Discretization of the Urban and Non-Urban Shape: Unsupervised Machine Learning Techniques for Territorial Planning
One of the goals of the scientific community is to equip the discipline of spatial planning with efficient tools to handle huge amounts of data. In this sense, unsupervised machine learning techniques (UMLT) can help overcome this obstacle to further the study of spatial dynamics. New machine-learni...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/20/10439 |
| _version_ | 1797475577235505152 |
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| author | Lorena Fiorini Federico Falasca Alessandro Marucci Lucia Saganeiti |
| author_facet | Lorena Fiorini Federico Falasca Alessandro Marucci Lucia Saganeiti |
| author_sort | Lorena Fiorini |
| collection | DOAJ |
| description | One of the goals of the scientific community is to equip the discipline of spatial planning with efficient tools to handle huge amounts of data. In this sense, unsupervised machine learning techniques (UMLT) can help overcome this obstacle to further the study of spatial dynamics. New machine-learning-based technologies make it possible to simulate the development of urban spatial dynamics and how they may interact with ecosystem services provided by nature. Modeling information derived from various land cover datasets, satellite earth observation and open resources such as Volunteered Geographic Information (VGI) represent a key structural step for geospatial support for land use planning. Sustainability is certainly one of the paradigms on which planning and the study of past, present and future spatial dynamics must be based. Topics such as Urban Ecosystem Services have assumed such importance that they have become a prerogative on which to guide the administration in the difficult process of transformation, taking place not only in the urban context, but also in the peri-urban one. In this paper, we present an approach aimed at analyzing the performance of clustering methods to define a standardized system for spatial planning analysis and the study of associated dynamics. The methodology built ad hoc in this research was tested in the spatial context of the city of L’Aquila (Abruzzo, Italy) to identify the urbanized and non-urbanized area with a standardized and automatic method. |
| first_indexed | 2024-03-09T20:47:07Z |
| format | Article |
| id | doaj.art-d9e28edfda844f31b6a7437a1989df28 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:47:07Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d9e28edfda844f31b6a7437a1989df282023-11-23T22:44:39ZengMDPI AGApplied Sciences2076-34172022-10-0112201043910.3390/app122010439Discretization of the Urban and Non-Urban Shape: Unsupervised Machine Learning Techniques for Territorial PlanningLorena Fiorini0Federico Falasca1Alessandro Marucci2Lucia Saganeiti3Department of Civil, Construction-Architecture and Environmental Engineering, University of L’Aquila, Via G. Gronchi, 18, 67100 L’Aquila, ItalyDepartment of Civil, Construction-Architecture and Environmental Engineering, University of L’Aquila, Via G. Gronchi, 18, 67100 L’Aquila, ItalyDepartment of Civil, Construction-Architecture and Environmental Engineering, University of L’Aquila, Via G. Gronchi, 18, 67100 L’Aquila, ItalyDepartment of Civil, Construction-Architecture and Environmental Engineering, University of L’Aquila, Via G. Gronchi, 18, 67100 L’Aquila, ItalyOne of the goals of the scientific community is to equip the discipline of spatial planning with efficient tools to handle huge amounts of data. In this sense, unsupervised machine learning techniques (UMLT) can help overcome this obstacle to further the study of spatial dynamics. New machine-learning-based technologies make it possible to simulate the development of urban spatial dynamics and how they may interact with ecosystem services provided by nature. Modeling information derived from various land cover datasets, satellite earth observation and open resources such as Volunteered Geographic Information (VGI) represent a key structural step for geospatial support for land use planning. Sustainability is certainly one of the paradigms on which planning and the study of past, present and future spatial dynamics must be based. Topics such as Urban Ecosystem Services have assumed such importance that they have become a prerogative on which to guide the administration in the difficult process of transformation, taking place not only in the urban context, but also in the peri-urban one. In this paper, we present an approach aimed at analyzing the performance of clustering methods to define a standardized system for spatial planning analysis and the study of associated dynamics. The methodology built ad hoc in this research was tested in the spatial context of the city of L’Aquila (Abruzzo, Italy) to identify the urbanized and non-urbanized area with a standardized and automatic method.https://www.mdpi.com/2076-3417/12/20/10439unsupervised machine learningUMLTurban planningclustering modelsmachine learningmosaic urban pattern |
| spellingShingle | Lorena Fiorini Federico Falasca Alessandro Marucci Lucia Saganeiti Discretization of the Urban and Non-Urban Shape: Unsupervised Machine Learning Techniques for Territorial Planning Applied Sciences unsupervised machine learning UMLT urban planning clustering models machine learning mosaic urban pattern |
| title | Discretization of the Urban and Non-Urban Shape: Unsupervised Machine Learning Techniques for Territorial Planning |
| title_full | Discretization of the Urban and Non-Urban Shape: Unsupervised Machine Learning Techniques for Territorial Planning |
| title_fullStr | Discretization of the Urban and Non-Urban Shape: Unsupervised Machine Learning Techniques for Territorial Planning |
| title_full_unstemmed | Discretization of the Urban and Non-Urban Shape: Unsupervised Machine Learning Techniques for Territorial Planning |
| title_short | Discretization of the Urban and Non-Urban Shape: Unsupervised Machine Learning Techniques for Territorial Planning |
| title_sort | discretization of the urban and non urban shape unsupervised machine learning techniques for territorial planning |
| topic | unsupervised machine learning UMLT urban planning clustering models machine learning mosaic urban pattern |
| url | https://www.mdpi.com/2076-3417/12/20/10439 |
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