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Use Case | Locating defects of the road surface

Case studies

One of the goals was to filter small single cracks not affecting significantly road quality and only pay attention to bigger damage areas.

Road maintenance is a complex task requiring:
  • Localization of defect areas
  • Counting approximate area sizes for repair
  • Planning sufficient but not excessive material and human resources


Process:
 - Manual annotation: approx. 15 minutes
 - Initial machine learning detector's training in ATLAS cloud: approx. 50 minutes

Result:
  • Shareable web-map with marked defects in ATLAS
  • Report with georeferenced areas in GeoJSON

Here is an example of how areas with cracked asphalt can be identified with Atlas: CASE#1

Client: G2 INGENIERIA PARA LA CONSTRUCCIÓN S.A. DE C.V. 
Country: Mexico
Industry: Civil Engineering, Construction & Maintenance