12. Updating and Maintenance of 3D Maps using cost effective IoT Devices and AI Algorithms

Background of Current Process & Challenge Statement

A round of geospatial scanning has been performed nationwide in Singapore to generate a set of 3D Maps. For the maps to be usable, intelligent systems needs to be adopted to keep the maps up-to-date. Available in the market are high-precision positioning/mapping hardware (from Trimble, Leica Geosystems, etc.). These survey-grade, high-precision systems are accurate. But they are not cost effective and require technical skills to operate and perform processing work. In order to achieve scalability across the whole nation, there is a need for technologies that are cost effective and ‘light weight’.


  • Adopting “prosumer” or “consumer” level approach by leveraging on low-cost imaging and sensors hardware (positioning accuracy for data acquisition), apps on mobile devices (routing maps and data collection), and public participation.
  • Leveraging on cloud and network (5G) to transmit data and to perform cost effective analysis.
  • Leverage on computer vision technologies, e.g. Image differencing algorithms, to determine changes since the last update of maps.  Once changes are detected, the 3D Maps and change logfiles are automatically updated.
  • The source of image / video feed to be aggregated from manual 3D scanning work, existing cameras of security systems, traffic monitoring systems, etc.
  • The maps formats adopted include WebGL, CityGML, OGC and IndoorGML.

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