Flood Risk Analysis through Computer Vision and Geometric Correction Approach-Reference-Cited by-同舟云学术

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Flood Risk Analysis through Computer Vision and Geometric Correction Approach

Published:2024-05-23 Issue: Volume:04 Page:259-262
ISSN:
Container-title:2024 International Conference on Computational Intelligence and Computing Applications (ICCICA)
language:
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Author:

Nimbarte Nita¹,Gedam Tanmayee¹,Masram Bharati¹,Tiwari Archana²,Balamwar Sanjay³

Affiliation:

1. Yeshwantrao Chavan College of Engineering,Electronics & Telecommunication Department,Nagpur,India

2. Shri Ramdeobaba College of Engineering,Department of Electronics Engineering,Nagpur,India

3. VNIT Campus,Maharashtra Remote Sensing and Applications Centre (MRSAC),Nagpur,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10584831/10584583/10584915.pdf?arnumber=10584915

Reference25 articles.

1. Deep learning methods for flood mapping: a review of existing applications and future research directions

2. A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery

3. Rapid Flood Mapping based on Remote Sensing Cloud Computing and Sentinel-1

4. A Spatial Improved-kNN-Based Flood Inundation Risk Framework for Urban Tourism under Two Rainfall Scenarios

5. Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

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