IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i4p879-d1122557.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/4/879/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/4/879/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wenyi Hu & Tian Chen & Chunjie Lan & Shan Liu & Lirong Yin, 2024. "SkipResNet: Crop and Weed Recognition Based on the Improved ResNet," Land, MDPI, vol. 13(10), pages 1-21, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Angela Cimini & Paolo De Fioravante & Nicola Riitano & Pasquale Dichicco & Annagrazia Calò & Giuseppe Scarascia Mugnozza & Marco Marchetti & Michele Munafò, 2023. "Land Consumption Dynamics and Urban–Rural Continuum Mapping in Italy for SDG 11.3.1 Indicator Assessment," Land, MDPI, vol. 12(1), pages 1-24, January.
    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. Alessia D’Agata & Giovanni Quaranta & Rosanna Salvia & Margherita Carlucci & Luca Salvati, 2023. "Mixed Land Use as an Intrinsic Feature of Sprawl: A Short-Term Analysis of Settlement Growth and Population Distribution Using European Urban Atlas," Land, MDPI, vol. 12(5), pages 1-21, April.
    4. Tommaso Orusa & Annalisa Viani & Enrico Borgogno-Mondino, 2024. "Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)," Land, MDPI, vol. 13(1), pages 1-21, January.
    5. Diego Lopez-Bernal & David Balderas & Pedro Ponce & Arturo Molina, 2021. "Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems," Future Internet, MDPI, vol. 13(8), pages 1-14, July.
    6. Andrea Molocchi, 2021. "Circular Economy and Environmental Sustainability: A Policy Coherence Analysis of Current Italian Subsidies," Sustainability, MDPI, vol. 13(15), pages 1-38, July.
    7. Marini, Michele & Caro, Dario & Thomsen, Marianne, 2023. "Investigating local policy instruments for different types of urban agriculture in four European cities: A case study analysis on the use and effectiveness of the applied policy instruments," Land Use Policy, Elsevier, vol. 131(C).
    8. Daniela Smiraglia & Alice Cavalli & Chiara Giuliani & Francesca Assennato, 2023. "The Increasing Coastal Urbanization in the Mediterranean Environment: The State of the Art in Italy," Land, MDPI, vol. 12(5), pages 1-17, May.
    9. Rita Nicolau & Beatriz Condessa, 2022. "Monitoring Net Land Take: Is Mainland Portugal on Track to Meet the 2050 Target?," Land, MDPI, vol. 11(7), pages 1-31, July.
    10. 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.
    11. Giacomo Bernello & Elena Mondino & Lucia Bortolini, 2022. "People’s Perception of Nature-Based Solutions for Flood Mitigation: The Case of Veneto Region (Italy)," Sustainability, MDPI, vol. 14(8), pages 1-13, April.
    12. Junwen Lu & Jinhui Wang & Xiaojun Wei & Keshou Wu & Guanfeng Liu, 2022. "Deep Anomaly Detection Based on Variational Deviation Network," Future Internet, MDPI, vol. 14(3), pages 1-17, March.
    13. Anna Biasin & Mauro Masiero & Giulia Amato & Davide Pettenella, 2023. "Nature-Based Solutions Modeling and Cost-Benefit Analysis to Face Climate Change Risks in an Urban Area: The Case of Turin (Italy)," Land, MDPI, vol. 12(2), pages 1-32, January.
    14. Francesca Assennato & Daniela Smiraglia & Alice Cavalli & Luca Congedo & Chiara Giuliani & Nicola Riitano & Andrea Strollo & Michele Munafò, 2022. "The Impact of Urbanization on Land: A Biophysical-Based Assessment of Ecosystem Services Loss Supported by Remote Sensed Indicators," Land, MDPI, vol. 11(2), pages 1-20, February.
    15. Elena Di Pirro & Peter Roebeling & Lorenzo Sallustio & Marco Marchetti & Bruno Lasserre, 2023. "Cost-Effectiveness of Nature-Based Solutions under Different Implementation Scenarios: A National Perspective for Italian Urban Areas," Land, MDPI, vol. 12(3), pages 1-19, March.
    16. Nicola Ricca & Ilaria Guagliardi, 2023. "Evidences of Soil Consumption Dynamics over Space and Time by Data Analysis in a Southern Italy Urban Sprawling Area," Land, MDPI, vol. 12(5), pages 1-22, May.
    17. Sara Mastrorosa & Mattia Crespi & Luca Congedo & Michele Munafò, 2023. "Land Consumption Classification Using Sentinel 1 Data: A Systematic Review," Land, MDPI, vol. 12(4), pages 1-25, April.
    18. Giuseppe Cillis & Dina Statuto & Pietro Picuno, 2021. "Historical GIS as a Tool for Monitoring, Preserving and Planning Forest Landscape: A Case Study in a Mediterranean Region," Land, MDPI, vol. 10(8), pages 1-20, August.
    19. Andrea Molocchi, 2020. "From production to consumption: An inter-sectoral analysis of air emissions external costs in Italy," ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, FrancoAngeli Editore, vol. 2020(2), pages 155-180.
    20. Federico Falasca & Alessandro Marucci, 2024. "Supporting Sustainable Development Goals through Regulation and Maintenance Ecosystem Services," Sustainability, MDPI, vol. 16(16), pages 1-16, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:879-:d:1122557. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.