Case Study: Drone-Based Runway Crack Detection at Dhaka Airport

11 October 2026 case-study drone arcgis-pro deep-learning pavement

Written by M. Mashrur Zaman • GIS & Photogrammetry Specialist

Drone imagery, georeferenced orthophotos and an ArcGIS deep-learning model to map runway pavement cracks at Hazrat Shahjalal International Airport for maintenance prioritization.

Context

A runway pavement assessment for the Civil Aviation Authority of Bangladesh (CAAB) at Hazrat Shahjalal International Airport (HSIA), Dhaka, delivered through Onushandhani Creeds Limited.

The challenge

Runway cracks need to be found and mapped quickly and objectively so maintenance can be prioritized, without long manual inspections on an active runway.

Approach

High-resolution nadir drone imagery of the runway surface
Georeferenced orthophotos as the base for GIS analysis
The ArcGIS Pavement Crack Detection deep-learning model to identify cracks automatically

Results

An automated, map-based crack inventory of the runway surface
A consistent, repeatable basis for maintenance prioritization

The full technical write-up is on this blog: "Runway Crack Detection Using Drones & ArcGIS Pro".