M
Michela Bertolotto
Researcher at University College Dublin
Publications - 219
Citations - 3593
Michela Bertolotto is an academic researcher from University College Dublin. The author has contributed to research in topics: Spatial analysis & Geospatial analysis. The author has an hindex of 26, co-authored 206 publications receiving 3250 citations. Previous affiliations of Michela Bertolotto include University of North Carolina at Charlotte & National Center for Geographic Information and Analysis.
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Octree-based region growing for point cloud segmentation
TL;DR: Empirical studies show the proposed approach to be at least an order of magnitude faster when compared to a conventional region growing method and able to incorporate semantic-based feature criteria, while achieving precision, recall, and fitness scores of at least 75% and as much as 95%.
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Virtual reality for collaborative e-learning
TL;DR: The research in this area and the resulting development of CLEV-R, a Collaborative Learning Environment with Virtual Reality, a web-based system that uses Virtual Reality and multimedia and provides communication tools to support collaboration among students are presented.
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Progressive Transmission of Vector Map Data over the World Wide Web
TL;DR: A solution to the progressive transmission of vector map data that allows users to apply analytical GIS methods to partially transmitted data sets and follows a client-server model with multiple map representations at the server side, and a thin client that compiles transmitted increments into a topologically consistent format.
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Exploratory spatio-temporal data mining and visualization
TL;DR: Within this system, new techniques have been developed to efficiently support the data-mining process, address the spatial and temporal dimensions of the data set, and visualize and interpret results.
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Geographic knowledge extraction and semantic similarity in OpenStreetMap
TL;DR: Devising a mechanism for computing the semantic similarity of the OSM geographic classes can help alleviate this semantic gap, and empirical evidence supports the usage of co-citation algorithms—SimRank showing the highest plausibility—to compute concept similarity in a crowdsourced semantic network.