Flooding represents the most-occurring and deadliest threats worldwide among natural disasters. Consequently, new technologies are constantly developed to improve response capacities in crisis management. The remaining challenge for practitioner organizations is not only to identify the best solution to their individual demands, but also to test and evaluate its benefit in a realistic environment before the disaster strikes. To bridge the gap between theoretic potential and actual integration into practice, the EU-funded project DRIVER+ has designed a methodical and technical environment to assess innovation in a realistic but non-operational setup through trials. The German Aerospace Center (DLR) interdisciplinary merged mature technical developments into the “Airborne and terrestrial situational awareness” system and applied it in a DRIVER+ Trial to promote a sustainable and demand-oriented R&D. Experienced practitioners assessed the added value of its modules “KeepOperational” and “ZKI” in the context of large-scale flooding in urban areas. The solution aimed at providing contextual route planning in police operations and extending situational awareness based on information derived through aerial image processing. The user feedback and systematically collected data through the DRIVER + Test-bed approved that DLR’s system could improve transport planning and situational awareness across organizations. However, the results show a special need to consider, for example, cross-domain data-fusion techniques to provide essential 3D geo-information to effectively support specific response tasks during flooding.

中文翻译：

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尝试危机管理的创新技术——“空中和地面态势感知”作为洪水应对的支持工具

洪水是世界范围内自然灾害中发生最多、最致命的威胁。因此，不断开发新技术以提高危机管理的反应能力。从业组织面临的剩余挑战不仅是确定满足其个人需求的最佳解决方案，而且是在灾难发生之前在现实环境中测试和评估其收益。为了弥合理论潜力和实际融入实践之间的差距，欧盟资助的项目 DRIVER+ 设计了一个有条不紊的技术环境，通过试验评估现实但非操作性设置中的创新。德国航空航天中心 (DLR) 跨学科将成熟的技术发展融合到“机载和地面态势感知”系统中，并将其应用于 DRIVER+ 试验，以促进可持续和以需求为导向的研发。经验丰富的从业者评估了其模块“KeepOperational”和“ZKI”在城市地区大规模洪水背景下的附加值。该解决方案旨在提供警察行动中的上下文路线规划，并根据通过航空图像处理获得的信息扩展态势感知。用户反馈和通过 DRIVER + Test-bed 系统收集的数据认可 DLR 的系统可以改善跨组织的运输规划和态势感知。然而，结果表明需要特别考虑，例如，