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Edge Device Integration to visualize Blue Whale Tracking using Space-Borne Remote Sensing Datascientifically
  • prudhvi Narayana Bandaru,
  • * DrSVasavi,
  • Balasai Sigireddy
prudhvi Narayana Bandaru
Velagapudi Ramakrishna Siddhartha Engineering College

Corresponding Author:prudhvinarayanabandaru@gmail.com

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* DrSVasavi
Velagapudi Ramakrishna Siddhartha Engineering College
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Balasai Sigireddy
Velagapudi Ramakrishna Siddhartha Engineering College
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Abstract

Introducing a CNN-based system for autonomously detecting and tracking blue whales in the Indian Ocean using spaceborne images. Leveraging deep learning models and image processing techniques, the system achieves accurate results with SASPlanet, UK Polar data, and Worldview-2 imagery. Employing Modified-MobileNet,RTDETR, and Segformer architectures, it offers a cost-effective solution for Detection , Segmentation and visualizing whales on edge devices like Telegram and WhatsApp bots. Evaluation metrics include an F1-score of 80, mAP of 83, precision of 90%, and recall of 98%, demonstrating robust performance in whale detection using spaceborne remote sensing data.
17 Jun 2024Submitted to Marine Ecology
17 Jun 2024Submission Checks Completed
17 Jun 2024Assigned to Editor
18 Jun 2024Reviewer(s) Assigned
02 Sep 2024Review(s) Completed, Editorial Evaluation Pending
08 Sep 2024Editorial Decision: Revise Major
26 Sep 20241st Revision Received
26 Sep 2024Submission Checks Completed
26 Sep 2024Assigned to Editor
27 Sep 2024Reviewer(s) Assigned
22 Nov 2024Review(s) Completed, Editorial Evaluation Pending
17 Dec 2024Editorial Decision: Revise Major