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Authors: Laura Annovazzi-Lodi ; Marica Franzini and Vittorio Casella

Affiliation: Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 5, Pavia and Italy

ISBN: 978-989-758-371-1

Keyword(s): Sentinel-2, Remote Sensing, Supervised Classification, SVM and Land Cover.

Abstract: This paper presents a case study of automatic classification of the remotely sensed Sentinel-2 imagery, from the EU Copernicus program. The work involved a study site, located in the area next to the city of Pavia, Italy, including fields cultivated by three farms. The aim of this work was to evaluate the so-called supervised classification applied to satellite images and performed with Esri's ArcGIS Pro software and Machine Learning techniques. The classification performed produces a land use map that is able to discriminate between different land cover types. By applying the Support Vector Machine (SVM) algorithm, it was found that, in our case, the pixel-based method offers a better overall performance than the object-based, unless a specific class is exclusively taken into consideration. This activity represents the first step of a project that fits into the context of Precision Agriculture, a recent and rapidly developing research area, whose aim is to optimize traditional cultiv ation methods. (More)

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Paper citation in several formats:
Annovazzi-Lodi, L.; Franzini, M. and Casella, V. (2019). Mapping Land Cover Types using Sentinel-2 Imagery: A Case Study.In Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-371-1, pages 242-249. DOI: 10.5220/0007738902420249

@conference{gistam19,
author={Laura Annovazzi{-}Lodi. and Marica Franzini. and Vittorio Casella.},
title={Mapping Land Cover Types using Sentinel-2 Imagery: A Case Study},
booktitle={Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2019},
pages={242-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007738902420249},
isbn={978-989-758-371-1},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Mapping Land Cover Types using Sentinel-2 Imagery: A Case Study
SN - 978-989-758-371-1
AU - Annovazzi-Lodi, L.
AU - Franzini, M.
AU - Casella, V.
PY - 2019
SP - 242
EP - 249
DO - 10.5220/0007738902420249

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