# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 5 Discussion on Recommendations for Utilising Satellite Data for Integrated Emergency Management
Despite methods such as random rotation, flipping and Gaussian blur, it is challenging to detect whether the model is overfitting the data or how well it would work on illegal landfill sites, which vary in size significantly. Moreover, object classification has been tested only for the fire case study and lacks cross-domain transferability. To test its validity, we suggest creating a set of fire site rasters to validate the model. When deploying such a machine learning algorithm for the snow and power outage case studies, we must create a training dataset to identify blocked roads due to snow events. To maximise research investment, we suggest implementing simple rule-based systems rather than machine learning to address the challenges faced by LRFs. * Spectral resolution: Pleiades data are limited to five bands (panchromatic, red (R), green (G), blue (B) and near-infrared (NIR)) and may not be suitable for specific disaster management applications. In scenarios where thermal detection is crucial, such as monitoring fire sites, the lack of thermal infrared (TIR) bands can be a limitation. TIR bands are essential for capturing the Earth's surface temperature, which can provide critical information about the progression of a fire or the structural integrity of infrastructure. Similarly, the absence of SWIR bands can hinder effective monitoring and response when managing snow incidents. SWIR bands can differentiate between snow and clouds, which appear similar in visible and NIR bands. This differentiation is vital for accurately assessing snow cover and potential risks associated with snow-related disasters. In 2023, SatVu launched HotSat, a satellite equipped with a high-resolution thermal sensor able to capture surface temperatures with a resolution of 3.5 m [83]. Despite its short-lived cycle due to technical challenges, it demonstrated the potential of new satellite technology in this domain. * Temporal resolution: Satellite data with high spatial resolution often have a lower temporal resolution, resulting in less frequent updates on ground conditions. This trade-off can pose challenges, for example, in monitoring power outages, as damage or issues may not be identified quickly enough to allow for an effective response. Changes in infrastructure, such as the construction of new transmission lines or the decommissioning of old equipment, may not be captured promptly, potentially affecting the accuracy of risk assessments and management strategies. * Data fragmentation: Relevant requisite information for disaster management can be scattered across various organisations and systems. Different agencies are responsible for providing these services and maintaining their data, which may or may not be publicly available. For example, a centralised dataset of emergency service facilities or highway gritting routes is currently lacking in the UK. This fragmentation further complicates gathering comprehensive and up-to-date information for analysis and decision-making, rendering it challenging to create cohesive outputs and analyse large multifactorial areas. This limitation can impede the effectiveness of disaster management efforts by hindering the development of a comprehensive understanding of the risks and resources available for response. * Data volume and processing: Users need significant computational resources and storage capacity to store, process, and analyse large file sizes, which can be a constraint for users with limited IT infrastructure such as the LRFs. * Dependency on external software for advanced processing: Before users can use Earth observation satellite data, they often require external processing software, for which they may also need help to use. Using satellite imagery for analysis also requires niche skills and knowledge of data access, integration and transformation before insights can be integrated into further decision-making during emergency management.