Python Based Modelling of Flood Damage Assessment Using High-Resolution Aerial Imagery

Sumaira Kousar; Saira Batool; Syed Amer Mahmood; Amer Masood; Safdar Ali Shirazi; Jahanzeb Qureshi; S M Hassan

Authors

Sumaira Kousar Institute of Geography, University of the Punjab
Saira Batool Center of Integrated Mountain Research, University of the Punjab
Syed Amer Mahmood Department of Space Science, University of the Punjab Lahore
Amer Masood Department of Space Science, University of the Punjab Lahore
Safdar Ali Shirazi Institute of Geography, University of the Punjab
Jahanzeb Qureshi Department of Space Science, University of the Punjab Lahore
S M Hassan Department of Space Science, University of the Punjab, Lahore, Pakistan

Abstract

Flood is a natural disaster that can cause devastating impacts on the community, infrastructure, and the environment. UAVs enable to compute the extent of the flood and to identify the vulnerable areas prone to future flooding, assisting in the formulation of effective mitigation strategies. This study presents a case study of Barwai Khwar, Swat, Khyber Pakhtunkhwa (KPK), pre-flood image attained from Google Earth Pro and the post-flood aerial imagery was collected by using unmanned aerial vehicles (UAVs). To capture the detailed visual information of the flood-affected region and to assess the extent of the flood damage the acquired imagery was then processed by using advanced image processing algorithms to extract essential information, such as inundation extent, floodwater depth, and changes in land cover. This procedure assists in evaluating the precise damage assessment and development of effective recovery and mitigation strategies. Results revealed that the 2022 flood in Barwai Khowar's large agricultural land was submerged (14758.9 perimeters), leading to a significant loss in crop yield and potential long-term impacts on food security. Additionally, critical infrastructure, including roads, bridges, and buildings suffered substantial damage. The destructed area of the retaining wall is 2184m (2km), housing damage is 1074.9m and 82.6 m of Nullah was calculated in this region. Moreover, the application of such technologies can facilitate more informed and timely responses to natural disasters, enhancing the overall resilience of communities and ecosystems.

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