# A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution
## 1 Introduction  
Natural hazards impose a threat to millions of people all over the world. Yet, if hazards can be so different, the exposed assets are mostly the same; people, buildings, infrastructure, and the natural environment. Despite the fact that exposure is often treated as a static entity, it should be regarded as a dynamic process in which exposed assets undergo continuous changes [1]. This is particularly relevant for exposed building stocks. Considering the disaster risk management cycle, exposed assets are subject to changes throughout the different phases of risk management, reaching from pre-disaster mitigation to the immediate response after an event and the long-term recovery of affected assets. Such dynamics typically appear on different spatial and temporal scales. It can, for example, be linked to continuous changes such as urbanization [2] or to changes of the residential building quality through retrofitting or other risk-reducing measures [3] as part of the mitigation phase. Damage and collapse of exposed buildings are typical abrupt changes that appear in consequence of a disastrous event [4]. Construction of temporary shelters or reconstruction of buildings are other examples of rapid and continuous exposure dynamics that are typically linked to the short- and long-term post-disaster phases [5]. With respect to the collection of exposure and/or damage data at a per-building scale, typical methods involve in-situ screening surveys [6]. Often aggregated census data are used for regional to national scale risk studies [7]. Lately, an increasing demand for remote sensing studies at these scales can be observed, with a shift going from simple (single timestamp) mapping to (multi-temporal) monitoring of exposed building stocks and damages from satellite images [8]. Due to the increasingly large variety of possible exposure and/or damage data sources and their inherent spatio-temporal nature, database models in support of disaster risk management need to consider aspects of data harmonization and integration and be able to store and efficiently manage data coming from different sources, at varying scales, with different quality, and changing over time and space. The crucial point of data storage and management is, however, rarely mentioned in the context of disaster risk management, despite the fact that it is becoming increasingly important with the increase in data volume and complexity. [PERSON] et al. (2016) [9] provide an overview of the challenges of implementing an exposure model for different geo-hazards at a global scale within a dynamic and scalable framework. Specific points that need to be jointly addressed by a database model in the context of disaster risk management include spatio-temporal data modelling, the use of standard taxonomies, multi-representation, information life-cycle management, and distributed version control [10], each of which can be considered separate research domains. A large body of literature exists on the different topics (see Section 2); however, as yet, no publicly available data management technology exists, to best of the authors' knowledge that matches these requirements in a single solution with particular focus on disaster risk management. The objective of this work is, therefore, to provide a conceptual and technical solution to store and manage heterogeneous building exposure data in a dynamic and spatially referenced manner throughout their life cycle. The proposed solution is implemented on the basis of free and open source software components. It combines best practices from different research domains into a single solution that specifically addresses the data management needs throughout different phases of the disaster risk management cycle. The paper is structured as follows. Section 2 provides an overview of existing work in the different research domains that are touched by this study. Choices made and advances achieved in order to support disaster risk management efforts are addressed. Section 3 introduces the database model.