The importance and challenges of data collection in risk assessment
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...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/08/e3sconf_eenviro2024_05007.pdf |
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| author | Xekalakis Georgios Fokaides Paris Christou Petros |
| author_facet | Xekalakis Georgios Fokaides Paris Christou Petros |
| author_sort | Xekalakis Georgios |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a6a58073f4574d5cb3edba3d50cfde0c |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-a6a58073f4574d5cb3edba3d50cfde0c2025-02-05T10:49:34ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016080500710.1051/e3sconf/202560805007e3sconf_eenviro2024_05007The importance and challenges of data collection in risk assessmentXekalakis Georgios0Fokaides Paris1Christou Petros2Frederick Research CenterDepartment of Mechanical Engineering, Frederick UniversityFrederick Research CenterReliable 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/08/e3sconf_eenviro2024_05007.pdf |
| spellingShingle | Xekalakis Georgios Fokaides Paris Christou Petros The importance and challenges of data collection in risk assessment E3S Web of Conferences |
| title | The importance and challenges of data collection in risk assessment |
| title_full | The importance and challenges of data collection in risk assessment |
| title_fullStr | The importance and challenges of data collection in risk assessment |
| title_full_unstemmed | The importance and challenges of data collection in risk assessment |
| title_short | The importance and challenges of data collection in risk assessment |
| title_sort | importance and challenges of data collection in risk assessment |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/08/e3sconf_eenviro2024_05007.pdf |
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