Mobiusi/Post-Disaster-Ruins-Drone-Aerial-Damage-Assessment-Dataset

Post-Disaster Ruins Drone Aerial Damage Assessment Dataset

The current real estate industry faces an increasing risk of natural disasters and an urgent need for rapid post-disaster assessment and recovery. Existing assessment methods rely heavily on on-site manual inspections, which are inefficient and costly, especially posing personal safety risks in post-disaster harsh environments. The Post-Disaster Ruins Drone Aerial Damage Assessment Dataset aims to enable fast, low-cost, and safe building damage assessments through high-resolution aerial images and precise damage annotations. This dataset is collected from actual post-disaster scenarios, using advanced drone equipment under conditions without personal safety threats. The data undergo multiple rounds of annotation and consistency checks, reviewed by a professional team with architecture and engineering backgrounds to ensure annotation accuracy and consistency. Data preprocessing includes image enhancement, noise filtering, and lighting correction, and is ultimately stored in JPG format, finely organized by region and damage type. The core advantage lies in the high precision and completeness of the data quality, and the use of innovative annotation methods and data augmentation techniques significantly enhances damage recognition accuracy. In application, this dataset can significantly improve post-disaster damage assessment speed and decision-making accuracy, increasing assessment efficiency by at least 30%. Compared to other datasets, this dataset holds unique advantages in diversity and scarcity, demonstrating good scalability, and is suitable for damage assessment in different disaster scenarios.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
damage_extent	string	Represents the level of damage to the building (e.g., minor, moderate, severe).
building_material	string	Identifies the primary material type of the building (e.g., concrete, brick, steel).
roof_condition	string	Used to assess the damage or integrity of the roof.
debris_presence	boolean	Indicates whether there are visible debris or rubble in the image.
floor_count	integer	Identifies the number of floors in the building.
window_intact	boolean	Assesses whether the windows remain intact.
wall_status	string	Assesses the condition of the walls.
vegetation_obstruction	boolean	Indicates whether vegetation is obstructing parts of the damaged building.

Compliance Statement

Authorization Type	CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com