## 4 Discussion and Future Work
Teaching the use of KeepOperational did not take long but adapting the learned and established process to incorporate a new tool into common practices takes significantly more time. In the preparation phase of the trial, KeepOperational was shown to the police and new features were added based on the input of the practitioners involved but were neglected in the trial. This example highlights that incorporating technical innovation may not only fail because user requests cannot be met, individual commitment to innovation on the part of the users is equally important. The trial disclosed that the technical maturity of KeepOperational needs to be further improved and continuously expanded. Even though the usability scored well, e.g., search capabilities or user administration for simultaneous use by different groups must be addressed to comply with user requirements. Furthermore, the performance in crisis scenarios of even larger geographic extent is an issue that needs to be tackled in future. The interdisciplinary and heterogenic working reality of CM practitioners complicates the provision of value-added products tailored to the needs of an entire user community simultaneously. The layered GeoPDF products are only one attempt to maximize the cost-benefit ratio. The trial revealed potential for improvement in usability aspects to better handle the interactive maps. The 3D PDF version especially showed deficits for comprehensive map applications. Whilst the PDF format can be easily exchanged and displayed without having to rely on additional software and continuous internet connectivity, the file-based approach reduces its acceptance by the user. Its rigid spatial extension turned out to be unsuitable when larger areas are to be depicted with high information density (e.g., urban areas). Web-based mapping dashboards that integrate latest crisis layers are already helping to keep an overview in large-scale disasters and support situation assessment in CM particularly due to their real-time capabilities. One recent example that demonstrated the benefits of these dynamic and highly automated mapping platforms is the dashboard created by researchers of Johns Hopkins University on spread of the newly emerged Coronavirus SARS-CoV2 [38]. However, so far, these dashboards are limited to insights in 2D space. In metropolitan areas where 3D data collection is increasing and regularly updated city models are common standard, the extension of such dashboards into the third dimension would be a logical progression. The trial conducted in The Hague just proved once more that 3D information is in high demand by the CM domain to support decision making comprehensively in large-scale crisis scenarios. As reflected in the evaluation results, the additional value created by data interpretation and compilation as map products is, however, compromised by its time component. Studies conducted by [PERSON] et al. [39] show that the value of remotely sensed information products decreases exponentially with the time lag to the event. This emphasizes that expanding the near real-time capability of the DLR \"Airborne and terrestrial situational awareness\" system also to the part of ZKI's mapping pipeline is crucial. Artificial intelligence techniques show great promise to speed-up the task of analysis and value-adding. \"Classical\" machine learning methods (e.g., SVM, random forest) [40] are increasingly being replaced by more powerful convolutional neural networks (CNN). DLR's research is amongst others developing on CNN for three areas of cognitive image data analysis: Semantic segmentation (e.g., flood areas), object recognition (e.g., buildings), and change detection (e.g., damage to buildings). Another interesting use case building upon deep learning methods is presented by [PERSON] et al. [41] on enhancing results through automated multisource/-scale data analysis.