Dursun Zafer Seker
Biographic Data
| ID | 182573 |
|---|---|
| NAME | Dursun Zafer Seker |
| GIVEN NAMES | Dursun Zafer |
| FAMILY NAME | Seker |
| SIGNATURE | SEKER D Z |
| AFFILIATIONS | Istanbul Technical University |
| ORCID | 0000-0001-7498-1540 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 4 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
Impact of Synthetic Data on Deep Learning Models for Earth Observation: Photovoltaic Panel Detection Case Study
This study explores the impact of synthetic data, both physically based and generatively created, on deep learning analytics for earth observation (EO), focusing on the detection of photovoltaic panels. A YOLOv8 object detection model was trained using a publicly available, multi-resolution very high resolution (VHR) EO dataset (0.8 m, 0.3 m, and 0.1 m), comprising 3716 images from various locations in Jiangsu Province, China. Three benchmarks we…
GIS and remotely sensed data-based morphometric elements analysis for determination of Bengal Basin evolution
D modeling of historical measurement instruments using photogrammetric and laser scanning techniques
Machine Learning-Based Supervised Classification of Point Clouds Using Multiscale Geometric Features
3D scene classification has become an important research field in photogrammetry, remote sensing, computer vision and robotics with the widespread usage of 3D point clouds. Point cloud classification, called semantic labeling, semantic segmentation, or semantic classification of point clouds is a challenging topic. Machine learning, on the other hand, is a powerful mathematical tool used to classify 3D point clouds whose content can be significan…
Location-Based Analyses for Electronic Monitoring of Parolees
This study analyses the spatio-temporal pattern of parolees using electronic monitoring, where the developed spatial framework supports the Environmental Criminology concepts such as crime patterns or crime attractive locations. A grid-based solution for spatio-temporal analyses is introduced to ensure the anonymity of the parolees. In order to test these developed concepts, the Istanbul Metropolitan Area was selected as the pilot study area. Fol…
Implementation of ultra-light UAV systems for cultural heritage documentation
The Impact of Dust and Sandstorms in Increasing Drought Areas in Nineveh Province, North-western Iraq
According to many local and international reports, the risk of dust and sandstorms has increased significantly in Iraq, creating serious environmental and social problems. In this study Nineveh province was selected as the study area to investigate the relationship between the increase in such storms and drought expansion. In the study, storm-feeding regions and a probable storm path were detected using remote sensing and Geographic Information S…
The Impact of Dust and Sandstorms in Increasing Drought Areas in Nineveh Province, North-western Iraq
According to many local and international reports, the risk of dust and sandstorms has increased significantly in Iraq, creating serious environmental and social problems. In this study Nineveh province was selected as the study area to investigate the relationship between the increase in such storms and drought expansion. In the study, storm-feeding regions and a probable storm path were detected using remote sensing and Geographic Information S…
Location-Based Analyses for Electronic Monitoring of Parolees
This study analyses the spatio-temporal pattern of parolees using electronic monitoring, where the developed spatial framework supports the Environmental Criminology concepts such as crime patterns or crime attractive locations. A grid-based solution for spatio-temporal analyses is introduced to ensure the anonymity of the parolees. In order to test these developed concepts, the Istanbul Metropolitan Area was selected as the pilot study area. Fol…
Implementation of ultra-light UAV systems for cultural heritage documentation
Machine Learning-Based Supervised Classification of Point Clouds Using Multiscale Geometric Features
3D scene classification has become an important research field in photogrammetry, remote sensing, computer vision and robotics with the widespread usage of 3D point clouds. Point cloud classification, called semantic labeling, semantic segmentation, or semantic classification of point clouds is a challenging topic. Machine learning, on the other hand, is a powerful mathematical tool used to classify 3D point clouds whose content can be significan…
GIS and remotely sensed data-based morphometric elements analysis for determination of Bengal Basin evolution
D modeling of historical measurement instruments using photogrammetric and laser scanning techniques
Impact of Synthetic Data on Deep Learning Models for Earth Observation: Photovoltaic Panel Detection Case Study
This study explores the impact of synthetic data, both physically based and generatively created, on deep learning analytics for earth observation (EO), focusing on the detection of photovoltaic panels. A YOLOv8 object detection model was trained using a publicly available, multi-resolution very high resolution (VHR) EO dataset (0.8 m, 0.3 m, and 0.1 m), comprising 3716 images from various locations in Jiangsu Province, China. Three benchmarks we…
Geography (5 works) · Computer Science (4 works) · 3D Surveying and Cultural Heritage (3 works) · Artificial Intelligence (3 works) · Cartography (3 works) · Remote sensing (3 works) · Remote Sensing and LiDAR Applications (3 works) · Archaeological Research and Protection (2 works) · Archaeology (2 works) · Cultural heritage (2 works)