AI Driven Impact Assessment of Shaheen Tropical Cyclone Using Very High-Resolution Satellite Data

Understanding tropical cyclones (TCs) impact is critical for implementing effective post–cyclone management methods. While previous researches have assessed the effects of TCs in Oman, this study uniquely examines and quantifies the impact of the Shaheen tropical cyclone (STC) using advanced image resolution and analysis, including artificial intelligence in the form of Multiple Deep learning models, as well as using very high-resolution (41 cm) satellite imagery. This study found significant vegetation loss, with 85.8 ha (51.3%) of dense and 153.2 ha (18.9%) of sparse vegetation destroyed in Al–Khabourah. Al–Suwaiq was the most affected wilayat, losing 3160 ha (55.5%) of shrubs and grasses, 1713.9 ha (67.2%) of dense vegetation, and 609.8 ha (16.6%) of sparse vegetation. The cyclone also caused drastic increases in water surface areas, especially in Al–Khabourah by 496.82 ha and Al–Suwaiq by 180.8 ha. Additionally, 32% (42.73 ha) of buildings in Al–Khabourah and 67.5% (6.4 ha) of fishermen’s coastal structures were damaged. The deep learning model accurately classified land cover types with high precision (1, 0.93, 0.89, and 0.9 for barren, dense, sparse vegetation, and buildings, respectively) and an overall accuracy of 94%. Digital Elevation Models and Ruggedness Index maps identified flood-prone zones, especially in coastal areas. Ground validation confirmed model performance with an overall accuracy of 94%. This study demonstrates the effective application of artificial intelligence in the form of multiple deep learning techniques for detecting and mapping various objects, including vegetation, buildings, and water bodies, in the aftermath of the impact of TC. These insights can support government agencies and landowners in developing more effective post–cyclone planning and climate resilience strategies, especially in urban and coastal regions.