[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Contact us::
Site Facilities::
Search in website

Advanced Search
Receive site information
Enter your Email in the following box to receive the site news and information.
:: Volume 13, Issue 1 (9-2023) ::
JGST 2023, 13(1): 13-26 Back to browse issues page
A novel deep neural network for multi-scale building extraction from remotely-sensed images
Mohammad Parsaei , Saeid Niazmardi * , Ali Esmaeily
Abstract:   (90 Views)
Building extraction is one of the most crucial requirements of urban planning. Due to their availability and affordable cast, high-resolution remotely sensed images are often used for building extraction. Owing to their impressive performances, Deep learning techniques have attracted the attention of researchers for building extraction from high-resolution images. Nevertheless, most existing models perform poorly in recovering spatial details and discriminating buildings with various sizes and shapes. Hence, this paper proposes an improvement module to address the problems associated with multi-scale building extraction. The proposed module uses dilated convolutions to increase the receiving information area to reduce the discontinuities in the results of large buildings. Extracting large buildings using the proposed module and small buildings using the main architecture of the network has turned the proposed network into an effective method for building extraction. The results of the experiments showed that the proposed module with the IoU of 0.6495 and 0.8572 for Massachusetts and WHU data sets outperformed FCN, U-Net, USSP, and DeepLab V3+. The performance analysis of the proposed module also showed that this module was able to improve the performance of building extraction considering the IoU metric by 0.1077.
Article number: 2
Keywords: Building extraction, neural networks, remote sensing images, deep learning, multi-scale analysis
Full-Text [PDF 1496 kb]   (57 Downloads)    
Type of Study: Research | Subject: Photo&RS
Received: 2022/09/12
Send email to the article author

Add your comments about this article
Your username or Email:


XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Parsaei M, Niazmardi S, Esmaeily A. A novel deep neural network for multi-scale building extraction from remotely-sensed images. JGST 2023; 13 (1) : 2
URL: http://jgst.issgeac.ir/article-1-1111-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 13, Issue 1 (9-2023) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology