Version 1
: Received: 9 April 2024 / Approved: 9 April 2024 / Online: 10 April 2024 (12:10:22 CEST)
How to cite:
Hussain, Z.K.; Congshir, J.; Xin, Y.X.; Mustafa, M.R.E. A Comparative Study of PP-LiteSeg, Dual Attention Network, DeeplabV3p and Asymmetric Neural Network for Rooftop Detection in UAV Images. Preprints2024, 2024040705. https://doi.org/10.20944/preprints202404.0705.v1
Hussain, Z.K.; Congshir, J.; Xin, Y.X.; Mustafa, M.R.E. A Comparative Study of PP-LiteSeg, Dual Attention Network, DeeplabV3p and Asymmetric Neural Network for Rooftop Detection in UAV Images. Preprints 2024, 2024040705. https://doi.org/10.20944/preprints202404.0705.v1
Hussain, Z.K.; Congshir, J.; Xin, Y.X.; Mustafa, M.R.E. A Comparative Study of PP-LiteSeg, Dual Attention Network, DeeplabV3p and Asymmetric Neural Network for Rooftop Detection in UAV Images. Preprints2024, 2024040705. https://doi.org/10.20944/preprints202404.0705.v1
APA Style
Hussain, Z.K., Congshir, J., Xin, Y.X., & Mustafa, M.R.E. (2024). A Comparative Study of PP-LiteSeg, Dual Attention Network, DeeplabV3p and Asymmetric Neural Network for Rooftop Detection in UAV Images. Preprints. https://doi.org/10.20944/preprints202404.0705.v1
Chicago/Turabian Style
Hussain, Z.K., Yuan Xin Xin and Muhammad Raza e Mustafa. 2024 "A Comparative Study of PP-LiteSeg, Dual Attention Network, DeeplabV3p and Asymmetric Neural Network for Rooftop Detection in UAV Images" Preprints. https://doi.org/10.20944/preprints202404.0705.v1
Abstract
Remote sensing technology is crucial for accurate rooftop detection, benefiting urban planning, disaster management, and solar resource estimation. This study employs Efficient Interactive Segmentation (EISEG) to enhance the efficiency of remote sensing image labeling, with a particular focus on rooftop detection. It is necessary to use modern technology because traditional manual labelling methods are labor-intensive and complicated. The study introduces a novel framework on deep learning semantic segmentation models, facilitating an efficient approach to rooftop identification using high-resolution UAV remote sensing datasets. Large dataset of labeled UAV rooftop building images, in which each superpixel region is assigned a binary label indicating rooftop presence. Advanced methods including Asymmetric Neural Network (ANN), Dual At-tention Network (DANet), PP-LiteSeg, and Deeplab3 are implemented for automatic rooftop detection due to their higher performance and advanced architectures. These models are executed on the Baidu deep learning platform PaddlePaddle, generating initial rooftop segmentation maps crucial for estimating photovoltaic resources. The ANN model emerges with the highest accuracy at 96%, followed by DANet at 95.09%, PP-LiteSeg at 94.54%, and Deeplab3 at 81.61%. The outcomes presenting efficient models for automated rooftop identification, and demonstrating the continuous need for improving deep learning techniques in smart and sustainable cities.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
