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

Giulia Cecili (), Paolo De Fioravante, Luca Congedo, 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
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, 2022, vol. 11, issue 11, 1-19

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.

Keywords: deep learning; convolutional neural networks; land consumption mapping; land cover; remote sensing; semantic segmentation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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