Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

49
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
49

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Reinforcement learning-driven adaptive game therapy for cognitive impairment patients with improved vision transformer based detection model.

BMC psychology·2026
Same author

HLA Class II Alleles DRB1*11:01 and DQB1*03:01 Unmask Immunogenetic Susceptibility to Anti-Nivolumab Antibodies in Combination with Ipilimumab.

The AAPS journal·2026
Same author

Secure and intelligent SDN-IoV framework with blockchain-based authentication and optimization-based QoS routing.

Scientific reports·2026
Same author

ROI-guided relational YOLO-SegNet transformer for lightweight bone tumor segmentation and classification from X-ray images.

Scientific reports·2026
Same author

Inspection of pollination transfer and success in coffee flowering detection using intersection over union based cascade RCNN in a vision environment.

Scientific reports·2026
Same author

Trimester-aware yoga video recommendation using hybrid deep learning for pregnant woman.

Scientific reports·2026

Related Experiment Video

Updated: Jul 1, 2025

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

6.4K

Archimedes optimisation algorithm quantum dilated convolutional neural network for road extraction in remote sensing

Arun Mozhi Selvi Sundarapandi1, Youseef Alotaibi2, Tamilvizhi Thanarajan3

  • 1Department of Computer Science and Engineering, Holycross Engineering College, Thoothukudi, 628851, India.

Heliyon
|March 12, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for high-resolution road extraction from remote sensing images, improving accuracy by integrating quantum dilated convolutional neural networks with an optimization algorithm for better feature capture.

Keywords:
Artificial intelligenceConvolutional neural networkDilated convolutionRemote sensingRoad extraction

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

544
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

533

Related Experiment Videos

Last Updated: Jul 1, 2025

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

6.4K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

544
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

533

Area of Science:

  • Remote Sensing
  • Artificial Intelligence
  • Image Processing

Background:

  • Automated road extraction from remote sensing images (RSI) is crucial but challenging due to variations in road appearance.
  • Deep learning (DL) methods show promise but often struggle with boundary details and high-resolution mapping in RSI.
  • Traditional Convolutional Neural Networks (CNNs) face limitations in capturing intricate details and contextual information for road extraction.

Purpose of the Study:

  • To introduce a novel DL-based method, Archimedes Optimisation Algorithm, Quantum Dilated Convolutional Neural Network for Road Extraction (AOA-QDCNNRE), for high-resolution road segmentation in RSI.
  • To enhance the capacity of DL models to capture both local and global contextual information for precise road feature extraction.
  • To leverage hyperparameter tuning via the Archimedes Optimisation Algorithm to improve the performance of the Quantum Dilated Convolutional Neural Network (QDCNN).

Main Methods:

  • The study proposes the AOA-QDCNNRE technique, which combines Quantum Dilated Convolutional Neural Networks (QDCNN) with the Archimedes Optimisation Algorithm (AOA).
  • The QDCNN model integrates quantum technology (QC) with dilated convolutions to expand the receptive field without losing spatial resolution, improving feature capture.
  • AOA is employed for hyperparameter tuning of the QDCNN model to optimize road extraction outcomes.

Main Results:

  • The AOA-QDCNNRE technique successfully generates high-resolution road segmentation maps.
  • The integration of dilated convolutions enhances the network's ability to capture precise road features.
  • Simulation results on benchmark databases demonstrate that AOA-QDCNNRE outperforms existing algorithms in road extraction tasks.

Conclusions:

  • The AOA-QDCNNRE method offers a significant advancement in automated road extraction from remote sensing images.
  • The proposed approach effectively addresses the challenges of detail retention and high-resolution mapping in complex RSI.
  • The study highlights the potential of combining quantum-inspired deep learning with optimization algorithms for improved geospatial data analysis.