Smart contracts turn blockchains into distributed computing platforms. This article studies whether smart contracts as implemented by state-of-the-art blockchain technology may serve as component technology for a computing paradigm like serviceoriented computing (SOC) in the blockchain, in order to foster reuse and increase cost-effectiveness. A blockchain is a shared, distributed ledger, that is, a log of transactions that provides for persistency and verifiability of transactions [1]. A transaction is a cryptographically signed instruction constructed by a user of the blockchain [2], for example, the transfer of cryptocurrency from one account to another. Transactions are grouped into blocks, linked and secured using cryptographic hashes. A consensus protocol enables the nodes of the blockchain network to create trust in the state of the log and makes blockchains inherently resistant to tampering [3]. Thanks to these properties, blockchain technology is able to eliminate the need for a middleman from the management of transactions, such as a bank in the transfer of money. Next to logging transactions, blockchain platforms support the execution of pieces of code, socalled smart contracts [4, 5], able to perform computations inside the blockchain. For example, a smart contract may be used to automatically release a given amount of cryptocurrency upon the satisfaction of a condition agreed on by two partners. If we put multiple smart contracts (and partners) into communication, we turn the blockchain into a proper distributed computing platform [6]. This makes the technology appealing to application scenarios that ask for code execution that is reliable, verifiable and transactional. For example, Xu et al. [7] propose the use of smart contracts as software connectors for reliable, decentralized data sharing, while Weber et al. [8] propose the integration of multiple smart contracts for distributed business process execution. The first example aims to support data providers
Damage assessment is a fundamental step to support emergency response and recovery activities in a post-earthquake scenario. In recent years, UAVs and satellite optical imagery was applied to assess major structural damages before technicians could reach the areas affected by the earthquake. However, bad weather conditions may harm the quality of these optical assessments, thus limiting the practical applicability of these techniques. In this paper, the application of Synthetic Aperture Radar (SAR) imagery is investigated and a novel approach to SAR-based damage assessment is presented. Coherent Change Detection (CCD) algorithms on multiple interferometrically pre-processed SAR images of the area affected by the seismic event are exploited to automatically detect potential damages to buildings and other physical structures. As a case study, the 2016 Central Italy earthquake involving the cities of Amatrice and Accumoli was selected. The main contribution of the research outlined above is the integration of a complex process, requiring the coordination of a variety of methods and tools, into a unitary framework, which allows end-to-end application of the approach from SAR data pre-processing to result visualization in a Geographic Information System (GIS). A prototype of this pipeline was implemented, and the outcomes of this methodology were validated through an extended comparison with traditional damage assessment maps, created through photo-interpretation of high resolution aerial imagery. The results indicate that the proposed methodology is able to perform damage detection with a good level of accuracy, as most of the detected points of change are concentrated around highly damaged buildings.
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