The importance and challenges of data collection in risk assessment

¹ Frederick Research Center, 1036 Nicosia, Cyprus
² Department of Mechanical Engineering, Frederick University, 1036 Nicosia, Cyprus
³ Department of Civil Engineering, Frederick University, 1036 Nicosia, Cyprus

^* Corresponding author: g.xekalakis@frederick.ac.cy

Abstract

Reliable data collection is paramount for assessing and mitigating risks posed by natural hazards and climate change to urban sustainability and resilience. This study underscores the significance of diverse data collection methods, including field surveys, artificial intelligence (AI) analysis, and satellite imagery, each essential for developing robust risk assessment frameworks. Field surveys offer detailed insights into structural and non-structural building components, critical for localized risk assessments. AI enhances data processing efficiency, crucial for handling extensive datasets and enabling rapid response capabilities. Satellite imagery provides comprehensive area coverage, invaluable for strategic planning and emergency management in large or inaccessible areas. However, these methods also present unique challenges. Field surveys are resource-intensive and susceptible to environmental conditions, which can affect data accuracy. AI, while efficient, requires high-quality data and may struggle with complex scenarios that deviate from its training data. Satellite imagery, although broad-reaching, may lack the necessary resolution for detailed assessments and is dependent on weather conditions. Addressing these challenges is crucial to ensuring the integrity and reliability of risk assessments. By continuously refining these methods and maintaining high standards for data quality and ethical considerations, we can better prepare for and mitigate the impacts of natural hazards and climate change. This commitment to advancing data collection techniques is essential for protecting our built environments and ensuring community resilience.