Spatio-temporal Mining with Scene Data Integration for Urban Transportation Navigation

2018 ◽  
Author(s):  
Rong Wen ◽  
Wenjing Yan
Keyword(s):  
Data Integration ◽  
Urban Transportation ◽  
Temporal Mining ◽  
Spatio Temporal

scholarly journals TOWARDS LIMITING SEMANTIC DATA LOSS IN 4D URBAN DATA SEMANTIC GRAPH GENERATION

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 37-44
Author(s):  
D. Vinasco-Alvarez ◽  
J. Samuel ◽  
S. Servigne ◽  
G. Gesquière
Keyword(s):  
Data Integration ◽  
Urban Landscape ◽  
Temporal Data ◽  
Data Loss ◽  
Large Area ◽  
Semantic Graph ◽  
Semantic Data ◽  
Spatio Temporal ◽  
City Models ◽  
Urban Data

Abstract. To enrich urban digital twins and better understand city evolution, the integration of heterogeneous, spatio-temporal data has become a large area of research in the enrichment of 3D and 4D (3D + Time) semantic city models. These models, which can represent the 3D geospatial data of a city and their evolving semantic relations, may require data-driven integration approaches to provide temporal and concurrent views of the urban landscape. However, data integration often requires the transformation or conversion of data into a single shared data format, which can be prone to semantic data loss. To combat this, this paper proposes a model-centric ontology-based data integration approach towards limiting semantic data loss in 4D semantic urban data transformations to semantic graph formats. By integrating the underlying conceptual models of urban data standards, a unified spatio-temporal data model can be created as a network of ontologies. Transformation tools can use this model to map datasets to interoperable semantic graph formats of 4D city models. This paper will firstly illustrate how this approach facilitates the integration of rich 3D geospatial, spatio-temporal urban data and semantic web standards with a focus on limiting semantic data loss. Secondly, this paper will demonstrate how semantic graphs based on these models can be implemented for spatial and temporal queries toward 4D semantic city model enrichment.

scholarly journals Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

2020 ◽  
Vol 9 (9) ◽  
pp. 503
Author(s):  
Ba-Huy Tran ◽  
Nathalie Aussenac-Gilles ◽  
Catherine Comparot ◽  
Cassia Trojahn
Keyword(s):  
Land Cover ◽  
Data Integration ◽  
Geographic Area ◽  
Semantic Integration ◽  
Earth Observation ◽  
Territorial Unit ◽  
Process Guidance ◽  
Spatio Temporal ◽  
Modular Ontology ◽  
Administrative Units

Semantic technologies are at the core of Earth Observation (EO) data integration, by providing an infrastructure based on RDF representation and ontologies. Because many EO data come in raster files, this paper addresses the integration of data calculated from rasters as a way of qualifying geographic units through their spatio-temporal features. We propose (i) a modular ontology that contributes to the semantic and homogeneous description of spatio-temporal data to qualify predefined areas; (ii) a Semantic Extraction, Transformation, and Load (ETL) process, allowing us to extract data from rasters and to link them to the corresponding spatio-temporal units and features; and (iii) a resulting dataset that is published as an RDF triplestore, exposed through a SPARQL endpoint, and exploited by a semantic interface. We illustrate the integration process with raster files providing the land cover of a specific French winery geographic area, its administrative units, and their land registers over different periods. The results have been evaluated with regards to three use-cases exploiting these EO data: integration of time series observations; EO process guidance; and data cross-comparison.

scholarly journals Spatio-Temporal Matching for Urban Transportation Applications

2018 ◽  
Vol 3 (4) ◽  
pp. 1-39 ◽  
Author(s):  
Daniel Ayala ◽  
Ouri Wolfson ◽  
Bhaskar Dasgupta ◽  
Jie Lin ◽  
Bo Xu
Keyword(s):  
Urban Transportation ◽  
Spatio Temporal ◽  
Transportation Applications

SharkFin: Spatio-temporal mining of software adoption and penetration

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Evangelos E. Papalexakis ◽  
Tudor Dumitras ◽  
Duen Horng Chau ◽  
B. Aditya Prakash ◽  
Christos Faloutsos
Keyword(s):  
Software Adoption ◽  
Temporal Mining ◽  
Spatio Temporal
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