# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 5 Discussion on Recommendations for Utilising Satellite Data for Integrated Emergency Management
Toidentify areas with the most vulnerable populations during extreme weather events, [PERSON] and [PERSON] [82] suggest including people aged 65 years and above, young children, people with severe illnesses, homeless people and people with adequate heating or insulation, and critical infrastructure, such as hospitals, elderly homes and community centres. Local authorities, including health workers and social care personnel, can use vulnerability analysis to implement policies and take mitigation actions to reduce the risks of cold weather. The analysis results revealed some synergies across the snow and power outage case studies, i.e., areas most vulnerable to electricity transmission failures were also likely to suffer from low temperatures and snow events from winter storms. * Data fusion:  
To overcome the spectral limitations of high-spatial-resolution data, disaster management organisations often employ data fusion techniques, combining data from multiple satellite sensors with complementary spatial and spectral resolutions. For example, we can combine data from Pleiades with sensors that provide TIR and SWIR bands, such as Landsat or Sentinel-2, to create a more comprehensive dataset for effective disaster management. This approach allows practitioners to leverage the high spatial resolution of, e.g., Pleiades data while benefiting from the broader spectral information provided by other satellite systems. * Develop geospatial dashboard solution with advanced analyses:  
While R2-D2 started building the spatial data infrastructure through a prototype to demonstrate capability, work on this initial prototype could continue. The initial dashboard prototype on the three identified hazards can serve as a basis for stakeholder discussions by providing a holistic overview, visualisation and analysis examples of the three specific investigated incident types. Future work can enhance the dashboard's utility and flexibility by incorporating additional interactive features, allowing users to explore and analyse the data more effectively. For example, for change detection analysis, we suggest a manual analysis option for all three response types on the dashboard, allowing users to select an area and period to investigate change. The GEE Python API enables users to fetch the relevant number of open-access Sentinel-1 images (or Airbus Pleiades) and generate the change detection polygons, which stakeholders can query, as demonstrated in the fire case study. * Collaborate with emergency agencies for improved data availability:  
High-resolution satellite imagery can involve significant costs. We suggest establishing partnerships and collaborations between public and private organisations, cost-sharing agreements, data access grants or subsidies, in-kind contributions, or joint research and development initiatives to facilitate and standardise data sharing and ensure that multi-agencies can access relevant data for disaster management. Collaborations can help balance costs and maximise the use of high-resolution satellite data in disaster management dashboards. If multiple stakeholders need access to the same data, synergies can be achieved and saving opportunities can be leveraged. Although this study has illustrated how geospatial data analysis may be improved using satellite imagery, there are several existing challenges facing its use and application for emergency management. * Data availability:  
Due to a lack of data, this scoping study could not train and validate a machine learning algorithm to detect the location of fire sites automatically, as these models rely on high-quality training data. Previous studies, such as [49], used training data for computer vision models from waste sites in Hungary, Serbia, India and Brazil. However, the existing dataset is still relatively small, with approximately 200 images from 13 sites. Semanticsegmentation is the preferred method for addressing computer vision issues, such as landfill detection, but creating accurate training masks demands significant manual effort.