Automated Road and Building Extraction

This study links AI-derived crosswalk marking quality from aerial orthomosaic imagery (2020 and 2023) with pedestrian crashes (2020–2023) and traffic volume (AADT). Intersections are classified by the minimum-direction change in marking quality as Faded, No Change, and Improved. Methodologically, 2023 crash frequency…

Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs. Synthetic data augmentation can extend existing datasets with realistic images, and the quality of these images is generally assessed through fidelity metrics such as…

Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs. Synthetic data augmentation can extend existing datasets with realistic images, and the quality of these images is generally assessed through fidelity metrics such as…

Mask-based paradigms for road topology understanding, such as TopoMaskV2, offer a complementary alternative to query-based methods by generating centerlines via a dense rasterized intermediate representation. However, prior work was limited to 2D predictions and suffered from severe discretization artifacts, necessita…

Mask-based paradigms for road topology understanding, such as TopoMaskV2, offer a complementary alternative to query-based methods by generating centerlines via a dense rasterized intermediate representation. However, prior work was limited to 2D predictions and suffered from severe discretization artifacts, necessita…

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Accurate building segmentation and height estimation from single-view RGB satellite imagery are fundamental for urban analytics, yet remain ill-posed due to structural variability and the high computational cost of global context modeling. While current approaches typically adapt monocular depth architectures, they of…

Accurate building segmentation and height estimation from single-view RGB satellite imagery are fundamental for urban analytics, yet remain ill-posed due to structural variability and the high computational cost of global context modeling. While current approaches typically adapt monocular depth architectures, they of…

Accurate identification of buildings and roads in large-scale satellite imagery is critical for disaster management, urban transformation, and city planning. This study proposes a TDSO (Temporal Detection of Spatial Objects) approach that considers the temporal changes of spatial objects using different-temporal satel…

This study uses historical OpenStreetMap data and geospatial network analysis to examine Ankara, Türkiye, road network growth from 2007 to 2025. Yearly highway snapshots are reconstructed to quantify total network length, annual expansion rates, highway-type composition, and directional growth patterns. Projecting all…

This study uses historical OpenStreetMap data and geospatial network analysis to examine Ankara, Türkiye, road network growth from 2007 to 2025. Yearly highway snapshots are reconstructed to quantify total network length, annual expansion rates, highway-type composition, and directional growth patterns. Projecting all…

Multimodal remote sensing data provide complementary information for semantic segmentation, but in real-world deployments, some modalities may be unavailable due to sensor failures, acquisition issues, or challenging atmospheric conditions. Existing multimodal segmentation models typically address missing modalities b…

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