Geodesy and Cartography publishes original research in the fields of geodesy, cartography, remote sensing, geoinformation systems, geoscience, land management and environmental sciences. More information ...
Authors who publish with this journal agree to the following terms that this article contains no violation of any existing copyright or other third party right or any material of a libelous, confidential, or otherwise unlawful nature, and that I will indemnify and keep indemnified the Editor and THE PUBLISHER against all claims and expenses (including legal costs and expenses) arising from any breach of this warranty and the other warranties on my behalf in this agreement; that I have obtained permission for and acknowledged the source of any illustrations, diagrams or other material included in the article of which I am not the copyright owner. on behalf of any co-authors, I agree to this work being published in Geodesy and Cartography as Open Access, and licenced under a Creative Commons Licence, 4.0 https://creativecommons.org/licenses/by/4.0/legalcode. This licence allows for the fullest distribution and re-use of the work for the benefit of scholarly information. For authors that are not copyright owners in the work (for example government employees), please contact VILNIUS TECH to make alternative agreements.
Predicting land use/land cover changes using CA-ANN model in the Gia Lam District, Hanoi City
Gia Lam is a district in the eastern part of Hanoi, Vietnam, undergoing rapid development and urbanization. Predicting land use/land cover changes is crucial for supporting policymakers in designing effective local development strategies. This study aims to forecast land use/land cover changes in Gia Lam District through 2028. Sentinel-2 optical satellite imagery serves as the primary data source, with the classification of five land use/land cover types at different times performed using the Random Forest (RF) algorithm, and forecast results generated using a Cellular Automata (CA) model combined with an Artificial Neural Network (ANN) model. The results indicate a significant expansion of built-up areas, projected to cover 68.92% of the natural area by 2028. On the contrary, the area under annual vegetation is expected to decline to just 7.71%. These findings provide valuable insights for researchers and land use planners.
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
Estimating Euler pole parameters for Sundaland block in IGB14 using robust filtration approach
Enhancing the accuracy of the Euler pole parameters for the Sundaland block is essential, given the revised reference frame. The selection of GNSS stations should be obtained before the major event of the 2004 Mw9.2 Sumatra-Andaman earthquake. Initially, 34 GNSS stations in Malaysia, Thailand, Singapore, Indonesia, and Philippines (1999–2004) were adopted under the International Terrestrial Reference Frame 2014 (ITRF2014), specifically IGB14. This study employed precise point positioning (PPP) to obtain accurate coordinates via scientific GipsyX version 1.7. The coordinate time series derived from the X-file mapping approach was utilised to estimate a linear trend motion. The stations’ velocities were derived using a 3-dimensional (3D) linear regression approach. To achieve an optimal Euler pole parameter, the outlier filtration processes were employed using statistical tests, limit-based test, and combined statistical and limit-based tests. The combined Baarda and limit-based method was clarified as the robust filtration approach to derive the optimal Euler pole parameters for the Sundaland block at the latitude of 66.77° and longitude of –100.34°, with a rotation rate of 0.302±0.007°/Myr. A further study with densified data covering the maximum Sundaland region is expected to derive the Euler pole parameters more accurately.
Monitoring of landslide-prone areas using radar data: a case study of the Right Loess Plateau of the Dnipro River (Ukraine)
The right bank of the Kaniv Reservoir, which belongs to the Right Loess Plateau of the Dnipro River in Ukraine, is highly susceptible to landslides due to the geological features combined with climate change and human activities. Most landslides related complex morphostructure with central, characteristic elements was formed: a loess plateau with unstable slopes; the right-bank valley of the Dnipro River with erosive and accumulative terraces; ridge-beam, erosion and sliding. Human activities, namely clay mining, construction, and the expansion of agricultural lands induce landslides by altering slope stability. The current research aimed to assess landslide hazards by applying the DInSAR methodology, including the influence of slope, lithology, precipitation, and temperature regime. Optical sensors have known limitations due to their inability to capture Earth’s surface. The alternatives are radar sensors capable of seeing through the clouds and working independently of daylight. Integrating DInSAR technique data with classical geomorphological research helped define the kinematics and evolution of the landslides and establish their triggering factors in the Right Loess Plateau of the Dnipro River. Using measures in a seasonal change of temperature fluctuation and precipitation quantity can detect the outcrop, not overgrown vegetation areas, illustrating that radar data can detect landslide-prone areas.
Modelling the local geoid of East Kalimantan from combination of airborne and terrestrial gravity data using the KTH method
This research aims to determine the gravimetric geoid using the KTH method for East Kalimantan, where Indonesia’s new capital city is located. The geoid of East Kalimantan was calculated from a combination of airborne gravity data, terrestrial gravity data, DTU17 for the sea area, anomalies of EGM2008 degree 2190, SRTM15+, and GGM from EGM2008 degree 360. The geoid modelling is performed using LSMSSOFT software with a grid interval of 0.01˚. The evaluation of capsize variations includes 14 variations: 0.1˚, 0.2˚, 0.3˚, 0.4˚, 0.5˚, 0.6˚, 0.7˚, 0.8˚, 0.9˚, 1˚, 1.2˚, 1.3˚, 1.5˚, and 2˚. The KTH geoid was evaluated using 264 GNSS-Levelling data points. The best accuracy was obtained at a capsize of 0.6˚ with a standard deviation value of 0.0532 m. The accuracy of the geoid model after shifting to the tidal benchmark (BM) is 0.0526 m. A comparison with the recent national geoid model of INAGEOID2020 v.2 obtained using the Remove-Restore method, which has a standard deviation of 0.0588 m, shows that the accuracy of the KTH method geoid model is higher, with a precision improvement of 0.0062 m.
Prediction of terrestrial water storage changes by using GRACE data over Nile river basin
This research involved training two deep learning prediction models: Long Short-Term Memory (LSTM) and Dipper Throated Optimizations Fitness Grey Wolf-LSTM (DTOFGW-LSTM), utilizing data obtained from remote sensing to reconstruct and predict the Terrestrial Water Storage Changes (TWSC) over Nile River Basin (NRB). We evaluated factors including Terrestrial Water Storage Changes (TWSC) and Groundwater Storage Changes (GWSC), identified through the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow-On (GRACE-FO), alongside precipitation data collected by the Global Precipitation Climate Change Program (GPCP) to analyze the patterns of change within the research area. We utilized the LSTM and DTOFGW-LSTM algorithms to rebuild the TWSC and GWSC from 2018 to 2024. We utilized the precise model to forecast the GRACE gap from 2017 to 2018 and the TWSC from 2024 to 2030. The findings demonstrated the superiority of the suggested model (DTOFGW-LSTM) with a root mean square error (RMSE) of 0.51, a coefficient of determination (R²) of 0.99, and a mean absolute percentage error (MAPE) of 0.21.
Integration of aerial photo and LiDAR data for determining the position and height of oil palm trees using object-based analysis and canopy height model algorithm
The monitoring of oil palm trees using various technologies, methods, and software is conducted to replace the traditional techniques that are less effective. In this study, an analysis was conducted on the automatic detection results of oil palm trees to determine the estimated height of the trees. The trees were automatically extracted and calculated using eCognition Developer and eCognition Oil Palm Application (OPA) with the Object-Based Image Analysis (OBIA) algorithm on three sample areas: homogeneous, semi- homogeneous, and heterogeneous. The performance test of the two software on the three samples showed that the detection accuracy reached more than 80%. The automatic detection results were used to calculate the tree height using the Canopy Height Model (CHM). The Root Mean Square error (RMSe) was calculated for all centroid samples to evaluate the accuracy of the tree position detection and as a basis for determin- ing the height. The RMSe position result of eCognition OPA was lower than that of eCognition Developer. The RMS values for the homogeneous; semi-homogeneous; and heterogeneous areas were 0.8149; 0.7772; and 0.02118 for eCognition OPA, respectively, which are lower than the values of 0.7718; 0.9044; and 1.0517 for eCognition Developer, this indicates better estimated tree height results.
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
A comparison of transformation models between geodetic reference frames: case study in Illizi region (Algeria)
The Illizi region is an important petroleum zone in Algeria, where various seismic surveys have been conducted. The merging of adjacent surveys is not possible due to incompatible data linked to the geodetic networks. In this study, the transformation of coordinates, from the global system (WGS84) to the local system based on the Clarke 1880 A spheroid, is carried out based on a set of 57 control points well distributed over the study area with coordinates determined in both the global and local systems. Five approaches were used to determine the transformation parameters between the two systems, namely: Geocentric Translation Model, Bursa-Wolf Transformation, Molodensky-Badekas Transformation, Abridged Molodensky transformation and Multiple Regression Equations (MRE). From statistics on the determined parameters and considering its advantage of reversibility, the Bursa-Wolf Transformation Model is the most suitable model to be used to transform coordinates between the two systems in the study area. Small amount of residuals in transformed coordinates using this model indicates acceptable Bursa-Wolf parameter estimation. An improvement in the results was observed after removing of the outliers control points detected using a statistical test. For the validation of the estimated parameters, external control points were used. The results show acceptable RMS in transformed coordinates of these points.
Development of a visual tool for natural disaster risk assessment for the communities of the Republic of Georgia
The National Risk Index, developed by us, provides a relative measurement of national-level natural hazard risk across communities. The risk is defined as the potential for adverse impact from a natural hazard. Local and national authorities require information on the frequency and impact of hazardous events that have occurred or may occur in the future as a basis for risk-informed planning. Generally, most local authorities do not have the technical expertise to develop tools for this, and therefore, it is important to provide national-scale platforms for disaster risk assessment. This paper presents a national multi-hazard risk profiling platform for administrative units within Georgia, located in the Caucasus. The risk calculation is based on a method that uses historical disasters and their frequencies of hazardous phenomena such as landslides, floods, and earthquakes. We calculate the risk for the communities of Georgia. Most methods require detailed data from different sources to calculate risk, and data availability and quality are one of the main challenges in this type of work. This problem is particularly evident in developing countries, where there are limited resources to collect and analyze data quality. Our method is original because it allows for risk calculation, risk assessment, and online visualization of data based only on disasters, recorded in the country’s territory. The interactive map can be used as a visual tool for disaster risk assessment by the parties concerned as well as decision-makers.
WebGIS and land planning data management: a study of Hanoi University of Civil Engineering’s Campus 2
The use of WebGIS in urban land planning and management has become a global trend. A Web-GIS system was developed specifically for planning management at HUCE’s Campus 2. While not yet fully complete, the findings indicate that this system has provided substantial support to stakeholders, including local authorities, planners, and the broader community. Through efficient data management and analysis, the WebGIS system has facilitated timely monitoring and adjustments during the construction process, while also enabling comprehensive modifications to land planning. Additionally, its field interaction function gathers valuable information for detecting damages requiring repair and maintenance, along with updates on the status of areas where land use rights have not yet been transferred. Despite certain limitations, such as challenges in integrating 3D model data and enhancing public consultation methods, the incorporation of these research findings offers significant potential to improve the effectiveness of urban land planning and management in the future.
Developing standards for surveying, measuring, and mapping underground objects using the GPR method for Vietnam
In the context of rapid urbanization, detailed investigation of underground objects in the shallow layer is very important for safe and efficient urban planning, construction, and operation. With many outstanding advantages, Ground Penetrating Radar (GPR) technology has been increasingly applied worldwide in shallow geophysical surveys. However, in Vietnam, applying GPR for surveying and measuring objects in the above spatial range is only in its early stages, and there are no official standards or regulations. This paper uses the GPR method to develop technical standards for investigating and mapping underground objects within 0–6 m depth. The research methodology includes synthesis analysis of international standards, expert consultation, and field experiments. The main results of the study include: (1) Proposing a 9-step detailed GPR survey process; (2) Specific technical regulations for GPR survey work; (3) Classification of 4 quality levels of products (QLB1-QLB4) with specific requirements; (4) Guidelines for representing and presenting maps of underground objects. This standard will be the first document to standardize the application of GPR in measuring and acquiring information on underground objects in Vietnam, contributing to promoting the widespread application of GPR technology in practice.
Accuracy of the corner coordinates of a building surveyed with nine points in RTK GNSS method
In this study, measurements were made to compute the coordinates of a building’s corner with only one steel tape measure and two GNSS receivers in the field. First, three circle arcs were drawn in the field using a steel tape measure at a distance of 1 meter, 3 meters and 5 meters from the corner of the building, and three points were marked spontaneously on each of these three arcs. A total of nine points were obtained and the coordinates of these nine points were determined by using RTK GNSS technique. The coordinates of the corner of the building (K1) were computed by using the coordinates of nine points on 1 meter, 3 meter and 5 meter circle arcs. In this calculation process, the coordinates of the building corner are obtained by taking the average of the values by using nine points on the three circle arcs. When the building corner coordinates obtained with the total station and the coordinates obtained by marking nine points on the arcs were compared, the maximum and minimum differences were found in the range of 5–6 centimetre. Considering that it is advantageous in terms of time, this new method takes completely about 85 minutes for all of the points.
Comparison of different Mercator projection systems for the topographical survey of the Buchanan-Tokadeh (Yekepa) railway, Liberia, Africa
For this project of the Buchanan-Tokadeh (Yekepa) railway located in the Republic of Liberia, Africa, it is about carrying out comparative analysis of precisions between two projection systems, the Local Transverse Mercator (LTM), divided into 15 zones; and Hotine Oblique Mercator (HOM); as well as the projection heights of their Local Topographic Planes (LTP) both in orthometric heights to determine the scale factor, as well as ellipsoidal ones to extend the semi-axes of the ellipsoid of the WGS84 datum to give an scale factor equal to 1 of these systems, along 245 km of route, taking as reference tolerances precisions that must be within 1/40,000 or 2.5 cms per kilometer, and also that the difference in height does not exceed 150 meters. Getting results by performing survey traverse polygon made with a total station, in a way that the deformations using an LTM divided into 15 zones and the HOM expanding the semi-axes of the reference ellipsoid of the Datum WGS84 complying with the aforementioned standards.
Geoinformation wildfire mapping of Ukraine: analyzing FIRMS data for effective fire management
Sustainable growth in Earth remote sensing data necessitates the advancement of interpretation methods to address a wide array of economic challenges. This research paper proposes the development of a methodology to automate the assessment of fire-affected areas using GIS software such as ArcGIS and QGIS. Data on fire localization from NASA/NOAA Suomi NPP and NOAA-20 and MODIS (M6) satellites, sourced from NASA’s Fire Information for Resource Management System (FIRMS), as well as annual land use/land cover data from ESA WorldCover 2021, are utilized for this study. The series of maps obtained from the aggregation and generalization of fire distribution data for individual years across the administrative regions of Ukraine from 2021 to 2023 allows for the assessment of fire density, their correlation with different land cover types, and spatio-temporal changes. Graphs showing the distribution of fires based on land cover types in Ukraine for 2021–2023 have been generated. Additionally, the dynamics of fire occurrences in 2023 compared to 2022 are presented.
Using RS and AI GIS in land management and urban planning in Mosul, Iraq for sustainable development
The importance of Remote Sensing (RS) and Geographic Information Systems (GIS) comes from the fact that they are means that have been proven effective in supporting and developing the decision-making process in the field of urban control. The city of Mosul in Iraq has been subjected to a tremendous change in the dynamics of land use due to the wars. This study aims to use RS techniques and Artificial Intelligence Geographic Information Systems (AI GIS), as the study relies on Sentinel-2 images and online data sources to evaluate the changes that occurred in the natural environment by comparing the years during and after the destruction. Four classification methods were used in this study; Support Vector Machine (SVM) learning, Scene Classification technique (SC), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). High classifications accuracies were obtained ranging between (92%–97%). The residential area had decreased, followed by vegetation, which was converted to bare land by 64% in the year 2017 due to the war effect. In 2023 some recovery of settlement and increasing in other sectors was observed. The approach highlights the capability of spatial technologies and the role of RS and GIS in sustainable development.
Geodetic monitoring of landslide processes in coastal territories
The study is devoted to the methodology and features of geodetic monitoring of territories to provide analytical and predictive information regarding predicting the danger of coastal strip landslide processes. The causes, consequences, and risks of the development of landslide processes are analysed. The purpose of the study was to justify the need and to consider the methodology of stable high-precision observations of the coastal fortification structure, to identify the zone of active landslides, and to provide suggestions regarding the features and conditions of further observations. To conduct direct observations, it was necessary to lay benchmarks on the territory of the research object. The method using GNSS technologies was chosen for determining the coordinates of benchmarks at the research object. The advantages of using GNSS equipment for monitoring slope deformations compared to traditional geodetic methods have been determined. To achieve the maximum accuracy of measurements, it was decided to install a constantly operating satellite station of the “System.NET” network directly in the work area. During the study period, 33 geodetic soil benchmarks and 53 benchmarks were installed on the coastal fortification structure in the coastal zone. In combination with the methods of geodetic observations, such a density and distribution of benchmarks both in the soil and on the coastal fortification structure made it possible to obtain accurate data on the dynamics of landslide processes and to prevent the negative consequences of the landslide.
Determination of location of historical and cultural heritage objects using photogrammetric and geophysical methods
Two central problems related to the study of historical fortification systems are apparent. First, there are high labour costs for the excavation of defensive structures. Therefore, studying each line of defence along its entire length by traditional archaeological methods is practically impossible. That’s probably why special studies of the fortification system are the exception rather than the rule, and information about defensive structures is given in single sections. The second problem is related to the fact that some lines of fortifications were destroyed in ancient times or were practically destroyed due to later economic activity. The specified circumstances determine the need to use photogrammetric and geophysical methods for the preliminary search of the infrastructure of defence structures. This work provides an example of deciding mass graves during the Second World War using the interpretative properties of German aerial photographs of 1944, archival cartographic data on the territory of the Lviv Citadel, where the Nazi concentration camp for prisoners of war Stalag-328 was located during the war. After predetermining the places of mass graves by photogrammetric methods, geophysical surveys were carried out with the help of ground-penetrating radar (GPR) for the exact localisation of the graves. 13 locations of mass burials and mass executions and burning of bodies of prisoners of war were discovered.
Investigation of the Africa splitting into two as new ocean forms
Scientists believe that Ethiopian tectonic plate changes indicate that Africa is breaking apart, opening the path for the emergence of the planet’s sixth ocean. Ever since the East African Rift, a 35-mile-long fissure in Ethiopia’s desert, appeared in 2005, there has been a continuous movement between the tectonic plates. The second-largest continent in the world will virtually split in two due to the separation of the Somalian tectonic plate from the larger Nubian tectonic plate. This event has not been seen in hundreds of millions of years, when South America and Africa were separated into separate continents. The Somali and the Nubian tectonic plates are slowly disintegrating from each other, while the Arabian plate continues to pull away. The continental rift will happen along the east African Rift Valley, a geologically active region whose formation millions of years ago is similar to that of the tectonic movements that happen at the bottom of oceans. This means landlocked countries such as Rwanda, Uganda, Burundi, the Democratic Republic of Congo, Malawi, and Zambia would inadvertently find themselves with a coastline, and thus, build harbours that connect them to the rest of the world directly. Kenya, Tanzania, and Ethiopia would have two territories each. Though that will take between five to 10 million years, the continent will eventually split into two sub-continents, creating a new ocean basin between them.
Harmonization of marine gravity data in Eastern Baltic
Marine gravity datasets covering areas of state scale typically are made up of data that has been surveyed over multiple campaigns. These campaigns often take place many years apart, are of varying resolution and accuracy. This is due to ship-borne campaigns being much more expensive and time consuming than the ones on land; because of this, there is added value in validating and possibly correcting older data sets. Over the course of recent BalMarGrav marine gravity project, dense, high quality gravity data was obtained covering most of the Latvian exclusive economic zone. The new data have been compared to campaigns, both as means of new data validation, and to check for possible biases among older data sets. Purpose of this research is to further the effort, to provide wider coverage of old marine gravity points, to test automated gravity point digitization, and to perform inter-campaign comparisons, using new, filtered and more precise data. Data recovered during this research covers the previous data gaps between sets used in previous research. Using a more complete data coverage can improve new campaign data set validation and provide insights on inter-campaign biases within older data. Recovered data cover shallow coastal areas, where gravity mapping was not done over BalMarGrav project. Thus, by applying correctional values geoid errors can be minimized in the problematic transition zone between terrestrial and marine data. Survey reports containing 20th century marine gravity point data were digitized, using optical character recognition. Gravity point values were transferred to sea surface and transformed to modern reference frames. Both modern and historic marine gravity data were filtered for gross errors and bias tracks. Data set robustness was checked, using leave-one-out cross validation. After processing, a comparison was made between old and new data. Results present re-processed and filtered marine gravity datasets, and their comparison statistics. Comparison statistics before and after filtering reveal the increased accuracy and precision of filtered data. Mean comparison values reveal inter-campaign biases and provide correction values, which can increase data accuracy for use as input in future research and surface modelling.
Drought safety levels assessment in Uzbekistan part of the Khorezm oasis by geospatial methods
The issue of drought has emerged as a significant challenge in the Khorezm oasis over recent decades. Furthermore, the construction of the Kushtepa canal in Afghanistan is expected to exacerbate the impact of drought in the region. It is of the utmost importance to evaluate the resilience of the oasis to drought in order to ensure effective planning and mitigation strategies. This study employed geospatial data, including the normalized difference of vegetation index (NDVI), land surface temperature (LST), normalized difference of moisture index (NDMI), soil brightness, groundwater table, digital elevation model (DEM), and distance to Amudarya river, derived from Landsat 5 TM, and Landsat 8 OLI/TIRS data (2000–2023). A weighted overlay analysis was employed to identify the most influential factors, which were found to be distance from the river, canal density, soil brightness, LST, and groundwater table. The findings indicate that 3746 km2 of the oasis is safe, while 4644.32 km2, 5563.77 km2, 5486.17 km2, 7832.64 km2 are classified as dangerous, mid dangerous, high dangerous, and extreme dangerous, respectively. It is recommended that agricultural use be prioritised in areas deemed safe, that construction be restricted, and that population migration from high-risk regions to safer areas be facilitated.
Estimation of land surface temperature based on GIS and remote sensing data in Dahuk city
Land Surface Temperature (LST) is a crucial variable across various domains, including studies on the global ramifications of climate change, urban land use and cover, and geo- and biophysical modelling. Satellite data from Landsat 8, specifically the NIR channel, was utilized to generate LST maps, NDVI, and LU/LC for Dahuk City. The correctness of LULC maps was verified by ground observation locations. The results indicate that the LST ranged from 4 to 14 degrees Celsius in 2013 and from 10 to 20 degrees Celsius in 2023. The highest temperatures, ranging from 14 to 20 degrees Celsius, occur in urban areas, whilst the lowest temperatures, recorded in 2013 and 2023, are in forests and aquatic environments, measuring 4 and 10 degrees Celsius, respectively and this occurred because of unplanned expansion of urban areas on behalf of green area as indicated by NDVI. Through this study planners and decision making could predict the future increase in LST if no action taken against these activities.
Normalized Difference Vegetation Index (NDVI)
Structuring and validation of photogrammetric territorial data
In the loving memory of Mariasofia Paparo, this publication focuses on the validation procedures of photogrammetric geographic information and the production and interpretation of complex reports, which are essential for handling the vast amount of generated data. Furthermore, sources of error in the structuring of geographic data and the quality parameters and conformity criteria necessary for the utilization of such data within the national geodatabase have been investigated.
Conceptual modeling of the structure of a geoinformation system for formation of land plots in state and municipal ownership of settlements
The article deals with the issues of automation of land cadastral information processing based on the modern international standards for management and processing of geospatial information. Object-relational databases together with the GIS provide powerful opportunities for collection, systematization, processing, analysis of various data, as well as storage and visualization of spatial information. According to the results of the analysis of the current legislative provision and processing of the existing data based on the international standards of the ISO 19110 series, the research developed a conceptual scheme of the geospatial database for the formation of state and municipal land plots within settlements. The research provides a model for checking the topology of the geospatial database of the system of automated formation of state and municipal land plots within settlements according to the structure and requirements of the international standard “ISO 19107:2019 Geographic information. Spatial schema”. The developed conceptual model and topology verification model are the basis for physical data modeling and allow ensuring the principle of interoperability in the Spatial Data Infrastructure.
Accuracy assessment of a three-dimensional model obtained using the LiDAR sensor of the iPhone 13 Pro Max
Scanning of an educational classroom was performed using the LiDAR sensor of the iPhone 13 Pro Max. Comparison was conducted between the lengths of lines and coordinates determined from the model scanned by the LiDAR sensor with precise data measured using the Leica TCR 405 ultra electronic total station and determined from the model created photogrammetrically from images captured with the Sony Cyber-shot DSC-RX100 camera. The Root Mean Square Error (RMSE) of distance and spatial positioning was calculated for the LiDAR-scanned model.
PHASE: a Matlab-based software for the DInSAR PS processing
The availability of free Synthetic Aperture Radar (SAR) images, like the ones delivered by the ESA Copernicus Sentinel-1 satellites, has led to the development of several processing tools, some of which are also free and open source. In this framework, when analyzing Sentinel data, the ESA SNAP software is usually required for data preprocessing, while, in the context of free and open source (FOS), Persistent Scatterer Interferometry (PSI) analysis can be performed by StaMPS (released by Stanford University). The workflow could be completed by the snap2stamps package, aiming at integrating the two main software packages. However, these tools are not designed to automate all the required steps to perform a complete PSI analysis. For this reason, the aim of this work is to develop PHASE (Persistent scatterer Highly Automated Suite for Environmental monitoring), a Matlab-based software suite that relies on already available FOS software, properly updated, enhanced and integrated, all accessible and customizable through a simple GUI. The focus is on minimizing the user interaction with the software, thus decreasing potential sources of error, while improving processing repeatability. The user will therefore primarily be responsible for configuring the processing parameters. Indeed, a streamlined procedure has been established, covering the entire process from downloading the SAR images to exporting the PS time series into a simple table format. In the paper, we present the developed software, highlighting its strengths compared to the status quo, while also providing a short example of successful application of the entire procedure.
Impact of data structure types and spatial resolution on landslide volumetric change measurements
Terrain is a dynamic component of the landscape, subject to rapid changes, particularly in scenarios such as landslides. This study investigates how the spatial resolution and data structure of digital terrain models (DTMs) influence the estimation of landslide volume changes. We selected a landslide formed by the undercutting action of the Belá River in Slovakia as our research site. Our findings indicate that raster data structures, across various spatial resolutions, generally yield more consistent volume estimates compared to 3D mesh data structures. Nonetheless, at higher spatial resolutions (0.1 m and 0.25 m), the 3D mesh data structure demonstrates superior capability in capturing detailed terrain features, resulting in more precise volume estimations of the landslide.
Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
Evaluation of the low-cost depth cameras for non-destructive testing
The primary aim of this paper is to assess the effectiveness of a low-cost stereo (depth) camera as a non-destructive tool for the detection and measurement of cracks in concrete surfaces. The experiment was carried out on four concrete beams with cracks, created with different concrete mixes. The mixes of the four beams were made up of lightweight aggregates with 12% of normal weight aggregates. One beam was used as a reference without fibers, while 3D steel fiber reinforcement, 5D steel fibers reinforcement, and a hybrid fibers mix of 5D steel fiber and synthetic were used for the other three beams. The cracks in the beams were measured manually followed by taking their stereo images with a ZED camera. The ZED images were processed to produce 3D models of the concrete surfaces, which are useful for crack measurement in a three-dimensional framework. The project results are particularly significant in the measurement of all three dimensions (length, width and depth), with less than a 10% error between the actual and the experimental procedure. Relatively, multiple differential approaches gave a less accurate result of a 15% error mainly due to syntax errors. Results suggest that the ZED depth camera is an effective tool for robust detection and measurement of cracks in concrete surfaces.
Building algorithms and classification thresholds for objects from point cloud data to create 3D city models
This article aims to develop an improved algorithm for classification of point cloud data. The primary component of this algorithm is determination of the classification thresholds for different geographical objects, which helps in the automatic classification of the LiDAR point cloud data. The algorithm was tested to classify the point cloud of three different areas of Ha Long city in Quang Ninh province. The results from the three areas show that for the ground points our algorithm is on average 6.4% more accurate than the traditional progressive TIN densification (PTD) algorithm. Further, with the proposed point cloud classification algorithms the average accuracy for asphalt roads is 87.77%, 98.09% for vegetation, and 96.86% for roof objects. The classified roof objects were further processed for house digitization, which provided an average accuracy of 92.07%. The whole dataset was used to develop 3D city models of the three areas (A1, A2 and A3 in Figure 7) in Hon Gai ward, Ha Long city with Level of Detail (LoD) 2.
Cluster analysis of factors influencing the valuation of real estate objects
The current real estate market analysis reveals challenges in valuation methods, procedure adequacy, and evolving technological approaches. Uncertainty arises from using localised methods for valuing individual real estate objects. A significant concern is the reliability and completeness of valuation data. Researchers emphasise market-driven aspects as trends in real estate valuation. Features for valuation are identified through quantitative characteristics, uncovering components and their nature. The research analyses foreign and domestic practices for real estate object valuation. Challenges include understanding methodological and informational support through mathematical methods and addressing factors affecting real estate object valuation. The need for cluster analysis to identify factors affecting real estate object valuation is recognised. To implement cluster analysis of factors affecting real estate valuation, a method is proposed involving the development of classification features, optimal typological grouping, and clustering implementation technology. Six groups of factors were chosen: spatial formation, urban planning provision, environmental impact, investment indicators, infrastructure provision, and limiting characteristics. An agglomerative process calculated the distance matrix between clusters of factors. The MacQueen k-means clustering method determined final clusters, confirming the validity of the proposed factor groups. The clustering of factors affecting real estate valuation was based on obtained distance data. The result identifies a high level of factors influencing real estate object valuation. Nine coincidences justify this in their clustering with four units of factors influencing real estate object valuation.
Surface runoff estimation at the Densu River Basin using geographic information system (GIS) and remote sensing (RS)
Accurate estimation of runoff depth and volume is essential for effective watershed management. Runoff, resulting from rainfall, is influenced by numerous factors, including soil type, vegetation, land use patterns, and rainfall characteristics. The Densu River Basin, located in the Greater Accra region of Ghana, has experienced flooding incidents, partly attributed to changes in land use and land cover. To address these challenges and facilitate proper flood management, drainage network design, hydropower generation, and other applications, this study aims to estimate surface runoff depth in the Densu River Basin, Ghana. The Natural Resources Conservation Services Curve Number (NRCS-CN) method, combined with Geographic Information System (GIS) and Remote Sensing (RS), is employed for runoff depth estimation. The research involves supervised classification of Landsat images from 2001, 2011, and 2022 to determine land use patterns, calculate grass cover percentages, identify hydrologic soil categories, extract rainfall intensity data, compute maximum soil storage, and estimate runoff depths for 10 year, 25 year, and 50 year return periods. The study reveals a significant increase in direct surface runoff depth, from 138.29 mm to 144.70 mm, for soil type D (Clay loam), the dominant soil type in the basin, during the 10 year return period, attributed to changes in land use and climate within the basin. The findings from this study hold valuable insights for mitigating environmental hazards in the area and improving water resource management.
Temporal analysis of multi-spectral instrument level and surface reflectance data sets for seasonal variation in land cover dynamics by using Google Earth Engine
By rapid growth in programming tools, accessibility to end consumer computing power, and the availability of free satellite data, the data science and remote sensing fields have begun to converge in recent years. Before this major processing time is wasted in collection of data. Google Earth Engine easily overcomes above problem; it contains data from different satellites and has power of processing and computation also. Well known data provider satellites are present in the library of GEE and users can easily process and track real time data from these satellites over GEE. “Sentinel”, a mission of the European Space Agency and “Landsat”, an American Earth observation satellite have been used in a variety of remote sensing applications. GEE makes these data sets available to the general public. These datasets are utilised for computing and analysis purposes. The objective of this study is to find change in study area by using above discussed two satellite data, over each season of year on different category of classification (Random Forest, CART, GTB and SVM). This work focuses on improving the classification accuracy of different classification algorithm by reviewing training samples and analyzing post-classification with image differencing in the algebraic technique. Because Landsat data have a medium spatial resolution, therefore point-wise computation was used. Lastly, we also detect which data sets are working better on an appropriate machine learning algorithm, so after final calculation we estimate accuracy of each algorithm by using confusion matrix and kappa.