## 1 Introduction  
New planning uses innovative and technologically advanced tools through the continuous flow of data and simulations of possible scenarios. In this application context, approaches based on GIS technologies and methodologies play a fundamental role, both in vulnerability assessment and in supporting decision-making processes as a whole and in the analysis of risk, impact and consequences [1, 2]. In the last years, the scientific community has focused increasingly on the application implications of the use of GIS-based technologies, and the need for effective spatial standards and interfaces, spatial analysis tools and integrated hardware/software platforms (Spatial Data Infrastructure, SDI) have played a key role in the development of these activities [3, 4, 5]. GIS-based models have been used to quantify both the demand and provision of Urban Ecosystem Services (UES) [6, 7, 8]. These models allow, through spatial data such as land cover and land use (LULC), for the quantification of UESs by associating and comparing them with vegetation types and other landscape features [9]. Moreover, spatial dynamics can reveal heterogeneity and trends in the distribution of UESs over urban landscapes, which can be of importance for urban sustainability planning [10]. Other studies have quantified spatial variation in UES values using a hedonic price model and have analyzed spatial relations among biodiversity features to assess habitat supply [11]. The GIS-based approach to spatial planning allows one to benefit from ICT technologies. In particular, the new research activities in this area concern the application of indicators, engineering techniques aimed at identifying appropriate indices able to return information on the reaction of urban tissues with respect to environmental disturbances that determine impact/risk scenarios [12; 13]. The infrastructures that today aim to collect and manage the immense amount of information produced by different systems express their real power in integration and interoperability [5; 14]. New applications and innovative geospatial services are the basis of this research, with reference to spatial analysis and fast monitoring tools for urban resilience, the redevelopment of sensitive areas to combat hydraulic and hydrogeological risk; for the mitigation of climate change; for the improvement of/increase in ecosystem services; and for the evaluation of the carbon balance. All these issues are at the basis of the correct application of legislation on the Strategic Environmental Assessment (SEA), which should normally regulate the effects of urban planning [15; 16]. The same procedure is based on the formulation of alternative assessment scenarios and on the concepts of mitigation and compensation [17]. Therefore, through a technological and cultural reform, it is possible to make SEA an effective and fundamental tool for urban planning. In this particularly active context, the experimentation of technologies in territorial sciences assumes a fundamental importance for the development of the field of territorial planning. Indeed, today, Machine learning (ML) has increasingly been used in studies concerning territorial sciences and urban dynamics [18; 19; 20; 21]. Neural networks were introduced in the 1950s, but only recently they have reached advanced processing power and data storage capabilities to the point that deep learning (DL) algorithms can be used to create new applications, including satellite imagery analysis [22]. Therefore, experimentation with ML and DL can only increase the interpretive capabilities of the urban mosaic and can generate sustainable territorial configurations [23; 24]. This work exploits unsupervised machine learning techniques to discriminate urbanized from non-urbanized areas, a key step in implementing an urban and peri-urban mosaic pattern evaluation system.