Moving towards creating virtual urban-platforms, using 3D GIS maps of cities
By Abhirajika Agrawal & Anuj Gupta
Millions of dollars are lost to inefficient operation of city infrastructure and resources, like illegal construction, damaged roads, power line damages, disputes on land, city floods during rains and so on. The problem is inherently about scale – how can a city with limited enforcement and maintenance staff track and monitor every billboard, every construction site, or every inch of public infrastructure? They simply can’t, it is cost prohibitive to track micro changes in the built environment. But imagine an up-to-date, interactive 3D rendering of a city, accurate down to the millimetre- as is generated by SMART GIS. In this article, we present a scalable framework to create sophisticated 3-D GIS maps of cities, using SMART GIS, which will enable cities to create virtual urban-utility platforms, that will lead to resource optimization, direct governance, participatory operations of civil systems, etc, as envisioned for a “data- driven Smart city”.
It has been suggested that a smart city (also community, Business cluster, urban agglomeration or region) use information technologies to: 1) Make more efficient use of physical infrastructure (roads, built environment and other physical assets) to support a strong and healthy economic, social, cultural development. 2) Engage effectively with local people in local governance and decision by use of open innovation processes and e-participation with emphasis placed on citizen participation and co-design. 3) Learn, adapt and innovate and thereby respond more effectively and promptly to changing circumstances.
Smart GIS, developed by CivilMaps, is enabled by a Nexus of disruptive technologies like distributed computing, deep learning & machine vision algorithms, cloud computing and advanced laser sensing (LiDAR). These technologies enable high resolution – aerial and ground surveys of cities to create highly precise 3-D, point-based, spatial maps. Smart GIS utilizes deep learning techniques to rapidly process peta-bytes of survey data within hours.