Profile URL: https://hdl.handle.net/20.500.13091/11771
Job Title:Doç. Dr.
Email Address:hbmakineci@ktun.edu.tr
Main Affiliation:02.08. Department of Geomatic Engineering
Status: Current Staff
ORCID:
0000-0003-3627-5826
0000-0003-3627-5826Scopus ID:
57191188477
57191188477YÖK Akademik: AAE38EFC6DB07A59
Google Scholar:
ns0mGq8AAAAJ
ns0mGq8AAAAJWeb of Science ID:
ABC-4198-2020
ABC-4198-2020Name Variants:
Makineci, H. B. Makineci, H. Bilgehan
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Doctoral Thesis İnsansız Hava Aracı ile Fotogrametrik Temelli Görüntü Alımı ve Uçuş Optimizasyonu(Konya Teknik Üniversitesi, 2020) Makineci, Hasan Bilgehan; Karabörk, Hakanİnsansız Hava Araçları (İHA) ve İnsansız Hava Araçları Sistemleri (İHAS) son yüzyılın en önemli araştırma konularından biridir. Sivil kullanımın yaygınlaşması ve araştırmacıların kolay erişebilmesi sayesinde, doğal yaşamda ve insan hayatında çok önemli kullanım alanlarında İHAS'lar tercih edilmektedir. Hızla gelişen İHAS teknolojisi yardımıyla, yeni konular ortaya çıkmakta ve herkese hitap eden araştırmalar yapılmaktadır. Son yıllarda haritacılık alanında İHA kullanımının artması beraberinde birtakım problemlerinde oluşmasına sebep olmuştur. Özellikle araştırmacıların üzerine çalışmalar yaptıkları konulardan biri de İHA uçuş optimizasyonudur. Günümüzde birçoğunun (güneş pili taşıyanlar hariç) yakıtı sonlu kaynaklar tarafından sağlanan İHA'lar için enerji çok önemlidir. Gündelik hayatta insan beyninin kendiliğinden yaptığı optimizasyon işlemleri yakın zamanda, insan gibi düşünebilen Yapay Zeka (YZ) uygulamalarına konu olmaktadır. Bilgisayar öğrenmesi, derin öğrenme, Yapay Sinir Ağları (YSA) ve bunlara benzer birçok optimizasyon algoritması da YZ çalışmalarının ilerlemesine vesile olmaktadır. YZ algoritmalarının elektronik işlerdeki optimizasyon ihtiyacını gidererek gündelik hayata yönelik önemli avantajlar sağlayacağı aşikardır. Bu çalışmada amaç, İHA'nın fotogrametrik kullanımında havada kalabileceği optimum koşulları belirlemek ve sonuçları analiz ederek uçuş optimizasyonunu gerçekleştirebilmektir. Bu amaca uygun olarak havada kalma süresine etki eden tüm parametreler çeşitlendirilerek, farklı deney sonuçları gözlemlenmiştir. Kırsal alan, kentsel alan gibi farklı tip alanlarda ve eğimli arazi, çok eğimli arazi gibi farklı eğim koşullarında yapılan arazi çalışmaları sonucunda elli üç adet deneme uçuşu gerçekleştirilmiş ve gözlemler kayıt edilmiştir. YSA kullanılarak yapılan optimizasyon çalışmasında, arazide gerçekleştirilen elli üç farklı İHA uçuşunda belirlenen girdi parametreleri (İHA Tipi, Yer Örnekleme Aralığı, Bindirme Oranı ve Atmosferik Koşullar) kullanılarak eğitilmiş ağ ile çıktı parametreleri olarak belirlenen Batarya Durumu ve Uçuş Süresi test edilmiştir. Fotogrametrik ürünler üretilecek şekilde alınan görüntüler değerlendirilerek, uçuş planlaması parametrelerinin optimizasyonu hedeflenmiştir. Uçuş optimizasyonu bir bütün olarak ele alınarak girdi parametreleri çeşitlendirilip, çıktı parametrelerin regresyon değerleri irdelenmiştir. YSA çerçevesinde farklı eğitim algoritmalarının (Gradient Descent –GD- Algoritması ve Levenberg-Marquet - LM- Algoritması) optimizasyona etkileri araştırılmış, girdilerin normalizasyon öncesi ve sonrası durumları karşılaştırılmıştır. Sonuçların kullanıcıya ulaşması amacıyla bir grafik tabanlı arayüz hazırlanarak optimize edilmiş veriler grafik arayüze altlık olarak atanmıştır. Geliştirilen grafik ara yüzün haritacılıkta en sık kullanılan uçuş planlamalarına örnekler tanımlayarak, optimizasyon için fikir vermesi hedeflenmiştir. Sonuçlar tüm girdi parametrelerinin kullanıldığı optimizasyon modelinin en iyi sonuçlar için GD algoritmasında normalizasyon sonrası verilerle %82 doğrulukla tahmin yürüttüğünü ortaya koymuştur. Farklı parametrelerin optimizasyondan çıkarıldığı tahmin sonuçlarında da en iyi sonuçların normalizasyon öncesi verilerle GD algoritmasında %69 olduğu görülmüştür. Sonuç olarak fotogrametrik amaçlı İHA kullanımında uçuş optimizasyonunun YSA ile optimizasyonunun gerçekleştirildiği belirlenmiştir.Article Citation - WoS: 9Citation - Scopus: 10Evaluation of Test Field-Based Calibration and Self-Calibration Models of Uav Integrated Compact Cameras(SPRINGER, 2021-11-09) Kazar, Gülüstan Kılınç; Karabörk, Hakan; Makineci, Hasan Bilgehan; Kılınç Kazar, GülüstanUnmanned aerial vehicles (UAVs), which have made a name for themselves in photogrammetry studies in recent years, provide users with integrated camera systems. Identifying interior orientation parameters, such as focal coordinates, focal length and distortions, is an essential requirement for camera systems used for photogrammetric purposes. This process, which is called camera calibration, is offered automatically by software from the library. Another important known calibration method is self-calibration. Calibrating cameras by creating 2D or 3D test areas is a troublesome and grueling option. However, it is the most commonly accepted way in terms of accuracy. In this study, images were taken in different test areas (2D and 3D) to perform the calibrations of the cameras integrated on two different UAVs, namely DJI Phantom 4 Pro and Parrot Anafi. The calibration parameters determined from the images taken were compared with the calibration parameters obtained by the self-calibration method, and block adjustment was performed with ground control points marked in the study area. In order to perform performance analysis, the root-mean-square error (RMSE) was determined from the control points. In conclusion, it was determined that the results of both the calibrations obtained with the test fields and those obtained with self-calibration were acceptable.Article Citation - WoS: 11Citation - Scopus: 15Accuracy Assessment of Dems Derived From Multiple Sar Data Using the Insar Technique(SPRINGER HEIDELBERG, 2021-01-08) Karabörk, Hakan; Makineci, Hasan Bilgehan; Orhan, Osman; Karakuş, PınarIn this study, digital elevation models (DEMs) derived from AlosPalsar data (Japanese Space Agency-JAXA), Sentinel-1A data, and Envisat ASAR data (European Space Agency-ESA) were compared by using a global navigation satellite system (GNSS). In addition, AW3D30, SRTM, and ASTER GDEM (open-access DEMs) data were also included in the accuracy evaluation. The DEM accuracies were investigated in three different terrain types, namely a plain area, mountainous area and agricultural area, and compared at elevation values on a pixel-based. The accuracy obtained from the ALOS PALSAR satellite data was found to be more reliable for all three terrain types. The standard deviation and root mean square values were calculated and compared to each other. The results of the accuracy assessments showed that the best result for the plain area was obtained with the Sentinel-1A and SRTM data, for the mountainous area was obtained with the SRTM data and for agricultural area was obtained with the ALOS PALSAR and SRTM data.Article Spatio-Temporal Change Detection of Built-Up Areas with Sentinel-1 SAR Data Using Random Forest Classification Arnavutköy Istanbul(2023) Maki̇neci̇, Hasan BilgehanAs one of the most populated cities in Turkiye and the world, the Istanbul metropolis has always attracted the masses. Arnavutköy Town has become one of the critical points of Istanbul City with increasing built-up areas (BAs). The spatial-temporal change detection of the expansion of the BA of this district is essential data on behalf of Istanbul City. This research aims to determine urban areas expansion zones, also defined as the BA footprint, from Sentinel-1 radar data. The determination of Sentinel-1A data of the urban area change detection encountered in Arnavutköy Town between 2018-2021 with Random Forest (RF) classification machine learning algorithm is investigated in this study. The changes experienced with the spatial-temporal data were determined, and causes and effects were investigated. In order to visually compare the Normalized Difference Built-up Index (NDBI) and optical Sentinel-2A's false color urban RGB composite, which is a distinct data format, the processes have been proved. SAR satellite data was found to be more appropriate than optical satellite data since not being affected by atmospheric conditions for extracting BAs with remotely sensed data.Master Thesis Türkiye topoğrafik vektör veritabanı (TOPOVT) verilerinin Türkiye Ulusal Coğrafi Bilgi Sistemi (TUCBS) şemalarına uygun dönüştürülmesi(2026) Karakullukçu, Fatih; Makineci, Hasan BilgehanBu tez çalışması, Harita Genel Müdürlüğü Topografik Veri Tabanı (TOPOVT) ile Türkiye Ulusal Coğrafi Bilgi Sistemi (TUCBS) arasındaki veri dönüşüm ve entegrasyon süreçlerini incelemektedir. Çalışmanın temel amacı, mevcut TOPOVT verilerinin TUCBS standartlarına uyarlanarak ulusal düzeyde birlikte çalışabilir, standart ve bütünleşik bir coğrafi veri yapısının oluşturulmasıdır. Araştırma kapsamında, her iki sistemin veri sözlükleri ve UML model bileşenleri yapısal ve anlamsal (semantik) açıdan karşılaştırmalı olarak analiz edilmiştir. Bu analizler doğrultusunda detay sınıfları, öznitelikler ve kod listeleri eşleştirilmiş; kaynak ve hedef şemalar arasındaki uyumsuzlukları giderecek dönüşüm kuralları belirlenmiştir. Uygulama sürecinde, veri bütünlüğünü korumak amacıyla otomatik dönüşüm araçları kullanılmıştır. Elde edilen sonuçlar, sunulan yöntemin TOPOVT verilerinin TUCBS standartlarına entegrasyonunu teknik olarak mümkün kıldığını ve bu sürecin birlikte çalışılabilirliğin sağlanmasına yönelik gereksinimleri derinlemesine irdelediğini ortaya koymaktadır. Geliştirilen dönüşüm modeli, veri bütünlüğünü güçlendirerek kaynak kullanımında verimliliği artırmakta ve Türkiye'nin ulusal coğrafi bilgi altyapısının sürdürülebilirliğine doğrudan katkı sunmaktadır.Article Citation - WoS: 4Citation - Scopus: 5A New Precise Point Positioning With Ambiguity Resolution (ppp-Ar) Approach for Ground Control Point Positioning for Photogrammetric Generation With Unmanned Aerial Vehicles(MDPI, 2024) Makineci, Hasan Bilgehan; Bilgen, Burhaneddin; Bulbul, SercanUnmanned aerial vehicles (UAVs) are now widely preferred systems that are capable of rapid mapping and generating topographic models with relatively high positional accuracy. Since the integrated GNSS receivers of UAVs do not allow for sufficiently accurate outcomes either horizontally or vertically, a conventional method is to use ground control points (GCPs) to perform bundle block adjustment (BBA) of the outcomes. Since the number of GCPs to be installed limits the process in UAV operations, there is an important research question whether the precise point positioning (PPP) method can be an alternative when the real-time kinematic (RTK), network RTK, and post-process kinematic (PPK) techniques cannot be used to measure GCPs. This study introduces a novel approach using precise point positioning with ambiguity resolution (PPP-AR) for ground control point (GCP) positioning in UAV photogrammetry. For this purpose, the results are evaluated by comparing the horizontal and vertical coordinates obtained from the 24 h GNSS sessions of six calibration pillars in the field and the horizontal length differences obtained by electronic distance measurement (EDM). Bartlett's test is applied to statistically determine the accuracy of the results. The results indicate that the coordinates obtained from a two-hour PPP-AR session show no significant difference from those acquired in a 30 min session, demonstrating PPP-AR to be a viable alternative for GCP positioning. Therefore, the PPP technique can be used for the BBA of GCPs to be established for UAVs in large-scale map generation. However, the number of GCPs to be selected should be four or more, which should be homogeneously distributed over the study area.Article Citation - WoS: 7Citation - Scopus: 8Seasonal Drought Analysis of Aksehir Lake With Temporal Combined Sentinel Data Between 2017 and 2021 Spring and Autumn(Springer, 2022-06-24) Makineci, Hasan BilgehanThe threat of drought has been felt almost worldwide in recent years. It is critical to determine the causes of drought and how seasonal changes affect it. Additionally, it is necessary to determine the speed and impact area of drought, monitor drought areas, and attempt to find solutions against drought. With the developing satellite sensing systems, remote sensing methods are being used to investigate topics such as the increase and extent of drought, uncontrolled water consumption in agricultural activities, and the effects of unnatural pollutants on freshwater resources such as lakes and rivers. Using Synthetic Aperture Radar (SAR) satellite data to monitor changes in water bodies is a relatively new area of study in remote sensing. The spatial extent and seasonal change (spring and autumn) of droughts between 2017 and 2021 in Aksehir Lake were determined from Sentinel-1A SAR satellite data, and the Normalized Differential Water Index (NDWI) was calculated using Sentinel-2A optical satellite data and Standardized Precipitation Index (SPI) in this research. In addition, a different approach was applied to determine the change of wetland boundaries more accurately by converting the linear Sigma0 band to the decibel (dB) band and applying a non-linear 3 x 3 maximum filter to the dB band to Sentinel-1A data. Consequently, it has been established that Aksehir Lake, which used to have wetlands during the spring seasons but began to dry up in the autumn seasons, had completely dried up in both periods in 2021.Article Comparison of Digital Elevation Models Produced with Photogrammetric Usage of UAV by Geodetic Techniques(2020) Karabörk, Hakan; Durdu, Akif; Maki̇neci̇, Hasan BilgehanUnmanned Aerial Vehicles (UAV) use in the production of the map for photogrammetric purposes. Unlike aerial photogrammetry, UAV cameras are non-metric amateur cameras. Therefore, they need some operations to use in photogrammetry. Structure from Motion (SfM) algorithms prefers for processing images because of the usage of the non-metric cameras. These algorithms generally identify key-points (via feature extraction) on the photos and match tie-points (via feature point matching) in overlap images. SfM is a photogrammetric technique that produces keypoint to match by identifying key points, such as edge-to-corner points, through high-resolution RGB photos. The scope of this study was to compare the results obtained by UAVs and the results acquired by ground truth data. In this comparison, SfM algorithm performance, the effects of flight height, overlap rate, and UAV-type on the model investigated, and significant results achieved. Additionally, the models obtained from the UAV photographs with different flight heights and overlaps in the areas with varying characteristics of the slope compared. Consequently, it determined the difference between around 20 cm (Z value), comparing the flight height of 80 m and the flight height of 120 m. Since it is observed that the flight height does not have a significant effect.Article Determining Land Cover and Land Use Changes Using Aster Satellite Images at Different Time Points: The Case of Arnavutköy, Istanbul(2026) Maki̇neci̇, Hasan Bilgehan; Çelik, CansuThe use of satellite systems and remote sensing (RS) technologies is rapidly growing. RS is frequently utilized for classifying land use and detecting land cover changes, with resulting thematic maps providing valuable data sources. This study investigates land cover changes in Arnavutköy, a district in northwest Istanbul, from 2000 to 2009. The results, supported by existing literature, show that Arnavutköy has experienced rapid urbanization, particularly since the early 2000s. The evaluation of land cover types in the study focused on urban areas, vegetation/forests, bare soil, and wetlands. Findings indicate a consistent increase in urban areas over the period, while agricultural lands, natural vegetation, and habitats declined. The main factors driving these changes are identified as population growth, transportation and infrastructure development, land speculation, and planning pressures.Article Citation - WoS: 24Citation - Scopus: 34Boundary Constrained Voxel Segmentation for 3d Point Clouds Using Local Geometric Differences(PERGAMON-ELSEVIER SCIENCE LTD, 2020) Sağlam, Ali; Makineci, Hasan Bilgehan; Baykan, Nurdan Akhan; Baykan, Ömer KaanIn 3D point cloud processing, the spatial continuity of points is convenient for segmenting point clouds obtained by 3D laser scanners, RGB-D cameras and LiDAR (light detection and ranging) systems in general. In real life, the surface features of both objects and structures give meaningful information enabling them to be identified and distinguished. Segmenting the points by using their local plane directions (normals), which are estimated by point neighborhoods, is a method that has been widely used in the literature. The angle difference between two nearby local normals allows for measurement of the continuity between the two planes. In real life, the surfaces of objects and structures are not simply planes. Surfaces can also be found in other forms, such as cylinders, smooth transitions and spheres. The proposed voxel-based method developed in this paper solves this problem by inspecting only the local curvatures with a new merging criteria and using a non-sequential region growing approach. The general prominent feature of the proposed method is that it mutually one-to-one pairs all of the adjoining boundary voxels between two adjacent segments to examine the curvatures of all of the pairwise connections. The proposed method uses only one parameter, except for the parameter of unit point group (voxel size), and it does not use a mid-level over-segmentation process, such as supervoxelization. The method checks the local surface curvatures using unit normals, which are close to the boundary between two growing adjacent segments. Another contribution of this paper is that some effective solutions are introduced for the noise units that do not have surface features. The method has been applied to one indoor and four outdoor datasets, and the visual and quantitative segmentation results have been presented. As quantitative measurements, the accuracy (based on the number of true segmented points over all points) and F1 score (based on the means of precision and recall values of the reference segments) are used. The results from testing over five datasets show that, according to both measurement techniques, the proposed method is the fastest and achieves the best mean scores among the methods tested. (C) 2020 Elsevier Ltd. All rights reserved.
Research Topics
Domains
Physical Sciences
Fields
Environmental ScienceEarth and Planetary SciencesEngineering
Subfields
Environmental EngineeringGeologyGlobal and Planetary ChangeEcologyAerospace Engineering
Specific Research Areas
Remote Sensing and LiDAR Applications
3D Surveying and Cultural Heritage
Land Use and Ecosystem Services
Remote Sensing in Agriculture
Robotics and Sensor-Based Localization
Sustainable Development Goals
11SUSTAINABLE CITIES AND COMMUNITIES
11
Research Products
14LIFE BELOW WATER
3
Research Products
13CLIMATE ACTION
2
Research Products
2ZERO HUNGER
2
Research Products
7AFFORDABLE AND CLEAN ENERGY
2
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
2
Research Products
6CLEAN WATER AND SANITATION
2
Research Products
15LIFE ON LAND
1
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
Documents
23
Citations
144
h-index
7
Documents
18
Citations
95
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Konya Technical University | 31 |
| Mersin Üniversitesi | 6 |
| Selçuk University | 4 |
| Ankara Hacı Bayram Veli University | 2 |
| Osmaniye Korkut Ata University | 1 |
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Data obtained from OpenAlex
| Journal | Count |
|---|---|
| Turkish Journal of Remote Sensing | 4 |
| Türkiye Fotogrametri Dergisi | 2 |
| Ain Shams Engineering Journal | 1 |
| Applied Sciences-Basel | 1 |
| ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING | 1 |
Current Page: 1 / 8
Scholarly Output
49
Articles
39
Views / Downloads
112/258
Supervised MSc Theses
3
Supervised PhD Theses
1
WoS Citation Count
81
Scopus Citation Count
120
Patents
0
Projects
0
WoS Citations per Publication
1.65
Scopus Citations per Publication
2.45
Open Access Source
30
Supervised Theses
4
Scopus Quartile Distribution
Competency Cloud
