Implementation of remote-sensing models to identify post-disaster health facility damage: Comparative approaches to the 2023 earthquake in Turkey.

ABSTRACT: Earthquakes and other disasters often cause substantial damage to health facilities, impacting short-term response capacity and long-term health system needs. Identifying health facility damage following disasters is therefore crucial for coordinating response, but ground-based evaluations require substantial time and labor. Artificial intelligence (AI) models trained on satellite imagery can estimate building damage and could be used to generate rapid health facility damage reports. There is little published about methods of generating these estimates, testing real-world accuracy, or exploring error. This study presents a novel method of overlaying model damage outputs with health facility location data to generate health facility damage estimates following the February 2023 earthquake in

SUBMITTER: Ramachandran A 

PROVIDER: S-EPMC12558478 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature