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Land Consumption Mapping with Convolutional Neural Network: Case Study in Italy

Author

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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)

  • 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

In recent years, deep learning (DL) algorithms have been widely integrated for remote sensing image classification, but fewer studies have applied it for land consumption (LC). LC is the main factor in land transformation dynamics and it is the first cause of natural habitat loss; therefore, monitoring this phenomenon is extremely important for establishing effective policies and sustainable planning. This paper aims to test a DL algorithm on high-resolution aerial images to verify its applicability to land consumption monitoring. For this purpose, we applied a convolutional neural networks (CNNs) architecture called ResNet50 on a reference dataset of six high-spatial-resolution aerial images for the automatic production of thematic maps with the aim of improving accuracy and reducing costs and time compared with traditional techniques. The comparison with the National Land Consumption Map (LCM) of ISPRA suggests that although deep learning techniques are not widely exploited to map consumed land and to monitor land consumption, it might be a valuable support for monitoring and reporting data on highly dynamic peri-urban areas, especially in view of the rapid evolution of these techniques.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:11:p:1919-:d:955898
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    References listed on IDEAS

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    1. Costanza, Robert & d'Arge, Ralph & de Groot, Rudolf & Farber, Stephen & Grasso, Monica & Hannon, Bruce & Limburg, Karin & Naeem, Shahid & O'Neill, Robert V. & Paruelo, Jose, 1998. "The value of the world's ecosystem services and natural capital," Ecological Economics, Elsevier, vol. 25(1), pages 3-15, April.
    2. 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.
    3. 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.
    4. Rattan Lal, 2015. "Restoring Soil Quality to Mitigate Soil Degradation," Sustainability, MDPI, vol. 7(5), pages 1-21, May.
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    Cited by:

    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 & 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.

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