## 1 Introduction
This paper describes a novel approach for the prompt collection of geo-localized visual information of the damage state of the buildings hosting insurance policies, which would considerably streamline the claim management process. The proposed approach is based on the use of a lightweight mobile mapping system to collect geo-referenced omnidirectional images of the affected urban environment, which would then be analyzed off-line. Splitting the _in situ_ data collection from the actual analysis of the collected images, would have a number of advantages:  
* a partial shift in the burden of inspection to an offline procedure, which could be conveniently scaled up or down by involving a pool of skilled operators operating from remote;
* less risk for the _in situ_ operators to be affected by aftershocks or structural collapses, as it would generally involve less interference with recovery operations;
* a simple technical solution for visual data collection would not require expert operators and could be quickly deployed and operated on the ground;
* the same approach could be used pre-event to collect reference images, thereby capturing further information useful for the post-event claim settlement process. Within this scheme, the critical role of geo-information and geographical information systems has to be acknowledged. In this framework, a methodology for the prioritization of data collection is introduced, based on the combination of two or more geo-information layers encoding different indicators of sampling importance (referred to as focus maps [11] and described below). The survey and collection of geo-spatial data, _i.e._, geo-localized information, can be considered equivalent to the statistical characterization of a population whose elements are of interest for the purpose of the survey. Often the dimension of the population, or the burden associated with the data collection itself, are incompatible with a full enumeration and characterization of the elements. If we consider, for instance, the task of inspecting the residential areas in Istanbul in order to map the damage distribution (or to characterize their vulnerability in a pre-event phase), it is clear that a full-scale survey of the 800,000+ buildings would require significant resources. In these cases, a statistical characterization based on the sampling of the underlying population should be applied. Moreover, if we consider the task of inspecting buildings for the assessment of the damage state in a post-event application, the limited time frame (important to guarantee a swift settling of claims) represents an additional constraint. The inspection of a much smaller number of elements of the population, followed by an extrapolation (inference) of the characteristics of interest would make the task feasible within a reasonable time frame [12]. The practical necessity of characterizing only a small number of elements, and perhaps over subsequent collection stages, calls upon the development of optimization approaches that would answer the following question: \"_What elements should be accessed first in order to achieve an optimal trade-off between the uncertainty of the overall estimates and the time and cost of the survey itself?_\" We refer to such an approach as optimization and prioritization of geo-spatial data collection. In the considered context, prioritization refers to selective sampling operations which realize a statistical characterization of a population of interest (in our case the population is defined by the buildings that hold one or more earthquake insurance policies) and optimize the use of available resources in order to comply as closely as possible to existing constraints. From the geo-statistic point of view, this is closely related to applying an adaptive spatial sampling [13,14] with unequal probability [15]. In the literature, a few references can be found on the use of _a priori_ information to improve sampling, mostly connected to the characterization of environmental resources.