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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.[14] 2) Engage effectively with local people in local governance and decision by use of open innovation processes and e-participation[15] with emphasis placed on citizen participation and co-design.[16][17] 3) Learn, adapt and innovate and thereby respond more effectively and promptly to changing circumstances.[18]
Presently, the Smart City construct would fall in the "early adopters" stage in the evolutionary cycle. That being said, having monitored and researched the progression of smart cities around the world, it is very encouraging to see maturing of the movement, as well as the regular fusion of new technologies.
Many "Smart Cities" in Europe and the US have started creating highly precise & interconnected GIS maps using 3D survey technologies. GIS or Geographic Information Systems use computer-based systems to link maps and databases, creating a relationship between data, location, and information. GIS serves as the information back bone for data-driven smart cities. However, the tools for creating GIS so far, such as Satellite Imagery, Handheld GPS and Static Lasers are inaccurate, non-spatial, slow and limited in their scale of vision. As an alternative to that, here is a scalable approach to create highly accurate, spatial, fast and sophisticated cloud based 3D-GIS maps, using Smart GIS. The plan is to overlay multiple layers of data–computed and analysed on these maps, ultimately connecting it to the various stakeholders of the city.
At the go, the idea sounds as fantastical as narrated by a science-fiction story, like the Matrix trilogy, where the city itself is a virtual platform! However, we are not going that far. We simply propose to create a virtual urban platform connecting the various players in the city via media networks. We propose to do so by overlaying precise up-to date 3-D renderings of city assets to its information and analyses and connecting it to the concerned urban actors via electronic networks. Different layers of information linked to different urban actors and stakeholders. The idea so far is simple–Scan the city, measure it, connect it and manage it!
Nexus of disruptive technologies like distributed computing, deep learning & machine vision algorithms, cloud computing and advanced laser sensing (LiDAR) enable high resolution–aerial and ground surveys of cities to create highly precise 3-D, point-based, spatial maps. Deep learning techniques can rapidly process peta-bytes of survey data within hours to extract asset information maps.
GIS maps, when integrated with other layers of city information and networking systems, present a huge opportunity for creating multiple data driven Smart City Platforms involving different stakeholders–the government, private sector, and the civil society. These layers can include building footprint locations, address information, energy consumption, street line markings, railroad and metro rail assets, waterway and wetland areas, surface and volume analysis of roadways, encroachments, neighborhood boundaries, election wards and districts, zoning boundaries, green area cover, etc.
In Chicago, ComEd provides electricity to 3.8 million customers and manages 90,000 miles of power lines in an 11,400-square-mile territory. CityScan is piloting mobile LiDAR to measure the tilt of power poles, inventory company and 3rd party equipment on the polls, assess wire sag, and identify tree encroachment. With this information, ComEd can make informed decisions about where to focus resources to protect Chicago’s electricity infrastructure.
For many cities, street infrastructure is the biggest asset. On the west coast, a mid-sized city is planning a comprehensive sidewalk assessment. The original plan was to spend $1 million to have engineering students walk the entire sidewalk network, evaluating damage by hand. The process would have taken up to a year. With mobile terrestrial LiDAR, the city could have results in a month and with precise damage data like how deep a sidewalk crack is down to the centimeter.
The applications are almost endless–from identifying illegal building conversions and overcrowding, to post-disaster damage assessments, to computing energy modelling of built environment, to comprehensive fire risk assessments combining external LiDAR scans with city records. Interfacing mobile terrestrial LiDAR with municipal data is the newest innovation in proactive enforcement and infrastructure management. This innovation will enable cities to cut costs, recover lost revenues, reduce corruption, increase accountability and deliver services proactively by creating accuracy, efficiency and transparency in the systems.
Zurich, Edmonton, Barcelona, Berlin, Paris and many more cities across developed countries aim to be a prototype for such data-sensing "smart cities". These applications will be of more use in developing countries than developed countries, where the urban concerns on corruption, public funds management, city infrastructure and utilities are extreme.
Large scale deployment and frequent audits will be possible through CivilMaps’ deep learning architecture and the sharing of reports with different stakeholders in the city will be enabled by CivilMaps cloud visualizer. It’s just around the corner…
References
Online resources on Smart Cities, such as Data Smart City Solutions, Harvard University, MIT–Smart City Grid project, Wikipedia and various internet sources
Research Paper titled "geoSmartCity: geomatics contribution to Smart City" by Daniel-Doran, Universite Laval, Quebec City, Canada
Anuj is the VP Business Development at CivilMaps. His work involves research in business and deep learning techniques.
A 2.298Mb PDF of this article as it appeared in the magazine complete with images is available by clicking HERE