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Visual QA
Rescuenet Damaged Building Detection is an AI-powered tool designed for disaster response and recovery. It uses visual question answering (Visual QA) technology to analyze images and identify damaged buildings. This tool is particularly useful for emergency responders, urban planners, and relief organizations to quickly assess damage after natural disasters or accidents, enabling faster and more efficient rescue operations.
• Damage Detection: Automatically identifies damaged buildings in uploaded images.
• Severity Classification: Classifies the extent of damage (e.g., minor, major, or catastrophic).
• Damage Mapping: Generates maps to visualize the location and extent of damages.
• High Accuracy: Leveraging advanced AI models for precise detection and analysis.
• User-Friendly Interface: Simplified uploading and analysis process for non-technical users.
What types of images can I upload?
You can upload JPEG, PNG, or BMP images. High-resolution images are recommended for better accuracy.
How accurate is the damage detection?
The accuracy depends on the quality of the uploaded image and the complexity of the scene. Under ideal conditions, the tool achieves high accuracy in detecting and classifying damage.
Can I use Rescuenet for real-time monitoring?
Yes, Rescuenet can be used for real-time monitoring in emergency situations. However, internet connectivity and processing time may affect real-time performance.