9 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…
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…
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…
3D Semantic Segmentation for Post-Disaster Assessment
Nhut Le, Maryam Rahnemoonfar
The increasing frequency of natural disasters poses severe threats to human lives and leads to substantial economic losses. While 3D semantic segmentation is crucial for post-disas…
Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessment
Ehsan Karimi, Nhut Le, Maryam Rahnemoonfar
Timely and accurate assessment of damages following natural disasters is essential for effective emergency response and recovery. Recent AI-based frameworks have been developed to…