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[P] Finding structures vulnerable to disaster in street view imagery

[P] Finding structures vulnerable to disaster in street view imagery

My company is supporting the World Bank’s program focused on resilient housing. Housing “resiliency” is a problem in poor urban areas around the world as these homes are often not up to code (e.g., because families are doing their own house construction or good materials aren’t available). This makes the homes (and the families inside) vulnerable to disasters like earthquakes and hurricanes. Many governments in these countries have resources to retrofit the houses for safety, but it’s inefficient for structural engineers to walk up and down neighborhoods to identify the vulnerable homes that need fixing.

We just finished some pilot work to identify building features that could confer risk. The features we detected here were

  1. building material
  2. whether or not a building looked designed by an architect
  3. and whether or not construction appeared complete.

This is definitely more application focused, so we just used TF’s object detection API under the hood w/ an SSD backbone and trained on ~7000 labeled street view images. The fun part was relating street view detections to an overhead building footprint map — something I talk about a lot in the blog. (Does anyone know how Google or other tech companies do this?) We’re working on releasing that training data if anyone else is interested in this problem space.

One related question: does anyone know if TF OD API will change in TF 2.0?

Edit: adding gif example

Processing gif afv9d79i78x21…

submitted by /u/wronk17
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.