14 papers
Benchmarking the Alignment of Data-Quality Metrics, Human Judgment and Land-Cover Segmentation Performance for Earth Observation
Ãmit Mert ÃaÄlar, Alptekin Temizel
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…
BELDE: Building a Large-scale Earth-observation Land-cover Dataset for Europe
Ãmit Mert ÃaÄlar, Alptekin Temizel
Earth observation imagery plays a critical role in environmental monitoring, urban planning, disaster assessment, and climate analysis. While multi-spectral sensors are increasingl…
LALE: Lightweight-Transformer Architecture for Land-Cover Estimation
Ãmit Mert ÃaÄlar, Alptekin Temizel
Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets. Prior work typically optimizes…
SenBen: Sensitive Scene Graphs for Explainable Content Moderation
Fatih Cagatay Akyon, Alptekin Temizel
Content moderation systems classify images as safe or unsafe but lack spatial grounding and interpretability: they cannot explain what sensitive behavior was detected, who is invol…
Disentangled Anatomy-Disease Diffusion (DADD) for Controllable Ulcerative Colitis Progression Synthesis
Umut Dundar, Alptekin Temizel
Synthesizing longitudinal medical images at controllable disease stages while preserving patient-specific anatomy is hindered by the entanglement of pathological textures and struc…
Grounding Synthetic Data Generation With Vision and Language Models
Ãmit Mert ÃaÄlar, Alptekin Temizel
Deep learning models benefit from increasing data diversity and volume, motivating synthetic data augmentation to improve existing datasets. However, existing evaluation metrics fo…