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Land Cover Mapping with Convolutional Neural Networks Using Sentinel-2 Images: Case Study of Rome

Giulia Cecili, Paolo De Fioravante, Pasquale Dichicco, Luca Congedo (luca.congedo@isprambiente.it), Marco Marchetti and Michele Munafò
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Giulia Cecili: Department of Biosciences and Territory, University of Molise, C/da Fonte Lappone, 86090 Pesche, Italy
Paolo De Fioravante: Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy
Pasquale Dichicco: Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy
Luca Congedo: Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy
Marco Marchetti: Department of Biosciences and Territory, University of Molise, C/da Fonte Lappone, 86090 Pesche, Italy
Michele Munafò: Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy

Land, 2023, vol. 12, issue 4, 1-20

Abstract: Land cover monitoring is crucial to understand land transformations at a global, regional and local level, and the development of innovative methodologies is necessary in order to define appropriate policies and land management practices. Deep learning techniques have recently been demonstrated as a useful method for land cover mapping through the classification of remote sensing imagery. This research aims to test and compare the predictive models created using the convolutional neural networks (CNNs) VGG16, DenseNet121 and ResNet50 on multitemporal and single-date Sentinel-2 satellite data. The most promising model was the VGG16 both with single-date and multi-temporal images, which reach an overall accuracy of 71% and which was used to produce an automatically generated EAGLE-compliant land cover map of Rome for 2019. The methodology is part of the land mapping activities of ISPRA and exploits its main products as input and support data. In this sense, it is a first attempt to develop a high-update-frequency land cover classification tool for dynamic areas to be integrated in the framework of the ISPRA monitoring activities for the Italian territory.

Keywords: deep learning; convolutional neural networks; land cover; remote sensing; Copernicus; Sentinel-2 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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