Detection for damaged Buildings after hurricane Lota based on single and ensemble model

Abstract:
Hurricane Lota caused huge damage to The United States hundreds of people lost both life and home, to prevent time consumption which was cause by ordinary ways of information and data collections, satellite photography is also being utilized for damage identification due to its capacity to cover wide spatial and temporal areas, which makes it challenging to analyses huge volume data. However, rescue operations necessitate a quick response, which sets increased demands on categorization methods. To improve the efficiency for detecting damage areas based on hurricane image captured by satellites. This study looks at how machine learning and deep learning may be used to detect damage from natural catastrophes, with an emphasis on hurricane damage.