## 4 Discussion and Future Work  
To determine innovation inhibitors in sociotechnical setup of a CM organization and to justify required changes and associated expenses, an objective assessment becomes necessary. However, the rather fragmented, interdisciplinary working realities of CM do not allow for standard scientific research methods, which are often based on large sample size to obtain generalizable results. Using a combination of qualitative and quantitative methods in a mixed-research approach, the Trial Guidance Methodology addresses this issue and proposes a more holistic way to gain insights systematically. For our participation in the trial as technology provider, the direct feedback from users and their vision on the presented technology is of greatest value. However, the presented assessment of the DLR systems demonstrates that consequent findings are limited as they strongly depend on affiliations and tasks, unitary groups are rather small, and results only valid in the specific context. Coming from security research, the desire for obtaining more transferable findings is high and cannot be met by participating in a single trial. However, as the outcomes from DRIVER+ are distributed under an open-source license, they can be adopted by interested CM organizations. DRIVER+ also promotes establishing a pan-European network of centers of expertise that agree to apply the DRIVER+ Test-bed in future. By means of the Portfolio of Solutions, past setups and results can be documented and shared and may evolve into a living database of trials and its outcomes. From a research perspective, this provides potential to draw more robust conclusions, e.g., by learning from comparable implementations. In this trial on large-scale flooding, the evaluation team concluded that the overall module ZKI could provide valuable assistance with its rapid situation assessment of the disaster extent and associated damages. However, the trial revealed deficits especially due to lack of water depths information in the water layer. [PERSON] et al. [37] illustrate the challenges with deriving water depth from multi-source remote sensing data products that can commonly be obtained in crisis situations. Even with accurate, high-resolution base data, inherent uncertainties in the prerequisite digital terrain model and flood extent layer may cause significant vertical deviations in the water level estimates due to slight geometric inaccuracies in both datasets. Based on the experience made in the trial, we suggest taking a different approach by fusing disaster information derived from earth observation data with results of hydrological modelling. It should be considered though that crisis mapping strives for an objective representation of an actual situation, whereas hydrologic modelling is based on mathematical assumptions. This simplification incorporates again some degree of uncertainty and compromise in favor of processing capability. The approach, however, seems promising to supplement the inundation area by water depth information of a required level of detail that is difficult to achieve in rapid mapping (classes of \(<30\)cm; \(\geq 30\)-\(50\)cm; \(\geq 50\)cm). Consequently, such an interdisciplinary procedure is much needed to adapt support capacities in case of flooding to explicit user requirements and to extend mapping capabilities to the 3D space more effectively. Nonetheless, error estimates must be considered and weighed carefully. Bridging the world of modelling and remote sensing thus offers great potential, yet associated uncertainties need to be communicated appropriately. Using another trial setup to draw conclusion on results of this research topic would be a viable option. Providing data with associated depth information would also be an additional gain for KeepOperational. The tool was used during the trial by the practitioners, but not as much as asspired. Providing a new technology that is both easy to deploy and tailored to the needs for CM, is challenging.