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Google AI Introduces the Open Buildings 2.5D Temporal Dataset that Tracks Building Changes Across the Global South

Google AI Introduces the Open Buildings 2.5D Temporal Dataset that Tracks Building Changes Across the Global South

Practical Solutions and Value of Google’s Open Buildings 2.5D Temporal Dataset

Challenges Addressed:

Governments and organizations lack timely and accurate data on building changes, hindering urban planning and crisis response efforts.

Practical Solution:

Google’s dataset uses Sentinel-2 satellite imagery to estimate building changes globally every five days, enhancing accuracy and coverage.

Key Features:

  • Utilizes machine learning to track building presence and height changes over time.
  • Employs HRNet architecture-based models for accurate building footprint detection.
  • Achieves 78.3% mean Intersection over Union (IoU) accuracy, close to high-resolution imagery.
  • Offers tools for estimating building heights with 1.5 meters mean absolute error and reliable building count predictions.

Value Proposition:

The dataset significantly advances building change detection in the Global South, enabling better urban planning and crisis response through innovative use of public satellite imagery and machine learning models.

List of Useful Links:

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Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

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