Assessing the causes of structural failures is generally a hard task, which requires the knowledge of a great amount of information (i.e., structural characteristics, soil properties, soil-structure interaction, rainfall, and climatic data), hardly available during the monitoring phase. Regardless of the causes and the likely solutions of a given critical phenomenon, however, a preliminary monitoring stage is essential to prevent catastrophic events. For this purpose, a largescale continuous monitoring of time-evolving phenomena that might affect the stability of engineering facilities in urban areas would be very useful. Health monitoring of civil structures and infrastructures is typically conducted through in situ nondestructive techniques [8; 9; 10; 11]. However, when very long structures or blocks of many buildings are to be monitored, classical methods may become overly expensive and difficult to apply in practice. In such cases, global positioning system (GPS) techniques can be profitably used [12; 13], even combined with traditional local measurements [14] and remote sensing techniques [15]. Very effective remote sensing techniques are based on exploiting multitemporal interferometric synthetic aperture radar (MT-InSAR) displacement timeseries, which enable the monitoring of wide geographical areas over a weekly basis and extended spatial coverage. In fact, they are widely used for hazard assessment of very large or not accessible zones (due, for instance, to military conflicts or extreme climate conditions) [16; 17]. The fields where MT-InSAR techniques can be applied are several, ranging from archaeological detection [18], glacier dynamic assessment [19], and environmental impact monitoring [20] to several civil engineering applications [21; 22; 23; 24; 25; 26; 27]. The potential of MT-InSAR techniques can be amplified by their combination with advanced methods of data analysis. The use of computational algorithms to analyze remote sensing data has been expanding for over half a century [28], since crop identification based on the Apollo satellite spectral data was produced in the 1970s [29]. More recently, neural-network-based algorithms were used to analyze geological data for different purposes, including detection of hydrocarbon resources, pollution, and oil spills [30], classification of complex natural ecosystems [31; 32], creating hazard survey maps in earthquake-prone areas [33], and assessment of some properties of soils [34; 35]. In the civil engineering field, neural-network-based methods are currently used, for instance, to predict blast-induced ground vibrations [36], to control the behavior of buildings subjected to ground motion [37], to improve the features extraction under ultrasonic tests [38], or to optimize the structural design [39]. Neural-network-based algorithms have also been applied to analyze InSAR data in urban or suburban areas [25; 40; 41; 42]. The present paper aims to contribute to move toward an automatized method for monitoring ground settlements in urban areas, with the purpose of highlighting hazardous events at their early stage. The proposed method is based on the combination of MT-InSAR techniques, which allow continuously collecting timeseries of surface deformations at a high-density grid of geographical points, and an artificial neural-network-based algorithm, which enables processing big data in real time, leading to an automatized alert system. On the basis of similarities between timeseries, the method aims to find precursors of likely ongoing critical surface deformations at their early stage. Empirical and analytical studies, in fact, showed that precursors may typically help to forecast some phenomena such as ground high stress in tunnels [43] or landslides [44]. The method here presented is based on the preliminary choice of a warning criterion that leads to finding the precursors of similar phenomena.