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Article

Accessible Routes Integrating Data from Multiple Sources

1
Database Lab., CITIC, Universidade da Coruña, Elviña, 15071 A Coruña, Spain
2
Department of Telematic Engineering, Universidad Carlos III de Madrid, 28911 Leganés, Spain
3
Applied Geotechnologies Research Group, Campus Universitario de Vigo, Universidade de Vigo, CINTECX, As Lagoas, Marcosende, 36310 Vigo, Spain
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 7; https://doi.org/10.3390/ijgi10010007
Received: 9 November 2020 / Revised: 7 December 2020 / Accepted: 21 December 2020 / Published: 26 December 2020
(This article belongs to the Special Issue Large Scale Geospatial Data Management, Processing and Mining)
Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain). View Full-Text
Keywords: spatial data mining; geospatial NLP; geospatial data fusion; large scale geospatial processing; pedestrian navigation; physical accessibility spatial data mining; geospatial NLP; geospatial data fusion; large scale geospatial processing; pedestrian navigation; physical accessibility
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MDPI and ACS Style

Luaces, M.R.; Fisteus, J.A.; Sánchez-Fernández, L.; Munoz-Organero, M.; Balado, J.; Díaz-Vilariño, L.; Lorenzo, H. Accessible Routes Integrating Data from Multiple Sources. ISPRS Int. J. Geo-Inf. 2021, 10, 7. https://doi.org/10.3390/ijgi10010007

AMA Style

Luaces MR, Fisteus JA, Sánchez-Fernández L, Munoz-Organero M, Balado J, Díaz-Vilariño L, Lorenzo H. Accessible Routes Integrating Data from Multiple Sources. ISPRS International Journal of Geo-Information. 2021; 10(1):7. https://doi.org/10.3390/ijgi10010007

Chicago/Turabian Style

Luaces, Miguel R., Jesús A. Fisteus, Luis Sánchez-Fernández, Mario Munoz-Organero, Jesús Balado, Lucía Díaz-Vilariño, and Henrique Lorenzo. 2021. "Accessible Routes Integrating Data from Multiple Sources" ISPRS International Journal of Geo-Information 10, no. 1: 7. https://doi.org/10.3390/ijgi10010007

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