@article {sturari2017integrating,
	title = {Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping},
	journal = {European Journal of Remote Sensing},
	volume = {50},
	number = {1},
	year = {2017},
	pages = {1{\textendash}17},
	publisher = {Taylor \& Francis},
	author = {Mirco Sturari and Emanuele Frontoni and Roberto Pierdicca and Adriano Mancini and Eva Savina Malinverni and Anna Nora Tassetti and Primo Zingaretti}
}
@article {pierdicca20163d,
	title = {3D visualization tools to explore ancient architectures in South America},
	journal = {Virtual Archaeology Review},
	volume = {7},
	number = {15},
	year = {2016},
	pages = {44{\textendash}53},
	author = {Roberto Pierdicca and Eva Savina Malinverni and Emanuele Frontoni and F. Colosi and R. Orazi}
}
@conference {pierdicca2016cyberarchaeology,
	title = {Cyberarchaeology: improved way findings for archaeological parks through mobile augmented reality},
	booktitle = {International Conference on Augmented Reality, Virtual Reality and Computer Graphics},
	year = {2016},
	pages = {172{\textendash}185},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	author = {Roberto Pierdicca and Emanuele Frontoni and Primo Zingaretti and Eva Savina Malinverni and Galli, Andrea and Marcheggiani, Ernesto and Costa, Carlos Smaniotto}
}
@article {pierdicca2016smart,
	title = {Smart maintenance of riverbanks using a standard data layer and Augmented Reality},
	journal = {Computers \& Geosciences},
	volume = {95},
	year = {2016},
	pages = {67{\textendash}74},
	publisher = {Pergamon},
	author = {Roberto Pierdicca and Emanuele Frontoni and Primo Zingaretti and Adriano Mancini and Eva Savina Malinverni and Anna Nora Tassetti and Marcheggiani, Ernesto and Galli, Andrea}
}
@article {pierdicca2016virtual,
	title = {Virtual reconstruction of archaeological heritage using a combination of photogrammetric techniques: Huaca Arco Iris, Chan Chan, Peru},
	journal = {Digital Applications in Archaeology and Cultural Heritage},
	volume = {3},
	number = {3},
	year = {2016},
	pages = {80{\textendash}90},
	publisher = {Elsevier},
	author = {Roberto Pierdicca and Emanuele Frontoni and Eva Savina Malinverni and F. Colosi and R. Orazi}
}
@article {Pierdicca201525,
	title = {Making visible the invisible. Augmented reality visualization for 3D reconstructions of archaeological sites},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9254},
	year = {2015},
	note = {cited By 0},
	pages = {25-37},
	doi = {10.1007/978-3-319-22888-4_3},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84944688602\&partnerID=40\&md5=5988b5e6786fdbcd931f64ac7f8483f5},
	author = {Roberto Pierdicca and Emanuele Frontoni and Primo Zingaretti and Eva Savina Malinverni and F. Colosi and R. Orazi}
}
@article {Frontoni2014853,
	title = {SIT-REM: An interoperable and interactive web geographic information system for fauna, flora and plant landscape data management},
	journal = {ISPRS International Journal of Geo-Information},
	volume = {3},
	number = {2},
	year = {2014},
	note = {cited By 0},
	pages = {853-867},
	publisher = {MDPI AG},
	abstract = {The main goal of the SIT-REM project is the design and the development of an interoperable web-GIS environment for the information retrieval and data editing/updating of the geobotanical and wildlife map of Marche Region. The vegetation, plant landscape and faunistic analysis allow the realization of a regional information system for wildlife-geobotanical data. A main characteristic of the SIT-REM is its flexibility and interoperability, in particular, its ability to be easily updated with the insertion of new types of environmental, faunal or socio-economic data and to generate analyses at any geographical (from regional to local) or quantitative level of detail. Different query levels obtain the latter: spatial queries, hybrid query builder and WMSs usable by means of a GIS. SIT-REM has been available online for more than a year and its use over this period has produced extensive data about users{\textquoteright} experiences.. {\textcopyright} 2014 by the authors; licensee MDPI, Basel, Switzerland.},
	issn = {22209964},
	doi = {10.3390/ijgi3020853},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948991558\&partnerID=40\&md5=e554cb57d885eec6bb72afa03e616523},
	author = {Emanuele Frontoni and Adriano Mancini and Primo Zingaretti and Eva Savina Malinverni and Pesaresi, S.c and Biondi, E.c and Pandolfi, M.d and Marseglia, M.e and Mirco Sturari and Zabaglia, C.g}
}
@conference {Mancini20131589,
	title = {Road pavement crack automatic detection by MMS images},
	booktitle = {2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings},
	year = {2013},
	note = {cited By 0},
	pages = {1589-1596},
	abstract = {The research topic was to test different feature extraction methods to localize road pavement cracks useful to construct a spatial database for the pavement distress monitoring. Several images were acquired by means of a line scan camera that assembled in a Mobile Mapping System (MMS) allows tracking directly the position of the images by a GPS-INS system. Following an automatic digital image processing was performed by means of several algorithms based on different approaches (edge detection and fuzzy set theory). The detected cracks were described with some parameters in relation to some shape characteristics (dimension, typology, direction), which are necessary to recognize the gravity of the road pavement conditions. The edge detection techniques tested in this research allowed identifying fatigue cracking or alligator cracking and also thin linear cracks in images with strong radiometric jumps by applying filters, gradient functions and morphological operators. The snake approach was one of them, in particular the type called Gradient Vector Flow (GVF). Another approach was based on the fuzzy theory. The advantage of this method is that the pixels, necessary to identify the cracks in road pavement, are darker than their surroundings in an image. The last stage was the pavement distress spatial database collection. The Mobile Mapping System (MMS) has allowed localizing the raster data and consequently the vector features of the detected cracks, associating into the table their attributes too. The proposed approaches allow to automatically localize and classify the kind of road pavement crack. {\textcopyright} 2013 IEEE.},
	doi = {10.1109/MED.2013.6608934},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84885199881\&partnerID=40\&md5=bf9cd82e8356555a25eb7cc1fd02b547},
	author = {Adriano Mancini and Eva Savina Malinverni and Emanuele Frontoni and Primo Zingaretti}
}
@article {Malinverni20111025,
	title = {Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery},
	journal = {International Journal of Geographical Information Science},
	volume = {25},
	number = {6},
	year = {2011},
	note = {cited By 13},
	pages = {1025-1043},
	abstract = {Traditionally, remote sensing has employed pixel-based classification techniques to deal with land use/land cover (LULC) studies. Generally, pixel-based approaches have been proven to work well with low spatial resolution imagery (e.g. Landsat or System Pour L{\textquoteright}Observation de la Terre sensors). Now, however, commercially available high spatial resolution images (e.g. aerial Leica ADS40 and Vexcel UltraCam sensors, and satellite IKONOS, Quickbird, GeoEye and WorldView sensors) can be problematic for pixel-based analysis due to their tendency to oversample the scene. This is driving research towards object-based approaches. This article proposes a hybrid classification method with the aim of incorporating the advantages of supervised pixel-based classification into object-based approaches. The method has been developed for medium- scale (1:10,000) LULC mapping using ADS40 imagery with 1 m ground sampling distance. First, spatial information is incorporated into a pixel-based classification (AdaBoost classifier) by means of additional texture features (Haralick, Gabor, Law features), which can be selected {\textquoteright}ad hoc{\textquoteright} according to optimal training samples ({\textquoteright}Relief-F{\textquoteright} pproach,Mahalanobis distances). Then a rule-based approach sorts segmented regions into thematic CORINE Land Cover classes in terms of membership class percentages (a modified Winner-Takes-All approach) and shape parameters. Finally, ancillary data (roads, rivers, etc.) are exploited to increase classification accuracy. The experimental results show that the proposed hybrid approach allows the extraction of more LULC classes than conventional pixel-based methods, while improving classification accuracy considerably. A second contribution of this article is the assessment of classification reliability by implementing a stability map, in addition to confusion matrices. {\textcopyright} 2011 Taylor \& Francis.},
	doi = {10.1080/13658816.2011.566569},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960685342\&partnerID=40\&md5=18767c8a88bf2abff53ef96ec138f0aa},
	author = {Eva Savina Malinverni and Anna Nora Tassetti and Adriano Mancini and Primo Zingaretti and Emanuele Frontoni and A. Bernardini}
}
@conference {Malinverni20102836,
	title = {LCLU Information System for object-oriented nomenclature},
	booktitle = {International Geoscience and Remote Sensing Symposium (IGARSS)},
	year = {2010},
	note = {cited By 0},
	pages = {2836-2839},
	abstract = {A Land Cover/Land Use (LCLU) Information System is proposed as a new dynamic and flexible approach to describe landscape objects. It is able to give a deeper and more realistic thematic description by storing membership land cover attributes for each polygon automatically extracted and classified by the T-MAP software. The proposed approach can overcome the traditional "hard" classification by taking directly into account "fuzzy" cover components and making the classification approach more bounded with the polygon characteristics and their changes. The LCLU Information System can be easily integrated with different databases, making it suitable for different nomenclatures and further analysis, regarding environmental indexes, class updating and classification stability assessment. {\textcopyright} 2010 IEEE.},
	doi = {10.1109/IGARSS.2010.5651398},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78650884639\&partnerID=40\&md5=a38544ae56c5f11edee2b11e7730439c},
	author = {Eva Savina Malinverni and Anna Nora Tassetti and Primo Zingaretti}
}
@article {Bernardini201043,
	title = {Pixel, object and hybrid classification comparisons},
	journal = {Journal of Spatial Science},
	volume = {55},
	number = {1},
	year = {2010},
	note = {cited By 3},
	pages = {43-54},
	abstract = {The choice of the best classification approach for thematic map generation relies on many factors, such as image resolution and minimum mapping unit. The generalized GIS-ready products derived from the results of pixel-based approaches and the availability of higherresolution imagery have directed research towards object-based classification approaches. In this paper we present the superior performance of a hybrid methodology that combines the results of automatic segmentation with the land cover information derived from a pixel classification by means of the Winner Takes All (WTA) algorithm. Land use and land cover results obtained through this hybrid classification approach are compared with those of a One Against All (OAA) object-oriented classification approach. {\textcopyright} 2010 Surveying and Spatial Sciences Institute and Mapping Sciences Institute, Australia.},
	doi = {10.1080/14498596.2010.487641},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80052137947\&partnerID=40\&md5=74b7a4fe82987b5213c27b7315b0f768},
	author = {A. Bernardini and Emanuele Frontoni and Eva Savina Malinverni and Adriano Mancini and Anna Nora Tassetti and Primo Zingaretti}
}
@article {Zingaretti2009500,
	title = {A hybrid approach to land cover classification from multi spectral images},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {5716 LNCS},
	year = {2009},
	note = {cited By 5},
	pages = {500-508},
	abstract = {This work is part of a wider project whose general objective is to develop a methodology for the automatic classification, based on CORINE land-cover (CLC) classes, of high resolution multispectral IKONOS images. The specific objective of this paper is to describe a new methodology for producing really exploitable results from automatic classification algorithms. Input data are basically constituted by multispectral images, integrated with textural and contextual measures. The output is constituted by an image with each pixel assigned to one out of 15 classes at the second level of the CLC legend or let unclassified (somehow a better solution than a classification error), plus a stability map that helps users to separate the regions classified with high accuracy from those whose classification result should be verified before being used. {\textcopyright} 2009 Springer Berlin Heidelberg.},
	doi = {10.1007/978-3-642-04146-4_54},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-76249123810\&partnerID=40\&md5=fcaeb5e46615d9bbdf96fcbf4ffac41a},
	author = {Primo Zingaretti and Emanuele Frontoni and Eva Savina Malinverni and Adriano Mancini}
}
@conference {Frontoni2009,
	title = {Stability maps for really exploitable automatic classification results},
	booktitle = {2009 17th International Conference on Geoinformatics, Geoinformatics 2009},
	year = {2009},
	note = {cited By 1},
	abstract = {The paper describes a new methodology for producing really exploitable results from automatic classification algorithms. The output of these algorithms is usually constituted by an image with each region assigned to one out of n classes. If the end user, on the basis of results obtained from a control set provided with a ground truth, simply knows that classification over the whole dataset can be considered correct at, for example, 85\% (s)he cannot know where correct and erroneously classified regions are really located in the whole dataset. Obviously, the result obtained can be exploited to effectively compute global indexes over the dataset, but it cannot be used as a thematic map. Thus, in addition to the assignment of a class to each region we propose an approach that provides a stability map, a binary image that separates regions (S) classified with high accuracy from those (U) whose classification result should be verified before being used. Two further benefits derive from the construction of the stability map: the control set can be used to set up a good threshold for binarizing the stability map (that is, a threshold by which all regions S are effectively correctly classified); unreliable regions U can help the end user to identify principal causes of (types of regions leading to) misclassification and corresponding (fuzzy, neural, rule based, etc.) approaches to overcome them.},
	doi = {10.1109/GEOINFORMATICS.2009.5293443},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-74349085598\&partnerID=40\&md5=9cde6053a2e8199444209988fccd5e2c},
	author = {Emanuele Frontoni and A. Bernardini and Eva Savina Malinverni and Adriano Mancini and Primo Zingaretti}
}
