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.

With urban areas projected to accommodate 68 % of the global population by 2050, the imperative for inclusive, safe, and sustainable cities becomes paramount. In the timeline of urban centers, landslides represent one of the most destructive phenomena, involving several resources allocation with private and public investments, sometimes claiming human lives. By synergically connecting environmental, planning, and configurational spheres, this study seeks to support the proactive management of landslide risk. The proposed three-step methodology allowed to quantify the environmental features involved in landslide occurrence, evaluate planning framework vulnerabilities, and suggest alternative configurations for urban areas that experienced landslides. The methodology has been applied to the case study involving a tragic landslide in Casamicciola Terme (Italy) in November 2022. First, the stream network and the drainage basin corresponding to confluence point of the landslide into the sea have been calculated (environmental elaborations). Subsequently, these elaborations have been overlapped with the runoff mitigation and the sediment deposition layers, extracted through the INVEST software. Secondly, the reconnaissance of the local and superordinate planning levels has been realized, to deepen planning tools cogency on the study area, contextually deepening the constraints that characterize it. From the overlapping of these two steps, free landslide risk areas have been located. Finally, based on the available territorial surface (St_a) and the territorial cover ratio (Rc_t), two territorial configuration scenarios have been proposed, envisaging the relocation of the buildings involved into the landslide. Results show that landslide originated by three out of five gullies. Some portions of the urban areas of Casamicciola Terme are still under high and very high hydrogeological risk. Contextually, it emerges poor attention from the local planners to the superordinate planning framework. Historic settlement has an Rct of 33.64 %, while areas in which relocate the built up show an Rc_t of 32,45 % for scenario 1 and 27,9 % for scenario 2. The methodology resulted useful to address planning vulnerabilities, supporting the realization of alternative configurational scenarios. We expect our research to contribute to the evolving field of disaster risk reduction, by providing a systematic approach to manage landslide risk.

Sustainable development goals (SDGs) have a huge impact on global policies. Contextually, the concept of ecosystem services (ESs) naturally relies on the importance of integrating human activities into a framework in which ecosystems play a fundamental role in supporting upcoming societies. Introducing ESs in the process of SDG evaluation can be useful to facilitate their achievement through national and local planning policies. Nevertheless, this aspect is still poorly addressed. In the present study, an in-depth analysis has been conducted, to associate regulation and maintenance ecosystem services (ReMESs) with the SDGs set by the 2030 Agenda. Based on the available scientific literature, ReMESs have been linked to the SDGs and SDG targets. Specific attention has been paid to deepening linkages with the SDG targets that explicitly address the introduction of ecosystem and biodiversity values into national and local planning. Finally, SDG evaluation has been further investigated, linking the SDG targets to the statistical measures proposed by the Inter-agency and Expert Group on SDGs (UN-IAEG-SDGs). This last step focused on the Italian context, according to the indicators provided by the National Institute of Statistics. The results show that ReMESs are linked to 8 out of 17 SDGs (47%). Connections decrease when considering the SDG targets (20%) and the national statistical measures (18%). It also emerges that some targets, although being connected to ReMESs, do not have the right indicators to be quantified. Ecosystem services prove to be a valid element through which modern sustainable development goals can be accomplished. This study, which highlights several gaps to be filled, wants to offer valuable help in evaluating SDGs and their implementation through ReMESs.