Flood Mapping and Damage Assessment using Ensemble Model Approach

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 dis trict of Maharashtra, which was severely affected in the year 2021s flood.
Source: Sensing and Imaging - Category: Biomedical Engineering Source Type: research