Slow-moving landslides yearly induce huge economic losses worldwide in terms of damage to facilities and interruption of human activities. Within the landslide risk management framework, the consequence analysis is a key step entailing procedures mainly based on identifying and quantifying the exposed elements, defining an intensity criterion and assessing the expected losses. This paper presents a two-scale (medium and large) procedure for vulnerability assessment of buildings located in areas affected by slow-moving landslides. Their intensity derives from Differential Interferometric Synthetic Aperture Radar (DInSAR) satellite data analysis, which in the last decade proved to be capable of providing cost-effective long-term displacement archives. The analyses carried out on two study areas of southern Italy (one per each of the addressed scales) lead to the generation, as an absolute novelty, of both empirical fragility and vulnerability curves for buildings in slow-moving landslide-affected areas. These curves, once further validated, can be valuably used as tools for consequence forecasting purposes and, more in general, for planning the most suitable slow-moving landslide risk mitigation strategies

Testing innovative procedures and techniques to update landslide inventory maps is a timely topic widely discussed in the scientific literature. In this regard remote sensing techniques – such as the Synthetic Aperture Radar Differential Interferometry (DInSAR) – can provide a valuable contribution to studies concerning slow-moving landslides in different geological contexts all over the world. In this paper, DInSAR data are firstly analysed via an innovative approach aimed at enhancing both the exploitation and the interpretation of remote sensing information; then, they are complemented with the results of an accurate analysis of survey-recorded damage to facilities due to slow-moving landslides. In particular, after being separately analysed to provide independent landslide movement indicators, the two datasets are combined in a DInSAR-Damage matrix which can be used to update the state of activity of slow-moving landslides. Moreover, together with the information provided by geomorphological maps, the two datasets are proven to be useful in detecting unmapped phenomena. The potentialities of the adopted procedure are tested in an area of southern Italy where slow-moving landslides are widespread and accurately mapped by using geomorphological criteria

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