ADaSci Premium Membership fee will be revised from 1st March 2024. Lock your membership for 1 year at current price.

Detection of Flood Damaged Areas using Convolutional Neural Network

Author(s): Surya Pratap Singh, Harmeet Thukran

Abstract

The project is titled as “Detection of flood-damaged areas”. Its purpose is to detect the areas which are affected and aren’t affected by floods using the satellite images. With help of several architectures of Convolution Neural Networks (CNN) . We have used DenseNet 121 without any feature extraction and attained a validation accuracy on the last epoch as 94.6% and the testing accuracy of 97.4% further we have used ORB which has test accuracy of 95.1% and edge detection which provides the test accuracy of 93.4%. All these architectures were performed on the same model.

The Chartered Data Scientist Designation

Achieve the highest distinction in the data science profession.

Elevate Your Team's AI Skills with our Proven Training Programs

Strengthen Critical AI Skills with Trusted Generative AI Training by Association of Data Scientists

Explore more from Association of Data Scientists