130 citations · 133 across the 13 of their papers we have counts for
7 papers · 1 filter
PT-WNO: Point Transformer with Wavelet Neural Operator for 3D Point Cloud Semantic Segmentation
Nhut Le, Maryam Rahnemoonfar
Point cloud semantic segmentation requires architectures that capture both fine-grained local geometry and broad global scene structure. Transformer-based networks have demonstrate…
OPTNet: Ordering Point Transformer Network for Post-disaster 3D Semantic Segmentation
Nhut Le, Ehsan Karimi, Maryam Rahnemoonfar
Post-disaster damage assessment requires rapid and accurate semantic segmentation of 3D point clouds to identify critical infrastructure such as damaged buildings and roads. Early…
Instruct-ICL: Instruction-Guided In-Context Learning for Post-Disaster Damage Assessment
Armin Zarbaft, Ehsan Karimi, Nhut Le +1
Rapid and accurate situational awareness is essential for effective response during natural disasters, where delays in analysis can significantly hinder decision-making. Training t…
DA-SegFormer: Damage-Aware Semantic Segmentation for Fine-Grained Disaster Assessment
Kevin Zhu, William Tang, Raphael Hay Tene +3
Rapid and accurate damage assessment following natural disasters is critical for effective emergency response. However, identifying fine-grained damage levels (e.g., distinguishing…
PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulation
Zesheng Liu, Doyup Kwon, Ning Lin +1
Accurate and efficient storm-surge emulation is essential for coastal hazard assessment, yet high-fidelity hydrodynamic models remain too expensive for large scenario ensembles and…
Geometric Flood Depth Estimation: Fusing Transformer-Based Segmentation with Digital Elevation Models
Nhut Le, Ehsan Karimi, Maryam Rahnemoonfar
Post-disaster situational awareness relies heavily on understanding both the extent and the volume of floodwaters. While 2D semantic segmentation provides accurate flood masking, i…
K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data
Zesheng Liu, Maryam Rahnemoonfar
Subsurface stratigraphy contains important spatio-temporal information about accumulation, deformation, and layer formation in polar ice sheets. In particular, variations in intern…