Building Extraction from Unmanned Aerial Vehicle (UAV) Data in a Landslide-Affected Scattered Mountainous Area Based on Res-Unet

Chunhai Tan, Tao Chen, Jiayu Liu, Xin Deng, Hongfei Wang and Junwei Ma ()
Additional contact information
Chunhai Tan: Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
Tao Chen: Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
Jiayu Liu: Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
Xin Deng: Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
Hongfei Wang: Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
Junwei Ma: Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China

Sustainability, 2024, vol. 16, issue 22, 1-15

Abstract: Building extraction in landslide-affected scattered mountainous areas is essential for sustainable development, as it improves disaster risk management, fosters sustainable land use, safeguards the environment, and bolsters socio-economic advancement; however, this process entails considerable challenges. This study proposes a Res-Unet-based model to extract landslide-affected buildings from unmanned aerial vehicle (UAV) data in scattered mountain regions, leveraging the feature extraction capabilities of ResNet and the precise localization abilities of U-Net. A landslide-affected, scattered mountainous region within the Three Gorges Reservoir area was selected as a case study to validate the model’s performance. Experimental results indicate that Res-Unet displays high accuracy and robustness in building recognition, attaining accuracy (ACC), intersection-over-union (IOU), and F1-score values of 0.9849, 0.9785, and 0.9892, respectively. This enhancement can be attributed to the combined model, which amalgamates the skip connections, the symmetric architecture of U-Net, and the residual blocks of ResNet. This integration preserves low-level detail during recovery at higher levels, facilitating the extraction of multi-scale features while also mitigating the vanishing gradient problem prevalent in deep network training through the residual block structure, thus enabling the extraction of more complex features. The proposed Res-Unet approach shows significant potential for the accurate recognition and extraction of buildings in complex terrains through the efficient processing of remote sensing images.

Keywords: Res-Unet; building extraction; scattered mountainous area; unmanned aerial vehicle (UAV) data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/22/9791/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/22/9791/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:22:p:9791-:d:1517624

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().