Mustafa Zeybek

Followers

319

Following

124

Co-authors

Public Views

Surveying Engineer
Address: Selcuk University, Guneysinir Vocational High School, Architecture and Urban Planning, Konya/Turkiye

less

InterestsView All (9)

Uploads

Papers by Mustafa Zeybek

Improving the Spatial Accuracy of UAV Platforms Using Direct Georeferencing Methods: An Application for Steep Slopes

by Mustafa Zeybek and Selim Taskaya

remote sensing, 2023

The spatial accuracy of unmanned aerial vehicles (UAVs) and the images they capture play a crucia... more The spatial accuracy of unmanned aerial vehicles (UAVs) and the images they capture
play a crucial role in the mapping process. Researchers are exploring solutions that use image-based
techniques such as structure from motion (SfM) to produce topographic maps using UAVs while
accessing locations with extremely high accuracy and minimal surface measurements. Advancements
in technology have enabled real-time kinematic (RTK) to increase positional accuracy to 1–3 times the
ground sampling distance (GSD). This paper focuses on post-processing kinematic (PPK) of positional
accuracy to achieve a GSD or better. To achieve this, precise satellite orbits, clock information, and
UAV global navigation satellite system observation files are utilized to calculate the camera positions
with the highest positional accuracy. RTK/PPK analysis is conducted to improve the positional
accuracies obtained from different flight patterns and altitudes. Data are collected at altitudes
of 80 and 120 meters, resulting in GSD values of 1.87 cm/px and 3.12 cm/px, respectively. The
evaluation of ground checkpoints using the proposed PPK methodology with one ground control
point demonstrated root mean square error values of 2.3 cm (horizontal, nadiral) and 2.4 cm (vertical,
nadiral) at an altitude of 80 m, and 1.4 cm (horizontal, oblique) and 3.2 cm (vertical, terrain-following)
at an altitude of 120 m. These results suggest that the proposed methodology can achieve high
positional accuracy for UAV image georeferencing. The main contribution of this paper is to evaluate
the PPK approach to achieve high positional accuracy with unmanned aerial vehicles and assess the
effect of different flight patterns and altitudes on the accuracy of the resulting topographic maps.

Download

Extraction of forest inventory parameters using handheld mobile laser scanning: A case study from Trabzon, Turkey

Measurement, 2021

Abstract Forest inventory (FI) surveys are cumbersome when field measurements are performed by ma... more Abstract Forest inventory (FI) surveys are cumbersome when field measurements are performed by manual means. We propose a semi-automated data collection approach using handheld mobile laser scanning (HMLS) to estimate and map key FI parameters. To this end, machine learning (e.g., random forest classifier for tree detection) and innovative algorithms (e.g., ellipse fitting for diameter estimation of noncircular trees) were used for the first time in FI surveying. After surveying nine plots, we compared HMLS-derived data against the field reference. HMLS-derived tree diameters (DBHs) were strongly correlated with the reference data at the single-tree level ( r = 0.93–0.99; p 0.001). At the plot level, HMLS slightly overestimated DBHs in complex plots due to the influence of undergrowth and creepers on trunks. Yet, no statistically significant difference was found between the two datasets ( p > 0.05). Overall, HMLS was concluded as efficient and effective tool for FIs, even if used alone.

Heyelanlarin İzlenmesi̇nde Yersel Lazer Tarama Tekni̇kleri̇ni̇n Kullanilmasi Üzeri̇ne Araştirmalar

Heyelanlar çeşitli dinamik kuvvetlerin yeryüzünde meydana getirdiği değişimlerdir. Heyelanlar, ye... more Heyelanlar çeşitli dinamik kuvvetlerin yeryüzünde meydana getirdiği değişimlerdir. Heyelanlar, yeryüzü üzerinde önemli derecede etkiler oluşturan doğal afetlerin önde gelenlerinden bir tanesidir. Heyelanların etkilerinin azaltılması amacıyla izlenmesi Dünyada oldukça fazla sayıda ve değişik yöntemlerle çalışmalar yapılmaktadır. Teknolojinin sağladığı katkılarla bu yöntemler günden güne ilerlemekte, daha hassas, hızlı ve düşük maliyette araştırmaların sürdürülmesine imkân sağlamaktadır. GNSS ölçmelerinin yüksek doğruluğu, ekonomik olması, kıtalar arası hareketleri dahi izlenebilir hale getirmesi, klasik yersel ölçme yöntemlerine olan rağbeti bir hayli azaltmıştır. 2000'li yılların başında heyelanların GNSS yöntemleriyle araştırılması oldukça hızlı ilerlemiştir. Son on yılda teknolojik gelişmeler heyelanların izlenmesinde kullanılan tekniklerin gelişmesinde önemli katkılar sağlamıştır. Bu teknolojik gelişmeler doğrultusunda mühendislik uygulamalarına yeni bir boyut kazandıran laze...

Dağpazari Ki̇li̇sesi̇ (Mutmersi̇n) Rölöve Örneği̇

hkmo.org.tr

Restorasyon Projeleri,anıta ait ayrıntılı bir röleve (üçboyutlu modellendirmeye)dayanılarak hazır... more Restorasyon Projeleri,anıta ait ayrıntılı bir röleve (üçboyutlu modellendirmeye)dayanılarak hazırlanır.Rölöve bir binaın mevcut drumunun ölçekli çizimlerle (plan ,kesit ve görünüşler)anlatımıdır.Rölöve bir proje değildir.Projeye altlık veridir.Rölöve bir yapının,kent dokusunun veya arkeoloji kalıntının yakından incelenmesi,belgelenmesi,mimarlık tarihi açısından değerlendirilmesi ve restorasyon proesinin hazırlanabilmesi için bir araçtır.Bu çalışmada kültürel miraslarımızdan Mersin Mut'da bulunan Dağpazarı Kilisesinin Fotogrametrik yöntemle ölçümü ve belgeleme çalışması yapılmıştır.

Download

Road Surface and Inventory Extraction from Mobile LiDAR Point Cloud Using Iterative Piecewise Linear Model

Measurement Science and Technology

Roads are one of the main characteristics of cities, and their data should be updated periodicall... more Roads are one of the main characteristics of cities, and their data should be updated periodically. In this study, a new automatic method is proposed for extracting road surface information and road inventory from a Mobile LiDAR System-based point cloud. The proposed method consists of four steps. First, a three-dimensional point cloud is acquired using the MLS raw data. To improve the extraction accuracy, irrelevant points are removed from the point cloud. Piecewise linear models are used in the third step to classify the road surface. Road geometric characteristics such as centerline, profile, cross-section, and cross slope are extracted in the final step. The manually obtained road boundary is compared with the extracted road boundary to assess the classification results. Completeness, correctness, quality, and accuracy measures are range from 97% to 99%. When comparing these measures with previous studies, the proposed method produces one of the highest ones.

Elaziğ-Karakoçan Depremi̇ni̇n Tusaga-Akti̇f İstasyonlarina Etki̇si̇

ELAZIĞ-KARAKOCAN DEPREMININ TUSAGA-AKTIF ISTASYONLARINA ETKISI Ozet Dogu Anadolu Fay Zonu (DAFZ) ... more ELAZIĞ-KARAKOCAN DEPREMININ TUSAGA-AKTIF ISTASYONLARINA ETKISI Ozet Dogu Anadolu Fay Zonu (DAFZ) Turkiye’de cok sayida buyuk depremlere neden olan aktif fay zonlarindan birisidir. Turkiye’de 2009 yilindan beri calisan Turkiye Ulusal Sabit GNSS Istasyonlari Agi-Aktif (TUSAGA-Aktif) bulunmaktadir. Bu ag depremler neticesinde olusan deformasyonlari belirlemede etkin rol oynamaktadir. DAFZ yakininda, Elazig-Karakocan da 8 Mart 2010 tarihinde 6.0 Mw buyuklugunde Karakocan depremi meydana gelmistir. Bu calismanin amaci, TUSAGA–Aktif istasyonlarinin verileri yardimiyla Karakocan deprem merkezine yakin istasyonlarin yer degistirmelerin buyuklugunu ve yonunu belirlemektir. Istasyon verilerinin dengelenmesinde AUSPOS (Australian Online GPS Processing Service) internet tabanli veri isleme servisi kullanilmistir. Calismada Karakocan depreminden etkilendigi tahmin edilen DAFZ yakininda TUSAGA - Aktif istasyonlarinin deprem tarihinden 5 gun once ve 5 gun sonraki gunlere ait dengeleme sonuclari a...

Download

Automated Extraction of Road Information from UAV-based Data

GIM International, 2022

When it comes to monitoring the condition of roads, UAV technology can overcome many of the downs... more When it comes to monitoring the condition of roads, UAV technology can overcome many of the downsides associated with traditional methods, which can be time-consuming, labour-intensive and sometimes subjective. This article explores the opportunities for automated extraction of UAV-based data information about road construction, inventory and road environments. Automated Extraction of Road Information from UAV-based Data THE BENEFITS OF AIRBORNE TECHNOLOGY IN URBAN PROJECTS data. The fl ight plan is remotely set in the interface and the UAV fl ies and acquires data automatically. However, the fl ight plan needs to be adjusted according to the characteristics of the UAV platform, which are maximum fl ight time, fl ight speed, height above ground level and horizontal distance.

Download

El-tipi mobil LiDAR teknolojisinin orman envanterlerinde kullanımı: Artvin-Şavşat örneği

Ormancılık Araştırma Dergisi, 2021

Bu çalışmanın amacı; (i) orman envanterlerinde mobil lazer tarama (LiDAR) teknolojisinden yararl... more Bu çalışmanın amacı; (i) orman envanterlerinde mobil lazer tarama
(LiDAR) teknolojisinden yararlanma olanaklarını araştırmak ve (ii)
meşcere parametrelerine ilişkin LiDAR verilerini, uygulamada tespit
edilen değerlerle karşılaştırmaktır. Bu doğrultuda, Şavşat’ta arazi ölçümleri
gerçekleştirilen örnek alanlar el tipi LiDAR cihazı ile taranmıştır.
Daha sonra örnek alanlardan elde edilen veri setleri birbiriyle
karşılaştırılarak LiDAR’ın hassasiyeti sınanmıştır. Yapılan istatistik
testler sonucunda, LiDAR ve çapölçer ile ölçülen ağaçların çapları
arasında anlamlı bir fark bulunmamıştır (p>0,05). Yersel ölçümler
referans kabul edilirse; göğüs çapı, ağaç sayısı, meşcere üst boyu ve
meşcere hacmi parametreleri LiDAR cihazıyla sırasıyla; ort. 0,68 cm
(%2,2), 14 ad/ha (%2,0), 0,8 m (%3,4) ve 155,7 m3/ha (%24,6) hata
ile tahmin edilebilmiştir. Hacimde gözlenen yüksek hata üzerine,
arazideki altı adet ağaç önce LiDAR ile dikili halde taranmış ve sonra
kesilerek, bölümleme yöntemiyle hacimlendirilmiştir. Yerde ölçülen
gövde hacimlerinin LiDAR ile ort. 0,061 m3 (%5,1) hata ile tespit edilebildiği
görülmüştür. Dolayısıyla, meşcere hacimlerindeki yüksek
hata oranlarının LiDAR yönteminden değil, envanterde kullanılan
tek girişli hacim tablolarından kaynaklandığı anlaşılmıştır. Buna
karşılık, LiDAR nokta bulutları üzerinden ağaç türü ve meşcere tipleri
belirlenememiştir. Çalışmanın sonunda, amenajman planlarındaki
birçok meşcere parametresine ait değerlerin mobil LiDAR teknolojisiyle
arazide daha az vakit harcanarak kabul edilebilir doğruluk
düzeylerinde hesaplanabildiği sonucuna ulaşılmıştır.

Download

Turkish Journal of Remote Sensing and GIS

Unmanned Aerial Vehicle (UAV) technology is one of the fastest-growing technologies especially us... more Unmanned Aerial Vehicle (UAV) technology is one of the fastest-growing technologies especially used in image processing. Structure-from-Motion (SfM) based software are usually used to convert two-dimensional UAV-based images into three-dimensional (3D) data. Then, objects such as buildings, trees, and roads can be classified from the 3D data for further analysis. In this study, the road surface generated from 3D data was evaluated. There are several factors that affect the accuracy of the 3D data. In this study, two factors, namely UAV flight altitude and SfM based software, were evaluated. Two different flight altitudes, which were 35 meters and 50 meters, were used. It was found that the lower flights with closer altitudes did not make a significant difference on the results and produced similar results. Another factor is different SfM based software. Two well-known SfM based software were used in this study, which were the Agisoft Metashape and Pix4D Mapper. In this case study, i...

Download

Improvements on Road Centerline Extraction by Combining Voronoi Diagram and Intensity Feature from 3D UAV-Based Point Cloud

Innovations in Smart Cities Applications Volume 5, 2022

Mobil LiDAR sistemleri ile Orman Parametrelerinin Kestirilmesi

Mobil LiDAR Nokta Bulutu Verilerinden Asfalt Yol Yüzeyinin Otomatik Çıkarımı

Conventional air pollutant source determination using bivariate polar plot in Black Sea, Turkey

Environment, Development and Sustainability, 2021

The purpose of this study is to identify and characterize individual sources of pollutants such a... more The purpose of this study is to identify and characterize individual sources of pollutants such as PM10, SO2, NOx, and CO in the urban area in Karadeniz (Turkey) using the bivariate polar plots method. In addition, the relationship between the meteorological conditions and the pollutants was determined based on correlation analysis in the region. Bivariate polar plots are a graphical method used to demonstrate the dependence of pollutant concentrations on wind direction measured at stations. Thanks to these graphics, resource types and properties can be determined. Wind flow and pollution data were used to provide information on wind and pollutant interactions in the study area. As a result of the study, it was founded that the main source of pollutants is intensive anthropogenic activities such as urban, street traffic, agricultural activities, and natural resources. It has been concluded that the highway in the region is not an important source of pollutants. In addition, the pollutant relations were examined with meteorological data, and it was discovered that temperature and relative humidity were effective for all pollutants.

Assessment of changes in masseter muscle by three-dimensional close-range photogrammetry after Botulinum toxin type-A injection: A case report with review of literature

JPMA. The Journal of the Pakistan Medical Association, 2019

Masseter muscle hypertrophy (MMH) is a benign, unilateral or bilateral, painless enlargement. Tre... more Masseter muscle hypertrophy (MMH) is a benign, unilateral or bilateral, painless enlargement. Treatment protocols include surgical excision or a non-invasive option, using botulinum toxin type A (BTX-A). There is no study in the literature that measures this dimensional change in the masseter muscle (MM). The aim of this case report is to present changes in volume and surface area in MM with three-dimensional closer an gestereophotogrammetry (3DCS). For treatment 30 units of BTX-A was injected into the three points hypertrophic muscle and patient records were taken to compare with 3DCS with a non-metric Canon EOS 550 D camera before and after injection. The changes in the surface area and volume of this muscle were mapped and the objective data were obtained. This technique is useful for predicting the results of BTX-A application, and can be a useful tool for better physicianpatient communication.

Download

Evaluation of Landslide Susceptibility of Şavşat District of Artvin Province (Turkey) Using Machine Learning Techniques

The aim of this study is to produce landslide susceptibility maps of Şavşat district of Artvin Pr... more The aim of this study is to produce landslide susceptibility maps of Şavşat district of Artvin Province using machine learning (ML) models and to compare the predictive performances of the models used. Tree-based ensemble learning models, including random forest (RF), gradient boosting machines (GBM), and extreme gradient boosting (XGBoost), were used in the study. A landslide inventory map consisting of 85 landslide polygons was used in the study. The inventory map comprises 32,777 landslide pixels at 30 m resolution. Randomly selected 70% of the landslide pixels were used for training the models and the remaining 30% were used for the validation of the models. In susceptibility analysis, altitude, aspect, curvature, distance to drainage network, distance to faults, distance to roads, land cover, lithology, slope, slope length, and topographic wetness index parameters were used. The validation of the models was conducted using success and prediction rate curves. The validation resu...

Download

Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud

Transportation Research Record: Journal of the Transportation Research Board, 2021

The accuracy of random forest (RF) classification depends on several inputs. In this study, two p... more The accuracy of random forest (RF) classification depends on several inputs. In this study, two primary inputs—training sample and features—are evaluated for road classification from an unmanned aerial vehicle-based point cloud. Training sample selection is a challenging step since the machine learning stage of the RF classification depends greatly on it. That is, an imbalanced training sample might dramatically decrease classification accuracy. Various criteria are defined to generate different types of training samples to evaluate the effectiveness of the training sample. There are several point features that can be used in RF classification under different circumstances. More features might increase the classification accuracy, however, in that case, the processing time is also increased. Point features such as RGB (red/green/blue), surface normals, curvature, omnivariance, planarity, linearity, surface variance, anisotropy, verticality, and ground/non-ground class are investigat...

Inlier Point Preservation in Outlier Points Removed from the ALS Point Cloud

Journal of the Indian Society of Remote Sensing, 2021

In the last decade, airborne light detection and ranging (LiDAR) scanning (ALS) technology has be... more In the last decade, airborne light detection and ranging (LiDAR) scanning (ALS) technology has become a powerful technique for remote sensing, imaging, and mapping. However, the data obtained from any measurement system can include inaccurate signals affected by systematic errors or by the external environment. De-noising to remove inaccurate outlier points is a fundamental and challenging problem for ALS-based mapping applications. The proposed method aims to recover the patterned (planar and linear) points within the assigned outlier and removed points. The method consists of 3 steps. First, statistical outlier removal (SOR) filtering is implemented, and outlier points are detected with the filtering method. Next, the machine learning system reclassifies the filtered outlier points. If the classification result is “inlier” , that point is added to the filtered inlier point cloud as an inlier point. The accuracy of outlier points was evaluated against a manually determined validation set. The results achieved 99%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$99\\%$$\\end{document} and 98%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98\\%$$\\end{document} according to the highest overall accuracy criterion and kappa coefficient, respectively. These findings are a promising step to test the proposed method in three different test areas and extend it to widespread spatial dimensions. Furthermore, the findings show that many useful points are removed by SOR filtering. The developed methodology contributes to the reduction of errors caused by data losses in various modelling studies, especially for power transmission line and 3D façade modelling studies.

Indoor mapping and positioning applications of hand-held LiDAR Simultaneous localization and mapping (SLAM) systems

Turkey Lidar Journal, 2021

Download

An approach for the automated extraction of road surface distress from a UAV-derived point cloud

Automation in Construction, 2021

Abstract The condition of the road surface should be inspected to increase the service life of th... more Abstract The condition of the road surface should be inspected to increase the service life of the road and to ensure safety and comfort. This study aims to automatically detect and measure road distress from unmanned aerial vehicle (UAV)-based images. The proposed methodology consists of three steps. First, images acquired from the UAV are used to generate the three-dimensional point cloud. Then, the road surface is extracted from the 3D point cloud. Finally, the developed algorithm is used to automatically detect and measure road distress. The accuracy assessment is conducted by comparing the analyses from point cloud data and measurements obtained from the traditional inspection method. The root mean square error values range from 2.09–6.72 cm. Finally, the outcomes of the proposed methodology are compared with those of commercial GIS software. Both produce statistically similar results for detecting road surface distress.

Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey

Natural Hazards, 2021

Landslide susceptibility maps provide crucial information that helps local authorities, public in... more Landslide susceptibility maps provide crucial information that helps local authorities, public institutions, and land-use planners make the correct decisions when they are managing landslide-prone areas. In recent years, machine-learning techniques have become very popular for producing landslide susceptibility maps. This study aims to compare the performance of these machine learning models with the traditional statistical methods used to produce landslide susceptibility maps. The landslide susceptibility for Ardanuc, Turkey was evaluated using three models: logistic regression (LR), support vector machine (SVM), and random forest (RF). Ten parameters that are effective in landslide occurrence are used in this study. The accuracy and prediction capabilities of the models were assessed using both the receiver operating characteristic (ROC) curve and area under the curve (AUC) methods. According to the AUC method, the success rate of the LR, SVM, and RF models was 83.1%, 93.2%, and 98.3%, respectively. Further, the prediction rates were calculated as 82.9% (LR), 92.8% (SVM), and 97.7% (RF). According to the verification results, RF and SVM models outperformed the traditional LR model in terms of success and prediction rate. The RF model, however, performed better than the SVM model in terms of success and prediction rates. The landslide susceptibility maps produced as a result of this study can guide city planners, local administrators, and public institutions related to disaster management to prevent and reduce landslide hazards.

Mustafa Zeybek

Uploads

Papers by Mustafa Zeybek

Log In