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Extraction of Road Geometric Parameters from High Resolution Remote Sensing Images Validated by GNSS/INS Geodetic Techniques

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Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10407))

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Abstract

Roadway geometric data, user behavior and crash data provide the main input for developing existing highway safety evaluations. In particular, roadway geometric data can be useful in providing a quantitative guidance for alignment consistency and for having an initial indication of critical point presence along a road.

Furthermore the article 13 of the Italian Road Code requires an implementation of Road Cadastre for which it is necessary to know the planimetric and altimetric alignments.

This paper presents a methodology to obtain geometric road information from high resolution images using automatic techniques for the extraction of road geometric parameters.

The paper starts from the Road Cadastre characteristics and definitions and the analysis of the existing studies regarding remote sensing methodologies. Then two strategies, tested on road sections in Italy, are presented. The first one is based on the concept of spectral signature, while the second one is based on the Fractal Dimension of the image treated only as a numerical matrix.

The results were validated by GNSS/INS geodetic techniques, using also real time kinematic data.

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Author information

Authors and Affiliations

  1. GeoSNav Lab, University of Trieste, Via Valerio 6/2, 34127, Trieste, Italy

    Raffaela Cefalo & Tatiana Sluga

  2. Centre of Excellence for the Research in TeleGeomatics and Spatial Information, University of Trieste, Trieste, Italy

    Giulia Grandi

  3. Department of Engineering and Architecture, University of Trieste, Trieste, Italy

    Roberto Roberti

Authors
  1. Raffaela Cefalo
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  2. Giulia Grandi
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  3. Roberto Roberti
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  4. Tatiana Sluga
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Corresponding author

Correspondence to Raffaela Cefalo .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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Cefalo, R., Grandi, G., Roberti, R., Sluga, T. (2017). Extraction of Road Geometric Parameters from High Resolution Remote Sensing Images Validated by GNSS/INS Geodetic Techniques. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10407. Springer, Cham. https://doi.org/10.1007/978-3-319-62401-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-62401-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62400-6

  • Online ISBN: 978-3-319-62401-3

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