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Found 4 papers, 2 papers with code
RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment
Recent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise damage assessment.
Attention Based Semantic Segmentation on UAV Dataset for Natural Disaster Damage Assessment
The detrimental impacts of climate change include stronger and more destructive hurricanes happening all over the world.
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
This dataset demonstrates the post flooded damages of the affected areas.
Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment
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 segmentation.
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