The Magazine of Geodesy – Cartography, established in 1970, is one of the long-standing scientific journals in Vietnam specializing in geodesy, cartography, remote sensing, GIS, land administration, and Earth sciences. The journal applies an independent peer-review process to ensure academic quality, objectivity, and scientific integrity of published manuscripts. The peer-review process for regular research articles is typically completed within 2–3 weeks. Once accepted, articles are prioritized for editing and online publication at the earliest possible time. The entire publication process, from submission to official publication, is generally completed within approximately 2 months, in line with the journal’s orientation toward modern, transparent, and internationally integrated scientific publishing standards. The entire publication process is completed within 2 months.
Copyright remains with the authors. Authors grant the Magazine of Geodesy – Cartography the right of first publication. All articles published by the journal are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under the CC BY 4.0 license, readers are free to read, download, copy, distribute, print, search, and reuse the published work, provided that appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made to the work are indicated. Authors are responsible for obtaining permission for any third-party copyrighted material included in their manuscripts.
Analysis of impervious surface expansion trends during urbanization in Long An ward using Sentinel-2 imagery from 2016 to 2025
The urbanization process has led to a significant increase in impervious surface area, reducing the natural water infiltration capacity. On the other hand, urbanization also serves as a driving force for socio-economic development. This study utilizes Sentinel-2 satellite imagery at three time points (2016, 2020, and 2025), combined with the Random Forest algorithm on the Google Earth Engine platform, to classify and map impervious surfaces and analyze urban expansion trends over time in Long An Ward. The results show that impervious surfaces have rapidly increased, from 5.76 km² in 2016 to 7.01 km² in 2020, reaching a peak of 10.46 km² in 2025. Notably, the trend of impervious surface expansion is mainly concentrated in the southeastern and northeastern areas of Long An Ward, as identified through the Urban Expansion Intensity Index (UEII). This approach provides an important data foundation for planning and supports sustainable urban management in the context of increasing urbanization.
Copyright (c) 2026 Nhân Nguyễn, Dung Huỳnh Ngọc, Phương Văn Ngọc Trúc, Bảo Lê Thiên (Tác giả) https://creativecommons.org/licenses/by/4.0
Optical atomic clock network model for unifying the Asian height system
One of the fundamental tasks of geodesy is to unifing the height systems of countries worldwide or within a specific region. This will help ensure accuracy in research for transnational and intercontinental projects, such as sea level rise or transnational construction projects. However, traditional methods such as spirit leveling are not feasible due to their high time and cost. One of the recent research directions is the application of relativistic geodesy to unifing the global height system. In this study, the authors developed a simulation to standardize the Asian height system using a network of high-precision optical clocks. The simulation results show that it is possible to unifing the Asian height system with an accuracy of 2 ÷ 3cm. This is a promising result for further practical research.
Copyright (c) 2026 Dương Nguyễn, Hoàng Anh Thế (Tác giả) https://creativecommons.org/licenses/by/4.0
Land suitability assessment for the development of solanum procumbens cultivation in Ba To commune, Quang Ngai province
This study was conducted to evaluate the land suitability for the cultivation of Solanum procumbens Lour. (Ca gai leo) in Ba To Commune, Quang Ngai Province, providing a scientific basis for land-use planning and the sustainable development of medicinal plant production. Based on the FAO land evaluation framework, integrated with the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and Geographic Information Systems (GIS), a land mapping unit (LMU) map was developed by overlaying thematic layers, including soil type, soil texture, soil depth, slope, and irrigation conditions. The weights of the evaluation criteria were determined using the Fuzzy-AHP method, while the thematic layers were standardized based on fuzzy set theory and integrated within the GIS environment to generate a land suitability map. The assessment, covering an area of 11,698.02 ha, classified the land into four suitability classes: highly suitable (S1), moderately suitable (S2), marginally suitable (S3), and not suitable (N). The primary limiting factors affecting the cultivation of Solanum procumbens were soil type, soil depth, and soil texture. The results identified areas with high potential for cultivating Solanum procumbens and provide an important scientific basis for land-use planning, spatial allocation of agricultural production, and the sustainable development of medicinal plant cultivation in Ba To Commune, Quang Ngai Province.
Copyright (c) 2026 Quang Hiển Trương, Tấn Nghị Đỗ, Hồng Đức Ngô (Tác giả) https://creativecommons.org/licenses/by/4.0
Accuracy of the potential of spaceborne LiDAR data in estimating urban building heights in Hanoi
This study evaluates the potential of spaceborne LiDAR data from the ICESat-2 and GEDI sensor systems for the automated extraction and estimation of urban building heights in the inner wards of Hanoi, Vietnam. The proposed workflow integrates building footprint boundary data with a point-cloud noise filtering procedure to accurately classify return signals from building rooftops and reference ground surfaces. Based on an independent validation dataset consisting of 200 field-survey points, the statistical analysis indicates that GEDI full-waveform LiDAR data achieved high reliability with a coefficient of determination (R²) of 0.89, whereas ICESat-2 photon-counting technology demonstrated advantages in detailed along-track spatial resolution with an R² value of 0.86. The results highlight the capability of ICESat-2 and GEDI datasets to effectively supplement or partially replace conventional field-based surveying methods. This study also demonstrates strong potential for large-scale applications in optimizing urban planning, monitoring architectural development dynamics, and accelerating the development of 3D urban spatial databases in rapidly growing megacities across Vietnam.
Copyright (c) 2026 Tuấn Kiệt Hoàng, Phương Lan Vũ, Phương Bắc Nguyễn (Tác giả) https://creativecommons.org/licenses/by/4.0
Application of artificial intelligence and remote sensing in modern land degradation assessment
Current land degradation assessment is carried out through the integration of geospatial technologies with field surveys and sampling to map the extent, scale, and distribution of six types of land degradation according to land parcels. The application of artificial intelligence (AI) enables enhanced analytical capabilities and accurate, detailed, and continuous prediction of land degradation status. This study analyzes the scientific basis for applying machine learning and deep learning models, trained on multi-source feature datasets such as satellite imagery, natural condition data, and soil sample analyses, to derive thematic data layers on indices and coefficients used in evaluating six types of land degradation. The research aims to guide methods for integrating remote sensing, GIS, and artificial intelligence data in building automated, regularly updated, and widely applicable land degradation prediction models.
Copyright (c) 2026 Nguyễn Thanh Thuỷ, Nguyễn Phi Sơn, Đinh Ngọc Phú, Trần Xuân Trường (Tác giả) https://creativecommons.org/licenses/by/4.0
Assessment of the Impacts of Widespread Waterlogging on Agricultural Land Use in Dai Hue Commune, Nghe An Province Using GIS and Remote Sensing Technology
The purpose of this study is to assess the impact of flooding on agricultural land use in Dai Hue commune, Nghe An province. In this study, remote sensing and Geographic Information Systems (GIS) technologies were applied to create an effective flood map from active radar remote sensing data such as Sentinel-1 and Sentinel-2. Analysis of multi-temporal satellite imagery data (Sentinel-1 and Sentinel-2) before and after heavy rainfall, with the assistance of ENVI and SNAP software, was used to determine the extent and severity of flooding. Land use maps and area maps before and during flooding were overlaid to calculate the affected agricultural land area. The results identified the most vulnerable agricultural areas, estimated crop damage caused by flooding, and analyzed changes in farming patterns following flood events. These findings provide a scientific basis for sustainable land use planning, disaster risk management, and offer several adaptation solutions to mitigate the impacts of climate change in general, and flooding in particular, on the livelihoods of people engaged in agricultural production in the study area and similar regions
Copyright (c) 2026 Lê Thị Giang, Nguyễn Quang Huy, Hồ Đức Nhật, Bàn Ánh Trăng (Tác giả) https://creativecommons.org/licenses/by/4.0
Assessment of Coastal Zone Vulnerability Risks in the Area from Soc Trang to Bac Lieu
The coastal zone of Soc Trang and Bac Lieu provinces is one of the most important socio-economic regions in the Mekong Delta. However, in recent decades, this area has been increasingly affected by climate change, sea level rise, and coastal erosion. This study applies the Coastal Vulnerability Index (CVI) method to assess coastal vulnerability based on the integration of six key factors, including coastal geomorphology, shoreline change rate, coastal slope, sea-level rise, mean tidal range, and wave height. The results reveal a clear spatial variation in coastal vulnerability along the study area. Of the total coastline length of approximately 127.7 km, about 50.82 km is classified as having high and very high vulnerability (39.70%), 39.66 km as moderate vulnerability (30.98%), 20.54 km as low vulnerability (16.05%), and 16.68 km as very low vulnerability (13.27%). Areas with high vulnerability are mainly concentrated along the coast of Vinh Chau Town (Soc Trang Province), Dong Hai District, and Ganh Hao estuary (Bac Lieu Province), where low-lying terrain, gentle coastal slopes, significant shoreline erosion, and strong wave action are dominant. The findings provide a scientific basis for integrated coastal zone management, coastal protection planning, and the development of climate change adaptation strategies in the Soc Trang–Bac Lieu coastal region.
Copyright (c) 2026 Nguyễn Thị Nhân, Lê Đình Nam, Phạm Thị Thu Hằng, Thị Bảo Anh Bùi, Trần Tuấn Dương, Trần Hoàng Yến, Phạm Đức Hùng, Nguyễn Thùy Linh (Tác giả) https://creativecommons.org/licenses/by/4.0
Utilizing Remote Sensing and GIS technologies to monitor land surface temperature and urban heat islands in Ho Chi Minh city
This study evaluates the variation of land surface temperature (LST) and the urban heat island (UHI) at different times, including annual average LST, in Ho Chi Minh City before the merger of administrative units during 2015 - 2024 period, using Landsat 8, 9 and MODIS satellite data. Image processing was conducted on the Google Earth Engine (GEE) platform, integrating cloud filtering methods based on cloud cover and QA_PIXEL band. The results show that LST and UHI are unevenly distributed. Temperature (>45°C) concentrated in airport and industrial areas; (40-45°C) in urban cores; (35-40°C) in agricultural areas (depending on cropping seasons); (30-35°C) in areas with high vegetation cover; (<30°C) in water bodies and mangrove forests. LST calculated from Landsat and MODIS derived at meteorological station locations higher than the observed air temperature about 9.15°C and 4.91°C, respectively. Landsat-derived values are generally higher than values from MODIS, while MODIS-derived LST at Nha Be station is lower than at Tan Son Hoa station. Annual mean LST in urban core and northern Thu Duc City exceeds 36°C, while in suburban areas it ranges from 30-36°C (Landsat) and 30-35°C (MODIS), in Can Gio from 26-34°C (Landsat) and 26-30°C (MODIS). MODIS-derived annual mean LST is 3.4°C lower than Landsat, up to 11°C lower in strong UHI zones. Annual mean LST decreases with land use types, from urban areas (37-40°C) to agricultural land (34-36°C), aquaculture areas (31-35°C), forest (29-33°C), and water bodies (<30°C). UHI intensity from very low to low in areas with high vegetation cover and water bodies, moderate in suburban areas, and high to very high in urban cores and industrial zones. The results indicate that LST and UHI are strongly influenced by urbanization and landuse types, thereby providing a basis for solutions to mitigate the impact of the UHI climate change adaptation in the context of increasing urbanization.
Copyright (c) 2026 Nguyễn Hoàng Trọng Khoa, TS. Phạm Thị Mai Thy, PGS. TS. Võ Lương Hồng Phước (Tác giả) https://creativecommons.org/licenses/by/4.0
Spatial Morphological Evolution and Driving Factors of Major Coral Islands in the Truong Sa Archipelago Based on Multi-Temporal Remote Sensing Data
The spatial morphology of coral islands reflects the combined effects of marine hydrodynamics, reef geomorphology, sediment transport, and human activities, providing essential information for coastal monitoring and sustainable marine management. This study investigates the spatial morphological evolution of five major coral islands in the Truong Sa Archipelago, Vietnam, including Song Tu Tay, Son Ca, Nam Yet, Sinh Ton, and Truong Sa Lon, using multi-temporal satellite imagery acquired from SPOT-2, SPOT-4, Landsat-8, and Sentinel-2 in 2007, 2012, 2017, and 2022. Shorelines were extracted from geometrically corrected satellite images and integrated within a Geographic Information System (GIS), while shoreline dynamics were quantified using the Digital Shoreline Analysis System (DSAS). Four quantitative indicators, namely Length Change Rate (LCR), Area Change Rate (ACR), Centroid Shift Rate (CSR), and End Point Rate (EPR), were employed to characterize spatial morphological changes and shoreline evolution. The results reveal that all investigated islands exhibited an overall expansion trend during the study period, although the magnitude of change varied considerably among islands. Son Ca Island experienced the highest area growth with an ACR of 27.79 m²/year, whereas Song Tu Tay Island recorded the lowest increase of 3.65 m²/year. Shoreline length generally increased, with LCR values ranging from 0.12 to 3.76 m/year, indicating heterogeneous morphological development among islands. CSR analysis revealed multidirectional centroid migration, with Truong Sa Lon Island showing the largest spatial displacement, suggesting asymmetric island development. EPR analysis further identified erosion and accretion hotspots primarily distributed along shorelines directly exposed to seasonal wave and current regimes. The observed spatial morphological evolution is mainly controlled by seasonal wave climate, sediment transport processes, reef geomorphology, and island reclamation activities. The proposed integration of multi-temporal remote sensing, GIS, and DSAS provides an effective framework for long-term monitoring of coral island morphology and offers valuable scientific support for coastal management, geospatial database updating, and sustainable management of the Truong Sa Archipelago.
Copyright (c) 2026 Nguyễn Xuân Tùng, Minh Huấn Nguyễn, Kim Cương Nguyễn, Đặng Thanh Tùng, Lê Thị Hoa Huệ (Tác giả) https://creativecommons.org/licenses/by/4.0
Application of Google Earth Engine and GIS technology to determine coastal changes in Sam Son city, Thanh Hoa province
The purpose of the study is to apply Google Earth Engine (GEE) and GIS technology to determine the coastline changes from 2000 to 2023 in Sam Son city, Thanh Hoa province. The study used Landsat 5 and Landsat 8 image data on Google Earth Engine, and at the same time used GIS technology to determine the coastline changes. The research results show that using GEE shortens the processing time as well as saves storage memory and image data processing speed. Coastal changes in Sam Son city occur differently in different sections along the coast. From 2000 to 2010, the coastal erosion area increased significantly from 288,898m2 to 384,248m2 in the period 2010-2020, then decreased to 208,662m2 in the period 2020-2023. Meanwhile, the accretion area decreased from 569,563m2 to 361,499m2 in the period 2010-2020, then decreased again to 109,367m2 in the period 2020-2023. This research result helps managers make the right decisions in coastal land use management.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Evaluation of gravity field error when replacing gravity anomaly function with discrete values
Replacing the gravity anomaly function in the Stokes and Vening-Meinesz formulas with a set of gravity anomalies measured on the physical surface of the earth or in space leads to inevitable errors in calculating the elevation anomalies and the plumb-line angle components. The purpose of this paper is to show the relationship between the discrete distance of the measurement data and the gravity anomaly error when the gravity field is described by a third-order Markov function by the method of spectral analysis of a function with a limiting frequency, that is, the gravity anomaly function
Integrating GIS and Open-Source Python Libraries for Land Clearance Compensation and Visualization: A Case Study of a Rural Road in Go Xoai Hamlet, Tan Trung Commune, Go Cong City, Tien Giang Province
To support the calculation of compensation and the visualization of information related to land clearance, this study applies open-source Python libraries in combination with Geographic Information System (GIS) technology. The approach was experimentally implemented on a rural road project in Go Xoai Hamlet, Tan Trung Commune, Go Cong City, Tien Giang Province. The results show that the integration of GIS and Python significantly improved data processing and spatial analysis, enabling rapid identification of land acquisition areas, precise compensation calculation, and effective visualization through land use value maps and statistical charts. Among the land types, irrigated rice land (LUC) accounted for the highest proportion, representing 57.3% of the total compensation value in the area—substantially higher than perennial crop land (CLN) and rural residential land (ONT). These findings suggest that the proposed approach not only meets the practical requirements of land use planning and management in rural areas undergoing infrastructure development, but also contributes to the digital transformation and modernization of public land administration.
Application of ArcGIS Pro to Develop an Automated Tool for Monitoring Surface Water Dynamics: A Case Study of the Trà Khúc River in Quảng Ngãi Province (1995–2024)
Monitoring surface water changes over time is essential for understanding river morphology and supporting sustainable water resource management, especially under the impacts of climate change. This study presents the development of an automated tool using ArcGIS Pro to extract and analyze surface water dynamics from Landsat optical imagery. The Green/SWIR band ratio method was applied to identify surface water across four selected years between 1995 and 2024 in the Trà Khúc River, located in Quảng Ngãi City, Vietnam. The results indicate significant morphological changes, particularly in the river’s lower reaches. From 1995 to 2005, the river experienced a notable narrowing trend due to sediment deposition and the formation of sandbars. In contrast, the 2005–2015 period saw surface water expansion, largely attributed to dredging activities that caused bank erosion. This expansion trend continued through 2024, with increased water coverage over low-lying sandbars. The effectiveness of the developed tool demonstrates the potential of remote sensing in monitoring hydrological changes, offering timely insights for river management and climate adaptation strategies
The difference between height anomaly and geoid height in Vietnam according to the global gravity field model SGG-UGM-2
The purpose of this study is to determine the difference between height anomaly and geoid height in Vietnam. The formulas for calculating height anomalies, geoid heights and the differences between them have been studied. The experiment was performed in the Vietnam area with latitude limit: 8ºN - 24ºN; longitude: 102ºE - 114ºE with data from the global Earth Gravity Model SGG-UGM-2 and the Terrain Model ETOPO1. The experimental results show that: In the study area, the geoid height increases gradually in the Northwest - Southeast direction, has a negative value in the Northwest, has a positive value in the Southeast, and has a value smaller than the height anomaly. The difference between the height anomaly and the geoid height varies from 0 to 0.445 m, with decimeter-scale values in mountainous areas, centimeter-scale values and smaller in plain areas, and zero over the East Sea. This study provides an understanding of the differences between geoid and quasigeoid in the territory and territorial waters of Vietnam.
Application Google Earth Engine for monitoring green space changes in Thu Duc city using Sentinel-2 images during 2019–2024
Urbanization has increased the extent of impervious surfaces, indirectly placing pressure on the urban green ecosystem in Thu Duc City. This study utilizes Sentinel-2 satellite imagery from 2019 and 2024, combined with the Random Forest algorithm on the Google Earth Engine platform, to generate green space maps of Thu Duc City and analyze changes during the study period. The results indicate a declining trend in green space by 2024 compared to 2019, particularly in riverside areas and the eastern part of the city. The integration of Machine Learning in satellite image classification has enhanced accuracy, automated the processing of large datasets, and effectively supported green space monitoring amid the growing challenges of urbanization and climate change.
Application of Remote Sensing and GIS Technologies in Developing a Road Transportation Database: A Case Study in Son La Province, Vietnam
The development of a road transportation database plays a crucial role in infrastructure management and planning, especially in mountainous areas with complex terrain such as Son La province. This study integrates remote sensing imagery (Sentinel-2, Landsat-8, and high-resolution satellite images) with GIS technology to build a comprehensive transportation database for the entire study area. Through image preprocessing, supervised classification, and spatial information extraction, the GIS database was constructed with key data layers: road networks, bridges and tunnels, and major traffic nodes. Validation against field survey data shows an overall accuracy of 89%, with the road network layer reaching 91.3%. The results demonstrate that, the GIS-based transportation database can provide detailed information on road infrastructure, supporting evaluation and planning efforts in mountainous regions
Assessment of modern crustal motion in Viet Nam according to regional kinematic models
This paper presents an assessment of modern crustal motion in Vietnam through the application of regional kinematic models. The Sundaland kinematic models of Simons and Michel are introduced, along with the theoretical basis of kinematic modeling, particularly the use of the Euler pole to describe the rotational motion of tectonic plates. The absolute velocities of Vietnam's territory according to the Michel, Simons, and Nuvel-1A models are calculated and compared. The differences between the models and actual measurement results are analyzed, thereby providing comments on the suitability level of each model for the current crustal motion of Vietnam's territory.
Copyright (c) 2025 Thị Hạnh Tống (Tác giả) https://creativecommons.org/licenses/by/4.0
Create a 360-degree digital map with a website introducing innovative greenhouse products for growing safe flowers, fruits and vegetables associated with eco-tourism and experiences in Thai Binh province - Vietnam
In this article, we describe the establishment of a 360-degree map combined with a product promotion website, which is the result of a scientific topic "Research on building a model of improved greenhouses and net houses and growing safe flowers, fruits and vegetables associated with eco-tourism and experiences in Thai Binh province. Code: TB-CT/NN07/24-25". The objective of the topic is to build a model of 500 m2 of improved greenhouses and net houses that are windproof and movable (manufacturing a structural complex including a frame, connecting steel hooks, and a plastic film covering to form a closed house) in Trung An commune, Vu Thu district, Thai Binh province; growing safe flowers, fruits and vegetables, applying technology using microbial fertilizers, automatic irrigation technology, monitoring weather and soil environment using IoT sensors, associated with eco-tourism, experience and product promotion
The method of tranfomation of seabed depth data from the international reference system to the national reference system
The purpose of this study is to propose a method for tranformation of seabed depth data from the international reference system to the Vietnamese national reference system. Global seafloor bathymetric models have the latitude (B), longitude (L) of the points in the WGS-84 and the depth to the international mean dynamic topography (DQT). For tranformation of these coordinates and depths to the VN2000 and the national vertical system, it is necessary to determine the ellipsoid height (H) of the points in the WGS-84 and the difference between the mean dynamic topography of Vietnam and the international mean dynamic topography. These ellipsoid heights are determined based on geoid height from the global Earth Gravity Model (EIGEN6C4); the international mean dynamic topography (DTU15MDT) and the international depth of the points. The difference between the national and international mean dynamic topography is determined based on data at 31 tidal gauge stations in Vietnam. After obtaining all coordinate components in the WGS84, the coordinates are tranformated using the Bursa-Wolf formula with 7 coordinate tranformation parameters between WGS-84 and VN2000. The experiments were calculated for the GEBCO2020 bathymetric model over the central East Sea.
Land subsidence in Ho Chi Minh city risks and challenges for sustainable development of Ho Chi Minh city
Land subsidence is a common geological hazard in the world and seriously impacts sustainable urban development, especially in important cities such as Ho Chi Minh City. The article presents an overview of the authors’ results of land subsidence research in Ho Chi Minh City from 2006 to 2020 using InSAR analysis techniques. The research results show that the current average subsidence rate across the city is about 2-5cm per year. Particularly in areas with weak geology and concentrated areas such as commercial projects, the subsidence rate can be up to 7 to 8cm per year. Thereby, the group of authors proposes that Ho Chi Minh City needs to build a land subsidence simulation and forecasting information system to support decision-making to help the government have quick, appropriate, and accurate solutions, improve management capacity, contribute to effective urban management, and adapt to climate change and sustainable development.
Assessing Land Cover Changes and Surface Temperature Impact of Golf Course Development in Hanoi Using Remote Sensing
The rapid pace of urbanization has led to significant changes in land use, directly impacting local climatic conditions, particularly land surface temperature (LST). Among the expanding forms of land use are golf courses, which require large areas and often replace natural vegetation cover. This shift disrupts the surface energy balance and alters the regional thermal environment. This study employs remote sensing data to analyze the effects of land use and land cover (LULC) changes on LST in the context of golf course expansion in Hanoi from 1989 to 2023. The specific objectives of this research are: (i) to assess changes in LULC and the expansion of golf courses; (ii) to calculate LST; and (iii) to analyze the relationship between LULC and LST in the years 1989, 1999, 2009, 2020, and 2023. The results show a continuous decline in vegetative cover over time, while built-up areas and bare land have significantly increased from 1989 to 2023. The highest LST values were recorded in the city center, where high building density and limited greenery are observed. Notably, the conversion of land into golf courses has also contributed to LST increases, depending on the previous land cover type. Statistical analysis between areas inside and outside golf courses reveals clear differences in LST, indicating the thermal impact of golf course development. These findings highlight the importance of integrating thermal considerations into urban planning, aiming for sustainable development amid rapid urbanization
Artificial intelligence (AI) technology in the real estate sector.
Currently, everyone, especially the media, is talking about Artificial Intelligence (AI), and its impact on the real estate sector is a major question worth discussing. An impressive figure, according to the Global Market Report on AI in Real Estate 2025, shows that AI has grown exponentially in recent years. It is projected to increase from $222.65 billion in 2024 to $303.06 billion in 2025, with a compound annual growth rate (CAGR) of 36.1%. This impressive growth is likely due to the widespread development and application of market analysis and forecasting, chatbots and virtual assistants, real estate valuation and prediction, smart property search, and automation in property management worldwide.
Developing hourly rainfall maps from open meteorological data sources to analyze the spatial extent and temporal evolution of flash flood events
Research on flash floods and related geological hazards has often focused on spatial hazard assessment. However, studies on the temporal evolution of such disasters remain limited, primarily due to the lack of hourly rainfall data. Conventional rain gauge stations fail to capture the full spatial extent of disaster impacts and are typically unevenly distributed, especially in mountainous regions. This paper proposes a novel approach using the R programming language to retrieve, process, and calibrate open-access meteorological datasets in order to generate historical hourly rainfall maps. The proposed method is tested on three extreme rainfall events in Northern mountainous Vietnam: (1) the flash flood on August 1, 2025 in Điện Biên and Sơn La; (2) the flash flood on May 18, 2025 in Tuyên Quang and Bắc Kạn; and (3) the flash flood on August 5, 2023 in Yên Bái and Lai Châu. Results indicate that both the intensity and spatial distribution of hourly rainfall change rapidly. Peak hourly rainfall intensities often exceed 60 mm, and the locations experiencing such rainfall levels are typically those suffering the most severe damage. Notably, the spatial extent of flash flood impacts generally overlaps with areas where 24-hour accumulated rainfall exceeds 200 mm. The workflow for generating hourly rainfall evolution maps provides a reliable basis for delineating disaster-affected areas in alignment with actual damage observations. It also serves as a foundation for developing data-driven tools to establish quantitative thresholds for flash flood early warning systems
Automated Forest Loss Detection Using Machine Learning Algorithms and Sentinel-2 Imagery on Google Earth Engine: A Study in Ca Mau Province, Vietnam
This study presents the development and testing of machine learning models for automated forest loss detection in Ca Mau Province. The entire workflow, from preprocessing Sentinel-2 satellite imagery (Surface Reflectance - L2A) to model training and evaluation, was implemented on the Google Earth Engine (GEE) cloud computing platform. The study applied cloud filtering techniques to obtain images with cloud cover below 40%, while extracting 10 spectral bands and 8 vegetation indices, with delta indices (ΔNDVI, ΔEVI, ΔNBR, ΔSAVI) serving for model training and evaluation. Three machine learning algorithms were employed including Random Forest, Support Vector Machine (SVM), and Gradient Boosting Trees (GBT). The study utilized 2,500 sample points collected with a balanced ratio of 50% forest loss and 50% non-forest loss, and after extracting features for these sample points, the data was divided into 80% for training (train and validation) and 20% for testing, maintaining the 50:50 ratio in each subset. Evaluation results showed that the Gradient Boosting Trees model achieved the highest performance with an overall accuracy of 85.40%, recall of 72.00%, precision of 98.36%, and F1-score of 83.14%. With considerable recall and very high precision, the system demonstrates high reliability when alerting forest loss while detecting the majority of actual forest loss cases. The study successfully developed an automated forest loss detection tool on GEE, providing effective information to support local forest management efforts. However, further research on integrating Sentinel-1 radar data is needed to overcome cloud cover limitations during the rainy season and further enhance detection capabilities.
Application of Persistent Scatterer Interferometric SAR (PSInSAR) Method for Processing Sentinel-1 Images to Detect Surface Deformation in Muong Lay Town, Dien Bien Province
Interferometry (InSAR) is a technique to measure ground subsidence and has ability to building ground subsidence map on a large spatial scalewith high accuracy from space. TThis study presents the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) method to determine the surface deformation velocity of the terrain in Muong Lay town – Dien Bien province, based on Sentinel-1 ascending orbit data with 50 images acquired from 2018 to 2022. The results reveal surface deformation in the Muong Lay area with 20.493 PS points, ranging from -8.1 mm/year to 7.4 mm/year. The distribution of PS values across residential areas along the Nam Lay stream within Muong Lay town indicates zones experiencing downward subsidence (negative values) as well as uplift (positive values). These findings provide a basis for identifying the causes of terrain surface changes and for proposing preventive measures to mitigate damages in Muong Lay town, Dien Bien province, in future in-depth studies.
Urban tree extraction from UAV Imagery using deep learning techniques for urban management applications
This paper proposes a method that leverages deep learning techniques to extract urban trees from high-resolution UAV imagery. UAVs play a crucial role in capturing both nadir and oblique images with high geometric detail, facilitating effective modeling of the three-dimensional spatial characteristics of urban vegetation. The core of the proposed approach lies in its ability to automatically identify and segment trees based on learned visual features, particularly the shape and size of tree canopies, which often exhibit irregular boundaries and natural spatial distribution, distinct from man-made objects. The resulting output is a spatial dataset that accurately reflects the location and geometric structure of trees, effectively supporting urban green space management, analysis, and planning. The method demonstrates high feasibility, automation capability, and scalability, contributing to the development of intelligent and sustainable urban management systems.
GIS-Based Visualization of Land Compensation Information for a Rural Road Project in Go Xoai Hamlet, Son Qui Ward, Dong Thap Province
land management and supports the sustainable development of land resources. This is especially relevant in the context of the current digital transformation trend. Compensation information is visualized on a compensation value map by determining the spatial extent of land acquisition and calculating compensation amounts through the development of an automated tool using the Model Builder in ArcGIS Pro software. At the same time, a WebGIS system was developed to provide compensation information for a rural road in Go Xoai Hamlet, Son Qui Ward, Dong Thap Province. This system uses open-source technology, including GeoServer combined with the PostgreSQL database management system, to effectively store and manage geospatial data. Experimental results show that compensation information is displayed visually on both maps and the WebGIS platform, allowing users to query existing data such as: compensation value, landowner name, land type, compensation area, and other related details. Accordingly, the WebGIS system demonstrates feasibility and transparency in information disclosure, contributing to effective support for both local residents and government agencies in the land clearance and compensation process.
Completion And Standardization Of Measurement Standards Used In The Field Of Surveying And Maps
Measurement standards in the field of Surveying and Mapping are a form of technical infrastructure for surveying and mapping, playing a core role in the verification and calibration of measuring instruments. In recent years, a number of measurement standards for verification and calibration of measuring instruments have been established, such as: electronic total stations, leveling instruments, satellite positioning measuring instruments, photoelectric distance measuring instruments, theodolite measuring instruments, etc. In addition, some group 2 measuring instruments continue to need investment in establishing technical infrastructure for measurement standards in accordance with regulations, as well as the need to form measurement standards for group 1 measuring instruments when there is a request for verification and calibration. This article summarizes the situation of promulgation, publication and management of existing measurement standards, proposes measurement standards for verification and calibration of group 2 measuring instruments that are still lacking, and proposes solutions to standardize measurement standards used in verification and calibration of measuring instruments used in the field of Surveying and Mapping
Integration of UAV technology and machine learning algorithms for land cover classification using ArcGIS Pro software
The integration of Unmanned Aerial Vehicle (UAV) technology with machine learning algorithms is opening up an effective approach for processing and analyzing spatial data. This study performs land cover classification by applying two popular machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), using ArcGIS Pro software. High-resolution orthophotos (2.57 cm/pixel) obtained from UAV imagery over District 4, Ho Chi Minh City, were used for the classification process. Experimental results show that the SVM algorithm performs well on some challenging classes such as vehicles and boats, achieving higher user accuracy than Random Forest (RF). However, when evaluated overall based on Overall Accuracy (OA) and the Kappa coefficient, RF outperforms SVM, with OA of 86.6% and Kappa of 0.83, compared to SVM’s 77.4% and 0.71, respectively. This indicates that RF has high accuracy and more suitable for constructing land cover maps from UAV imagery. These results confirm the feasibility and effectiveness of applying UAV images combined with machine learning algorithms in spatial analysis and monitoring. This provides a crucial basis for resource management, urban planning, and environmental protection in the context of rapid urbanization in major cities.
Research on the urban heat island phenomenon in Hanoi City based on Landsat-9 satellite imagery data.
The phenomenon of Urban Heat Island (UHI) is a serious environmental issue affecting human health, air quality, and the living conditions of urban residents. This study uses Landsat-9 satellite imagery data to analyze the UHI phenomenon in the Hanoi area during a point in the summer of 2024. The Land Surface Temperature (LST) data from Landsat-9 were processed to determine the extent and magnitude of the urban heat island, while also investigating the relationship between factors such as building density, greenery, and land use types on the formation of UHI. The study indicated that the largest surface temperature difference between areas with abundant greenery and densely populated, built-up areas is 7.2°C, while the difference with other areas is 2.7°C. The research findings will provide deep insights into the impact of urbanization on the urban climate environment and propose solutions to mitigate the UHI effect in Hanoi.