Damage assessment of Libya 2023 floods using Object-based and Pixel-based classifications

Abstract. On September 11, 2023 the city of Derna in Libya experienced catastrophic flooding due to heavy rains from storm Daniel, The collapse of two dams led to widespread city flooding, causing extensive damage and loss of lives. This study employs a remote sensing approach, incorporating different methods, such as object-based image analysis (OBIA), pixel-based classification, and change detection, to assess flood damage in the city of Derna in the aftermath of Storm Daniel. The analysis focuses on post-flood changes in land cover classes, including built-up areas, roads, vegetation, bareland, and water bodies. Quantitative analysis revealed 111,400 m² of land cover alterations, with 30,350 m² of roads submerged in waterbodies—the most severely impacted infrastructure. Thematic maps and statistics (e.g., 19,624 m² of built-up areas submerged) provide actionable insights for prioritizing recovery efforts. This research provides valuable insights for decision-makers focusing on resilient urban recovery efforts. Using remote sensing, the study assessed damages to key urban elements, including residential structures, transportation networks, and vegetation cover. The findings highlight the widespread devastation caused by the floods, with roads and buildings identified as the most severely impacted infrastructure. The study's recommendations aim to support local and national governments in effectively allocating resources for both structural and non-structural flood mitigation strategies.