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Record Nr. |
UNINA9910566470903321 |
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Autore |
Lehtola Ville |
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Titolo |
Advances in Mobile Mapping Technologies |
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Pubbl/distr/stampa |
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Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
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Descrizione fisica |
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1 online resource (268 p.) |
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Soggetti |
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History of engineering & technology |
Technology: general issues |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Mobile mapping is applied widely in society, for example, in asset management, fleet management, construction planning, road safety, and maintenance optimization. Yet, further advances in these technologies are called for. Advances can be radical, such as changes to the prevailing paradigms in mobile mapping, or incremental, such as the state-of-the-art mobile mapping methods. With current multi-sensor systems in mobile mapping, laser-scanned data are often registered in point clouds with the aid of global navigation satellite system (GNSS) positioning or simultaneous localization and mapping (SLAM) techniques and then labeled and colored with the aid of machine learning methods and digital camera data. These multi-sensor platforms are beginning to undergo further advancements via the addition of multi-spectral and other sensors and via the development of machine learning techniques used in processing this multi-modal data. Embedded systems and minimalistic system designs are also attracting attention, from both academic and commercial perspectives.This book contains the accepted publications of the Special Issue 'Advances in Mobile Mapping Technologies' of the Remote Sensing journal. It consists of works introducing a new mobile mapping dataset ('Paris CARLA 3D'), system calibration studies, SLAM topics, and multiple deep learning works for asset detection. We, the Guest Editors, Ville Lehtola from University of Twente, Netherlands, Andreas Nüchter |
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from University of Würzburg, Germany, and François Goulette from Mines Paris- PSL University, France, wish to thank all the authors who contributed to this collection. |
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2. |
Record Nr. |
UNINA9910682556903321 |
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Autore |
Esuli Andrea |
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Titolo |
Learning to Quantify / / by Andrea Esuli, Alessandro Fabris, Alejandro Moreo, Fabrizio Sebastiani |
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Pubbl/distr/stampa |
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2023 |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (XVI, 137 p. 1 illus.) |
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Collana |
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The Information Retrieval Series, , 2730-6836 ; ; 47 |
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Classificazione |
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COM004000COM021030COM030000 |
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Disciplina |
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Soggetti |
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Information storage and retrieval systems |
Data mining |
Machine learning |
Information Storage and Retrieval |
Data Mining and Knowledge Discovery |
Machine Learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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- 1. The Case for Quantification. - 2. Applications of Quantification. - 3. Evaluation of Quantification Algorithms. - 4. Methods for Learning to Quantify. - 5. Advanced Topics. - 6. The Quantification Landscape. - 7. The Road Ahead. |
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Sommario/riassunto |
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This open access book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify is a task of its own related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate (“biased”) |
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class proportion estimates. The book introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research. The book is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate (“macro”) data rather than on individual (“micro”) data. |
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