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

Author

Listed:
  • 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
    geoLAB-Laboratory of Forest Geomatics, Department of Agricultural, Food and Forestry Systems, University of Florence, Via San Bonaventura, 13, 50145 Firenze, 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)

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.

Suggested Citation

  • Giulia Cecili & Paolo De Fioravante & Pasquale Dichicco & Luca Congedo & Marco Marchetti & Michele Munafò, 2023. "Land Cover Mapping with Convolutional Neural Networks Using Sentinel-2 Images: Case Study of Rome," Land, MDPI, vol. 12(4), pages 1-20, April.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:879-:d:1122557
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    References listed on IDEAS

    as
    1. Niraj Thapa & Zhipeng Liu & Dukka B. KC & Balakrishna Gokaraju & Kaushik Roy, 2020. "Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems," Future Internet, MDPI, vol. 12(10), pages 1-16, September.
    2. Giulia Cecili & Paolo De Fioravante & Luca Congedo & Marco Marchetti & Michele Munafò, 2022. "Land Consumption Mapping with Convolutional Neural Network: Case Study in Italy," Land, MDPI, vol. 11(11), pages 1-19, October.
    3. Paolo De Fioravante & Tania Luti & Alice Cavalli & Chiara Giuliani & Pasquale Dichicco & Marco Marchetti & Gherardo Chirici & Luca Congedo & Michele Munafò, 2021. "Multispectral Sentinel-2 and SAR Sentinel-1 Integration for Automatic Land Cover Classification," Land, MDPI, vol. 10(6), pages 1-35, June.
    4. Paolo De Fioravante & Andrea Strollo & Alice Cavalli & Angela Cimini & Daniela Smiraglia & Francesca Assennato & Michele Munafò, 2023. "Ecosystem Mapping and Accounting in Italy Based on Copernicus and National Data through Integration of EAGLE and SEEA-EA Frameworks," Land, MDPI, vol. 12(2), pages 1-22, January.
    5. Andrea Strollo & Daniela Smiraglia & Roberta Bruno & Francesca Assennato & Luca Congedo & Paolo De Fioravante & Chiara Giuliani & Ines Marinosci & Nicola Riitano & Michele Munafò, 2020. "Land consumption in Italy," Journal of Maps, Taylor & Francis Journals, vol. 16(1), pages 113-123, January.
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