Müslüm Hacar
Biographic Data
| ID | 9966784 |
|---|---|
| NAME | Müslüm Hacar |
| GIVEN NAMES | Müslüm |
| FAMILY NAME | Hacar |
| SIGNATURE | HACAR M |
| AFFILIATIONS | Yıldız Technical University |
| ORCID | 0000-0002-8737-8262 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
LLM-Based Map Conflation
Map conflation is essential for integrating heterogeneous road datasets, but it often requires region- and data-specific algorithm design to automate the complex identification of feature-to-feature correspondences. This effort is increased when only cartographic products are available instead of GIS-ready vectors since both digitization or matching corresponding features manually are labor-intensive. In this study, we assess the performance of a…
Network-Based Hierarchical Feature Augmentation for Predicting Road Classes in OpenStreetMap
The need to enrich the semantic completeness of OpenStreetMap (OSM) data is crucial for its effective use in geographic information systems and urban studies. Addressing this challenge, our research introduces a novel hierarchical feature augmentation approach to developing machine learning classifiers by the features retrieved from various levels of road network connectivity. This method systematically augments the feature space by incorporating…
Analyzing the Behaviors of OpenStreetMap Volunteers in Mapping Building Polygons Using a Machine Learning Approach
Mapping as an action in volunteered geographic information is complex in light of the human diversity within the volunteer community. There is no integrated solution that models and fixes all data heterogeneity. Instead, researchers are attempting to assess and understand crowdsourced data. Approaches based on statistics are helpful to comprehend trends in crowd-drawing behaviors. This study examines trends in contributors’ first decisions when d…
No prominent works on this page.
Analyzing the Behaviors of OpenStreetMap Volunteers in Mapping Building Polygons Using a Machine Learning Approach
Mapping as an action in volunteered geographic information is complex in light of the human diversity within the volunteer community. There is no integrated solution that models and fixes all data heterogeneity. Instead, researchers are attempting to assess and understand crowdsourced data. Approaches based on statistics are helpful to comprehend trends in crowd-drawing behaviors. This study examines trends in contributors’ first decisions when d…
Network-Based Hierarchical Feature Augmentation for Predicting Road Classes in OpenStreetMap
The need to enrich the semantic completeness of OpenStreetMap (OSM) data is crucial for its effective use in geographic information systems and urban studies. Addressing this challenge, our research introduces a novel hierarchical feature augmentation approach to developing machine learning classifiers by the features retrieved from various levels of road network connectivity. This method systematically augments the feature space by incorporating…
LLM-Based Map Conflation
Map conflation is essential for integrating heterogeneous road datasets, but it often requires region- and data-specific algorithm design to automate the complex identification of feature-to-feature correspondences. This effort is increased when only cartographic products are available instead of GIS-ready vectors since both digitization or matching corresponding features manually are labor-intensive. In this study, we assess the performance of a…
Geographic Information Systems Studies (3 works) · Artificial Intelligence (2 works) · Automated Road and Building Extraction (2 works) · Computer Science (2 works) · Data Management and Algorithms (2 works) · Adjacency list (1 work) · Centroid (1 work) · Conflation (1 work) · Data mining (1 work) · Data science (1 work)