Geomatics and Environmental Engineering, vol. 20, no. 5

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Vol. 20 No. 5 (2026): Geomatics and Environmental Engineering

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Submitted
Dec 11, 2025
Accepted
May 23, 2026
Published
Jul 30, 2026
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Enhancing the Urban Agglomeration Management Combining UAVs, Deep Learning, and WebGIS in Ho Chi Minh City, Vietnam

https://doi.org/10.7494/geom.2026.20.5.59
Ngoc Huyen Trang Tran
Ho Chi Minh City University of Natural Resources and Environment (HCMUNRE), Faculty of Geodesy, Cartography and Construction, Ho Chi Minh City, Vietnam
https://orcid.org/0000-0001-5589-2251
Van Trung Le
Vietnam National University Ho Chi Minh City (VNU-HCM), Ho Chi Minh City University of Technology (HCMUT), Faculty of Environment and Natural Resources, Ho Chi Minh City, Vietnam
https://orcid.org/0000-0002-0885-8656
Le Phu Vo
Vietnam National University Ho Chi Minh City (VNU-HCM), Ho Chi Minh City University of Technology (HCMUT), Faculty of Environment and Natural Resources, Ho Chi Minh City, Vietnam
https://orcid.org/0000-0002-5342-9324

Corresponding Author(s) : Ngoc Huyen Trang Tran

tnhtrang.sdh222@hcmut.edu.vn

Geomatics and Environmental Engineering, Vol. 20 No. 5 (2026): Geomatics and Environmental Engineering

Abstract

Ho Chi Minh City (HCMC), the biggest city and most urban agglomeration in Vietnam, has witnessed the rapid growth of urban expansion over the past several decades. However, the city authorities faced a wide range of constraints in handling urban management and planning issues. Among these, the management practices of urban construction activities and planning tasks are the most challenging due to the low quality of available data, lack of data synchronization between management departments, and the limitations in monitoring construction activities. These have resulted in poor performance in urban planning. In this study, Unmanned Aerial Vehicles (UAVs) were deployed to take photos of the existing construction activities in a residential area of HCMC. The traces of houses will be segmented and extracted automatically using a deep learning (DL) model. Using UAVs combined with DL is a solution that demonstrates high automation, which can create standardized and uniform datasets. A 3D map of the area was created and shared on the WebGIS platform. Comparing the construction status with the legal boundaries of the land plot, the horizontal and vertical construction permit standards will help detect illegal construction objects. The results of the study show that the combination of UAV images and DL shows strong potential for building 3D maps, with the accuracy of the ground and elevation being 4.8 cm and 11.9 cm, respectively. This result suggests a promising solution to assess construction status with an appropriate accuracy corresponding to a map scale of 1:500. Importantly, the achieved 3D map shared on WebGIS is effective for use as a data source between the line government agencies.

Keywords

building footprints urban management semantic segmentation transfer learning digital urban mapping
Tran, N. H. T., Le, V. T., & Vo, L. P. (2026). Enhancing the Urban Agglomeration Management Combining UAVs, Deep Learning, and WebGIS in Ho Chi Minh City, Vietnam. Geomatics and Environmental Engineering, 20(5), 59–89. https://doi.org/10.7494/geom.2026.20.5.59
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