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Current Abstracts
Past Abstracts

Email: Peter Collier


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Survey Review 55, No 388. January/February 2023

1. The novelty hybrid model development proposal for mass appraisal of real estates in sustainable land management
Suleyman Sisman, Ali Utku Akar & Sukran Yalpir

In this study, a new methodology has been developed for a sustainable mass appraisal system. A mathematical model was created with the combination of the Cobb-Douglas and the linear regression model. With the Analytic Hierarchy Process (AHP) method, real estate value criteria were grouped and weighted in a hierarchical structure. The weights obtained with AHP were integrated into the coefficients regarding the criteria weights and densities in the Cobb-Douglas hybrid model. The new hybrid model was confirmed with the features and price equivalents of 435 parcels for sale from the market. Besides, the model analysis results were compared with the Multiple Regression Analysis (MRA) modelling using market prices. While creating the methodology, Geographic Information Systems (GIS) was used to organize the geographic and regional data of the region. After developing the new hybrid model, criteria groups that developed the model and relevant sub-criteria were evaluated using Pearson's correlation analysis.

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2. Filtering airborne LIDAR data by using fully convolutional networks
Abdullah Varlik & Firat Uray

The classification of LIDAR point clouds has always been a challenging task. Classification refers to label each point in different categories, such as ground, vegetation or building. The success of deep learning techniques in image processing tasks have encouraged researchers to use deep neural networks for classification of LIDAR point clouds. In this paper, we proposed a U-Net based architecture capable of classifying LIDAR data. The results indicated that our network model achieved an average F1 score of 91% over all three classes (ground, vegetation and building) for our best model.

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3. Comparison of different machine learning models for mass appraisal of real estate
Süleyman Sefa Bilgilioğlu & Hacı Murat Yılmaz

The present study aimed to compare five machine learning techniques, namely, artificial neural network (ANN), support vector machine (SVM), chi-square automatic interaction detection (CHAID), classification and regression tree (CART), and random forest (RF) for mass appraisal of real estate. Firstly, 1982 precedent data was collected throughout the entire study area for train and test models. Secondly, a total of 68 variables were considered for the mass appraisal. Subsequently, the five machine learning techniques were applied. Finally, the receiver operating characteristic (ROC) and various statistical methods were applied to compare five machine learning techniques.

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4. Performance investigation of Trimble RTX correction service with multi-GNSS constellation
Omer Faruk Atiz, Abbas Qader Shakor, Sermet Ogutcu & Salih Alcay

In the middle of 2011, Trimble introduced the RTX correction service to support RT-PPP applications. In this study, the performance of Trimble RTX correction service is investigated over a one-month period using five different GNSS constellations – namely, GPS-only, GPS + GLONASS, GPS + Galileo, GPS + GLONASS + Galileo, and GPS + GLONASS + Galileo + BeiDou. The results show that positioning accuracy and convergence time are significantly improved with the use of the multi-GNSS constellation compared with the GPS-only solution. The result of the converged positioning accuracy indicates that the GPS + GLONASS + Galileo + BeiDou combination improves the accuracy by 63%, 54%, and 60% for north, east, and up components, respectively, compared with the GPS-only solution. The mean convergence time is reduced by the GPS + GLONASS+ Galileo + BeiDou combination by 70%, 71%, and 38.6% compared with the GPS-only solution in the north, east, and up components, respectively. 1.2 (north), 1.5 (east), and 2.3 cm (up) root mean square errors (RMSEs) of converged positioning from the GPS + GLONASS + Galileo + BeiDou combination are computed.

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5. Comparative analysis of real-time precise point positioning method in terms of positioning and zenith tropospheric delay estimation
Omer Faruk Atiz, Salih Alcay, Sermet Ogutcu & Ilkay Bugdayci

The positioning performance of widely used real-time precise point positioning (RT-PPP) software packages BNC, RTKLIB, and PPP-WIZARD were tested in terms of convergence time and accuracy. The convergence time of PPP-WIZARD solutions is reduced by ambiguity resolution (AR). The GPS + GLONASS + GALILEO (GRE) mode improved the convergence time of GPS + GALILEO (GE) mode by 22.0%, 15.5%, 17.1%, and 11.4% for the BNC, RTKLIB, PPP-WIZARD (AR) and PPP-WIZARD, respectively. For the GRE mode, RMSEs of the BNC, RTKLIB, PPP-WIZARD (AR), and PPP-WIZARD software packages in the horizontal/vertical component are 3.8/5.6, 2.6/6.2, 3.3/6.5, 4.3/7.0 cm, respectively. In comparison with the IGS-ZTD (International GNSS Service ZTD), BNC, RTKLIB, PPP-WIZARD (AR), and PPP-WIZARD solutions show a mean bias of 0.28, −0.72, 2.80, and 2.83 cm, respectively in GE mode. The GRE mode reduced the RMSEs of the ZTD estimations of BNC, RTKLIB, PPP-WIZARD (AR) and PPP-WIZARD by 2.9%, 5.1%, 0.6%, and 0.4% respectively.

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6. Positioning performance of GNSS-PPP and PPP-AR methods for determining the vertical displacements
Burak Akpınar

This study investigates the accuracy of vertical displacements monitored by Global Navigation Satellite Systems (GNSS) precise point positioning (PPP) with float-ambiguity solution and with ambiguity resolution (PPP-AR). For this purpose, a simulation was designed. The static GNSS observations were collected at a test point during different observation times over seven periods involving vertical displacements produced with a precision of less than one mm. Each set of GNSS observations was processed with both GNSS-PPP and PPP-AR methods. The results revealed that RMS values of PPP-AR solutions are about twice better than RMS values of PPP solution for all observation times and all vertical displacement values.

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7. Investigating the latest contribution of BeiDou-3 FOC to GPS/GLONASS/Galileo PPP
Sermet Ogutcu, Yuksel Kama Erkavas, Abbas Qader Shakor & Haitham Talib Farhan

In this study, the latest contribution of the BeiDou-3 FOC satellites to GPS/GLONASS/Galileo/BDS-2 combined static and kinematic precise point positioning (PPP) is investigated over a one-month period in 2021 considering the three different cut-off angles (7°, 15°, and 30°). The results show that the contribution of BDS-3 to GPS/GLONASS/Galileo PPP is generally marginal for 7° and 15° cut-off angles. The largest BeiDou-3 contributions to static and kinematic positioning accuracy are found to be 9%, 6%, and 19% (for 3-h sessions with the 15° cut-off angle) and 9%, 15%, and 8% (with the 30° cut-off angle) for the north, east, and up components, respectively. The largest reductions in static and kinematic convergence time by adding BDS-3 to GPS/GLONASS/Galileo are found to be 13%, 10%, and 6% (for the 30° cut-off angle) and 17%, 7%, and 6% (for the 30° cut-off angle) for the north, east, and up components, respectively.

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8. Book Review: Land surveying in Ireland, 1690–1830 by Finnian Ó. Cionnaith, Dublin, Four Courts Press, 272 pp., €35.00/£30.00 ISBN 978-1-80151-014-1
Peter Collier

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