The current research aims at investigating the potential of Image Segmentation (IS) as a data source for mapping, with a bottom-up approach, the spatial quality of slow routes, localized in the territories “in-between” the main cities. The paper analyses two different case studies in Lombardy and Molise regions, where a different territorial configuration and data are available. The IS method, that computes area percentages in the street-level imagery by using Pixellab/TensorFlow digital environment, has been applied for detecting three different environments that are intersected by the selected routes and that are also detectable by using GIS tools: open spaces, built environment and rows of trees. These have been considered as relevant since they affect the users’ perception of the places in a different way. The research points out how the IS method can be complementary to the GIS-based detection method to collect more detailed geo-information about the places, but also a very powerful tool to catch geo-information by the street-level imagery, in the territories where no thematic geospatial data are available.

Image Segmentation Procedure for Mapping Spatial Quality of Slow Routes

Rolando A.;D'Uva D.;Scandiffio A.
2022-01-01

Abstract

The current research aims at investigating the potential of Image Segmentation (IS) as a data source for mapping, with a bottom-up approach, the spatial quality of slow routes, localized in the territories “in-between” the main cities. The paper analyses two different case studies in Lombardy and Molise regions, where a different territorial configuration and data are available. The IS method, that computes area percentages in the street-level imagery by using Pixellab/TensorFlow digital environment, has been applied for detecting three different environments that are intersected by the selected routes and that are also detectable by using GIS tools: open spaces, built environment and rows of trees. These have been considered as relevant since they affect the users’ perception of the places in a different way. The research points out how the IS method can be complementary to the GIS-based detection method to collect more detailed geo-information about the places, but also a very powerful tool to catch geo-information by the street-level imagery, in the territories where no thematic geospatial data are available.
2022
Representation Challenges. New Frontiers of AR and AI Research for Cultural Heritage and Innovative Design
9788835141945
image segmentation
mapping
spatial quality
slow routes
fragile landscapes
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1220921
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