🚀 Mapflow Buildings Model: Now with Estimated 🔝Heights (Beta)

We’ve rolled out a major update to the default Mapflow Buildings model: you can now estimate building heights directly from imagery. This new feature leverages a dedicated regression-based model that infers height using visual indicators such as shadow length and visible wall segments.

The result is what they call 3D building footprints—where the building's contour is projected to ground level instead of the roof outline. This is especially useful for oblique imagery, where roofs often appear shifted.

You can enable this feature in the default workflow. It’s available across all interfaces: Mapflow Web, QGIS plugin, and Mapflow API.

TL;DR: What to Know About 3D Building Footprints

– Lidar and stereo-pair imagery offer higher precision for 3D modeling, but they require specialized, often costly data and processing pipelines.

– Mapflow’s new method trades a bit of precision for high scalability and minimal input requirements—ideal for large-scale applications where budget and speed matter.

– For enterprise use cases like telco infrastructure planning, we offer additional accuracy tuning using satellite metadata (e.g. sensor angle). It's like restoring the building's geometry out from the known projections and helps us to achieve a mean absolute error (MAE) just over 3 meters—about one floor of a typical residential building.

This approach works without stereo input or metadata, making it especially cost-effective and accessible.

While we continue gathering benchmark data and performance metrics, we will appreciate your feedback. Test it and find out the application to your particular tasks. If the output doesn’t meet expectations, let us know—we monitor ⭐ Mapflow rates and comments closely and offer refunds for poor results. Your input helps us prioritize improvements and tailor the tool to your needs.