Flood Mapping and Damage Assessment using Ensemble Model Approach

Flood is the most frequently occurring and dangerous natural disaster, which leads to loss of human life, economic loss, and agricultural loss. It also has an impact on a variety of services, including health, education, and transportation etc. So, in order to give assistance and conduct rescue operations promptly, flood detection, its mapping, and flood damage assessment are crucial duties. Additionally, they support urban planning, building design, and other future endeavors. This study focuses on generating flood maps using synthetic aperture radar images from the Sentinel-1 (COPERNICUS/S1_GRD) satellite. Further study includes damage assessments in seven different sectors: urban land, agricultural land, forest land, barren land, range land, permanent water bodies, and unknown. This forecasts how much of the land in these 7 areas was affected by flooding. For the aforementioned land use and land cover classifications, the study proposes the best-fitting ensemble model, which is the aggregate of 3 image segmentation models that are Resnet34, InveptionV3, and VGG16. These three models are trained on the DeepGlobe dataset to give a mean Intersection over Union score of 75.84% and an F1 score of 0.76. A further proposed damage assessment technique is validated on a selected study area, i.e., village Vasagade from Kolhapur district of Maharashtra, which was severely affected in the year 2021s flood.