Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Akbulut, Zeynep(3)
- Aliyazıcıoğlu, Şener(2)
- Günen, Mehmet Akif(2)
- Karsli, Fevzi(2)
- Özdemir, Samed(2)
- Alemdag, Selçuk(1)
- Bakirman, Tolga(1)
- Bayram, Bulent(1)
- Bilgilioglu, Burhan Baha(1)
- Bilgilioğlu, Burhan Baha(1)
- Cetin, Ibrahim(1)
- Dihkan, Mustafa(1)
- Ozkan, Taskin(1)
- Reis, Hatice Catal(1)
- Seker, Dursun Zafer(1)
- Yavuzdoğan, Ahmet(1)
- Zeybek, Halil Ibrahim(1)
- Çömert, Resul(1)
- Öztürk, Kaşif Furkan(1)
Yayınlar
- MakaleAçık Erişim2025Scopus SJRQ1DAPNet++: density adaptive PointNet + + for airborne laser scanning data
Akbulut, Zeynep; Karsli, Fevzi
Addressing the challenges arising from the irregularity and varying density of Airborne Laser Scanning (ALS) point clouds, which particularly affect the performance and generalization ability of 3D Deep Neural Networks (DNNs), is essential for their effective application in direct semantic segmentation tasks. In this study, we investigated the underexplored aspect of adapting PointNet + + for semantic segmentation of ALS point clouds. We introduced Density-Adaptive PointNet++ (DAPNet++) to enhance robustness against variable point densities through modifications made to the original PointNet + + architecture. Our methodology includes controlled block partitioning based on point density replacing the original batching strategy. Another key advancement in the study is the automation of the initial value of the neighborhood search radius by taking into account the characteristics of the dataset. This approach optimizes receptive field determination, crucial for effective semantic segmentation. The effectiveness of DAPNet + + is validated through extensive experiments on various datasets, including ISPRS Vaihingen, DALES, subsampled DALES, and OpenGF. Notable improvements include up to 11% increase in weighted mean Intersection-over-Union (mIoU) on the highly variable OpenGF test dataset and 3% increase in mIoU on the subsampled DALES dataset. Furthermore, the generalization capability of the DAPNet + + was tested, revealing an approximately 5% improvement in evaluation metrics compared to PointNet++. In summary, DAPNet + + minimizes trial-and-error in the selection of parameters for block partitioning and radius, and it enhances robustness against variable point density. With the proposed approach, it has been observed that there is a significant improvement in accuracy for underrepresented classes, mitigating class imbalance in ALS point clouds.
AtıfScopus9WoS5OpenAlex6
İncele - MakaleYalnız künye2025Scopus SJRQ1Discontinuities identification from rock outcrop using auto-encoder and point clouds
Günen, Mehmet Akif; Aliyazıcıoğlu, Şener
Rock slopes and geotechnical structures often exhibit discontinuity planes, which significantly influence their mechanical behavior and stability. The precise identification of these discontinuities is critical for ensuring the safety and cost-effectiveness of construction projects. However, previous methods are often limited by subjectivity, low automation, and challenges in inaccessible or hazardous environments. This study introduces a novel seven-step approach utilizing auto-encoders for estimating the orientation parameters of individual discontinuity sets from point cloud data. The steps include: data collection, filtering point clouds to eliminate outliers, determining neighborhood size, feature extraction, training a stacked auto-encoder for feature learning, estimating discontinuity sets using density-based clustering, and calculating individual discontinuities through the least squares method for orientation parameter estimation. In addition, the methodology incorporates a planarity-based approach for estimating neighborhood size. The statistical performance has been validated through evaluations in two real case studies and one synthetic case study. The classification achieves an accuracy exceeding 95%, highlighting the high efficacy of the approach. Results for orientation parameter estimation show consistency with existing methods and in situ measurements, with average orientation values for discontinuities within 5 degrees for individual sets. This framework represents a considerable advancement in rock discontinuity analysis, offering a robust and automated solution that is particularly effective in hazardous and hard-to-reach areas.
AtıfScopus10WoS10OpenAlex11İncele - MakaleAçık Erişim2025Scopus SJRQ1Supervised classification-based framework for rock mass discontinuity identification using point cloud data
Günen, Mehmet Akif; Öztürk, Kaşif Furkan; Aliyazıcıoğlu, Şener
Mapping and evaluating rock mass discontinuities using point clouds is a critical task in mining, civil, and geological engineering. Rock discontinuities can significantly impact the integrity, strength, and stability of rock masses. The orientation of these discontinuities is also a key characteristic of the rock mass. Accurate orientation estimation from point clouds enables more precise predictions of rock mass behavior, leading to improved safety, more efficient excavation processes, reduced operational costs, and significant time savings. In this context, a supervised classification-based framework is proposed for calculating orientation parameters from point cloud. Supervised classification plays a crucial role in tasks where a model learns complex patterns from labeled data to accurately predict previously unseen instances. The proposed method consists of eight-steps, including: data collection, pre-processing (data filtering), adaptive neighborhood size selection (omnivariance-based), feature extraction (geometric features), feature selection (Minimum Redundancy Maximum Relevance method), classification (Support Vector Machine), clustering (connected component labeling), and plane fitting to calculate orientation parameters (dip angle and dip direction). The framework was applied to two real-world datasets and one synthetic dataset, which was tested in two different subsampled forms (random and uniform subsampling). The results statistically demonstrated that the technique was effective in detecting and characterizing rock mass discontinuities with high Accuracy, Recall, Precision, and F-Score values ranging from 94.64% to 99.57%. The deviations of the method in the measurements of the dip angle and the dip direction, compared to the manual measurements, range from 1% to 4%, indicating strong agreement with the manual measurements and the existing studies.
AtıfScopus25WoS24OpenAlex26İncele - MakaleYalnız künye2024Scopus SJRQ2A New Approach for Ground Filtering of Airborne Laser Scanning Data Using PointNet++
Akbulut, Zeynep; Karsli, Fevzi; Dihkan, Mustafa
Ground filtering is an essential process that separates ground and non-ground points for generating digital elevation models (DEMs) from airborne laser scanning (ALS) point cloud data. Traditionally, ground filtering was carried out using rule-based approaches, but with the advancements in deep learning networks, ground filtering can now be performed using these methods. Deep neural networks (DNNs) require large amounts of training samples to reveal key information within the dataset. OpenGF is a new ultra-large-scale dataset which has nearly half a billion points from nine different scenes ranging from highly dense urban areas to steep vegetation covered mountains for ground filtering task. In this study, PointNet++ architecture was employed for ground filtering on the OpenGF dataset. Firstly, we slightly modified PointNet++ architecture’s sampling strategy to decrease the number of duplicate points in the batching process. We proposed a new adaptive tiling strategy based on input point cloud density to cope with highly varying point sampling of the OpenGF dataset. Modified PointNet++ architecture was trained with both adaptive tiling strategy and original OpenGF tiles to investigate the advantages and drawbacks of the proposed strategy on the ground filtering task. To evaluate the performance of the proposed strategy, the results for different test areas were compared with those obtained using the original PointNet++ and rule-based cloth simulation filtering (CSF) algorithms. Despite using fewer epochs, the proposed data collection strategy achieved up to 13% higher accuracy on test scenes with the modified PointNet++ architecture compared to the network trained with original OpenGF tiles.
AtıfScopus2WoS1OpenAlex2İncele - MakaleAçık Erişim2023Scopus SJRQ43D data integration for geo-located cave mapping based on unmanned aerial vehicle and terrestrial laser scanner data
Çömert, Resul; Özdemir, Samed; Bilgilioglu, Burhan Baha; Alemdag, Selçuk; Zeybek, Halil Ibrahim
The Akçakale cave is a significant natural and cultural heritage site in the Black Sea region of eastern Turkey. The complex geometry and difficult-to-access areas of the cave have made the use of traditional mapping methods challenging. To overcome these limitations, this study utilized TLS and UAV technology to produce highly accurate 2D and 3D data for cave management and risk assessment purposes. The TLS system was used to create a detailed 3D point cloud of the cave interior, while the UAV system generated a 3D model of the surface topography outside the cave. The two sets of data were combined in the GIS environment using a geodetic network established in the study area, providing a common geodetic reference system for both TLS and UAV data. The study found that the cave area is 13,750 m2, which is smaller than the area of 18,000 m2 that was previously estimated using conventional measurement methods. The volume and ceiling heights of the cave were calculated using the elevation models generated from TLS point cloud data. The 3D point cloud data were also used to map dripstone locations on the floor and ceiling of the cave, and the boundaries of rock blocks on the ground were precisely determined. The study identified potential risks associated with the cave, particularly the risk of rockfall in the source rock areas around the cave entrance and the southern part of the cave. The nearest building to the cave is approximately 35 meters away, and all the buildings in the area are less than 300 meters from the cave. In the event of the cave collapse, the buildings in the southern part of the cave are at risk of rockfall. This study demonstrates the effectiveness of combining data from TLS and UAV systems to generate broad and sensitive cave mapping and risk assessment data, which are critical for cave management and safety. The collected data can be used for cave stability investigations and rockfall risk assessments. This study provides a foundation for future explorations of the Akcakale cave and highlights the potential for modern surveying techniques to enhance our understanding of complex geological structures such as caves.
AtıfScopus4WoS3OpenAlex4İncele - MakaleYalnız künye2023Scopus SJRQ1SPAN: An open-source plugin for photovoltaic potential estimation of individual roof segments using point cloud data
Özdemir, Samed; Yavuzdoğan, Ahmet; Bilgilioğlu, Burhan Baha; Akbulut, Zeynep
Decentralized solar PhotoVoltaic (PV) is one of the most promising energy sources for cities and individuals pursuing energy self-sufficiency. Especially, the already available rooftop surfaces are a major contributor to push for rooftop mounted PV systems. However, accurate PV potential estimation of individual buildings is still a challenging task since many parameters must be considered such as meteorological factors, panel technology, geographical location, available roof surface area, surface azimuth and tilt angle. In this study, we created an efficient approach that can be used for roof surface's PV potential estimation based on point cloud data and capable of processing various scales from single building to city scale. In the proposed approach, each roof surface's features were utilized for PV potential estimation by employing the PVGIS database. PV potential estimation was carried out on daily, monthly, and annual periods to provide a better estimation. Also, we developed a flexible and easy to use open-source plugin based on the QGIS software for rooftop mounted PV potential estimation capable of estimating every roof surface's PV potential. The method was tested on 80 buildings selected from ROOFN3D dataset. The proposed approach achieved an overall accuracy of 84% and an F1 score of 0.92.
AtıfScopus21WoS18OpenAlex20İncele - MakaleYalnız künye2018Scopus SJRQ4Open source library-based 3D face point cloud generation
Bayram, Bulent; Ozkan, Taskin; Reis, Hatice Catal; Bakirman, Tolga; Cetin, Ibrahim; Seker, Dursun Zafer
Three dimensional (3D) face and body modeling is widely used in various fields such as plastic surgery, diagnosis of facial or body anomalies, 3D computer games and 3D simulation software. Since, commercial 3D face and body scanners are usually expensive, an alternative solution with lower cost is highly desirable. The objective of this study is to create 3D facial point cloud using Semi Global Image Matching method with minimum number of images utilizing a cost effective method. A non-metric Canon 600D camera with 18 megapixels resolution (3456 × 5184) and 60 mm macro lens have been used for face imaging that have been taken from a distance of 120 cm. Five faces have been modeled by the developed algorithm and scanned by David SLS-2 structured light system for accuracy assessment. Open source Cloud Compare software has been used for comparing the results of proposed method with the structured light system. The mean accuracy of five faces obtained as 90.5%. It has been observed that illumination conditions, uncontrolled movements of face or body, hair and eyebrow have negative impacts on the obtained results. The sufficiency of Semi global image matching method has been tested to create dense point cloud data from three stereo pairs for 3D facial modelling.
AtıfScopus3WoS2İncele