Introduction

The image segmentation model can be used to extract real-world objects from images, blur backgrounds, create self-driving automobiles, and perform other image processing tasks. The goal of this research is to create a mask that shows floodwater in a given location based on Sentinal-1 (a dual-polarization synthetic-aperture radar (SAR) system) images or features.

Dataset

The dataset collected from the competition: Map Floodwater from Radar Imagery, which is hosted by Microsoft AI for Earth. The dataset consists of Sentinel-1 images and masks, as well as a CSV file with metadata such as city and year. Sentinel-1 images and masks were acquired from various parts of the world between 2016 and 2020. In total, the dataset consists of 542 chips (1084 images) and corresponding masks. Based on how radar microwave frequency transmits and receives, a single chip comprises two bands or images. Vertical transmit and receive are represented by the VV images. On the other hand, VH images stand for vertical transmit and horizontal receive. Each picture is saved as a GeoTIFF file with the dimension of 512 X 512. The mask consist of three categories:

• Water: 1

• NON-Water: 0

• unlabeled: 255