6 papers
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…
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…
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…