130 citations · 133 across the 13 of their papers we have counts for
14 papers
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…
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…
3DAeroRelief: The first 3D Benchmark UAV Dataset for Post-Disaster Assessment
Nhut Le, Ehsan Karimi, Maryam Rahnemoonfar
Timely assessment of structural damage is critical for disaster response and recovery. However, most prior work in natural disaster analysis relies on 2D imagery, which lacks depth…
ZeShot-VQA: Zero-Shot Visual Question Answering Framework with Answer Mapping for Natural Disaster Damage Assessment
Ehsan Karimi, Maryam Rahnemoonfar
Natural disasters usually affect vast areas and devastate infrastructures. Performing a timely and efficient response is crucial to minimize the impact on affected communities, and…
RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment
Maryam Rahnemoonfar, Tashnim Chowdhury, Robin Murphy
Recent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise da…
Attention Based Semantic Segmentation on UAV Dataset for Natural Disaster Damage Assessment
Tashnim Chowdhury, Maryam Rahnemoonfar
The detrimental impacts of climate change include stronger and more destructive hurricanes happening all over the world. Identifying different damaged structures of an area includi…
Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment
Maryam Rahnemoonfar, Tashnim Chowdhury, Robin Murphy +1
In this paper, we present a large-scale hurricane Michael dataset for visual perception in disaster scenarios, and analyze state-of-the-art deep neural network models for semantic…