collection and management. In this paper we present a particular typology of sensor network, created for optimally manage the collection of exhausted oil stored in particular bins located in a wide area (about 20.000 km2). A sensor is located on each bin measuring the distance from the lid of the collected oil; a transmission module communicate, once a day, the oil and the battery level to a web server via GPRS. In this way the control center (on a web server) has a complete overview of the situation in a wide zone, in way to optimize the run of collectors only in points that require an operation. {\textcopyright} 2010 IEEE.},
	doi = {10.1109/MESA.2010.5551992},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77957355959\&partnerID=40\&md5=623731d74fba78b45e4b811bf63ec2b9},
	author = {Ascani, A. and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@conference {Mancini2009,
	title = {A winner takes all mechanism for automatic object extraction from multi-source data},
	booktitle = {2009 17th International Conference on Geoinformatics, Geoinformatics 2009},
	year = {2009},
	note = {cited By 3},
	abstract = {Automatic object extraction from multi-source aerial data is a desirable property for many activities, such as detecting 3D city model changes or updating road databases. This paper applies the Winner Takes All (WTA) mechanism, derived from other research fields, to combine the benefits of pixel and region classification. We fuse LiDAR data and multi-spectral high-resolution images to generate the set of features used by boosted classifiers to detect buildings, trees, bare land and grass. The main benefit of region based classification is that it removes the sensibility to noise of pixel based classifiers. The WTA approach is useful especially when pixel based approaches leave many pixels unclassified; typical cases are borders of building roofs or thin canopies, where LiDAR data are often noisy. Results in an urban environment using high-resolution LiDAR and multi-spectral data are presented comparing the performance of pixel, region and WTA approaches.},
	doi = {10.1109/GEOINFORMATICS.2009.5293425},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-74349100965\&partnerID=40\&md5=d8aa4248c0d2835fde5d5a907e76ad8d},
	author = {Adriano Mancini and Emanuele Frontoni and Primo Zingaretti}
}
