Building age estimation based on widely available multi-source1 geospatial data

Key points

• Building age is fundamental for urban infrastructure safety, operation, and planning, as it directly affects structural safety, and sustainable development.1
• Existing methods mostly rely on detailed building information such as street view, function, or materials, which are harder to obtain than geospatial data like satellite imagery and Digital Surface Models (DSMs).1
• Moreover, the inherent temporal ordering among building age categories is often overlooked.1
• Pixel-level estimation employs a pyramid decoder with Cosine-guided Mixture-of-Experts (CMoE) to fuse multi-source raster data across scales, capturing fine-grained building age differences, while instance-level prediction uses attention pooling and Property-Conditional Adaptive Instance Normalization (PCAdaIN) to extract instance-level representations and integrate property attributes.1

Sources (1)

• [1]Building age estimation based on widely available multi-source1 geospatial data
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 07:42 AM

  To address these limitations, we propose the first deep-learning framework for multi-building age estimation using widely available multi-source geospatial data, including remote sensing data and their derived products, as well as building footprint information that estimates age at both pixel and instance levels within a unified pipeline.

  Building age is fundamental for urban infrastructure safety, operation, and planning, as it directly affects structural safety, and sustainable development.

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