Highlighting the challenges that smart cities in the Asia Pacific region are facing, David Jonas, Project Manager, AAM Group reiterates the need for data collection to be backed by government agencies.
As countries across the Asian region experience a dramatic increase in urbanisation, authorities are turning to technology to improve the efficiency of the urban environments. Terms such as ‘smart city’ and ‘virtual city’ describe the goal to better manage the urban environment and to increase the efficiency of managing cities that are growing in every possible way. The term ‘smart city’ has a different meaning to different people. However, a key session at the recently concluded Smart Cities India Expo, in Delhi, offered a relatively useful definition —
A smart city effectively delivers public services to citizens and businesses wherever they may be located, in an integrated and resource efficient way while enabling innovative collaborations to improving quality of life, ensuring safety, reducing impact on environment, growing the local and national economy.
Platforms like these (the expo) display an impressive array of processes, sensors, software and services to instigate components of this definition. As Asia looks to deploy these processes in its drive toward smart cities, lack of suitable data is a serious limitation that is becoming apparent.
What is a Smart city?
Most of the current smart city processes originate from Europe or the US. The processes in these regions already have ready access to the spatial and textual data for modelling, computations or to display components of their systems. Highly detailed terrain models are available ‘off-the-shelf’ to assist processes dependent upon gravity or ground shape while the city models are available to define the urban environment. These can be commissioned as well with relative ease owing to the relatively low pricing and abundance of competent suppliers, the proximity of suitable resources, and the efficiency of modern technology. Unfortunately, in Asia-Pacific, the availability of suitable data is extremely limited. The last decade saw lean demand for such data, hence the drivers to collect and archive the data did not exist. Government legislation often limits the collection and distribution of “off-the-shelf” data. Data collection must be bespoke and must be backed by a government agency, so to say.
Data sources
Different authorities in the Asia-Pacific region are tacklingthe data-component of their smart city implementation differently. Of critical importance is to carefully review the various data sources available and to deploy those techniques best suited for the project and site parameters.There are 10 typical sources of data — see the table — which describe their contribution to smart cities in the Asia-Pacific region.
Case study: Virtual Singapore
Singapore is a good example of how cities within the Asia Pacific region are largely devoid of the spatial data. Although known as a high-tech city, Singapore had very little data available when Singaporean leaders issued the challenge to make the city-state a Smart city — Virtual Singapore. Singapore Land Authority (SLA) embarked on a structured approach to acquire the data to support this vision. SLA needed to provide authoritative spatial data layers to support the diverse range of uses where the 3D data layers were to be applied. Government departments need the survey rigour upon which they base their analyses and decisions in order to support sound and legally defendable actions since they also need a high degree of realism to convey the Smart city outputs to the public.
Data Source: Contribution to Asia-Pacific smart cities
1. Existing Data: Few cities have sufficient quality and quantity of spatial data to support smart city applications, with the exception of Hong Kong, Australia and New Zealand. Many cities are now developing road networks through various road navigation companies.
2. Satellite imagery: Satellite imagery (of increasingly better resolution and spectral bands) is available across the region, occasionally with local authorities restricting the avenues to purchase the data. Stereo imagery can provide height information to define terrain and city model.
3. Aerial photography: Bespoke aerial photography provides the most cost-efficient means of defining land-use of the built environment. Aircraft-mounted cameras allow image capture at the optimal accuracy, resolution, timing and extent. Most jurisdictions in the Region have limitations and/or lengthy procedures controlling the capture of aerial photography; these procedures are generally being relaxed and more photography is being captured.
4. Oblique aerial photography: Whilst traditional (nadir) aerial photography offers the most efficient form of image capture, oblique aerial photography provides imagery of the building facades. This is particularly useful to increase the level of realism of a city model, an important feature when conveying the results of smart city modelling to a wide variety of stakeholders. The benefits and limitations of aerial photography discussed above apply equally to oblique aerial photography.
5. Airborne LiDAR: Airborne LiDAR is eminently suited to defining a complex environment like a cityscape. A single pass of the aircraft can define every building, tree, powerline and tower, as well as the terrain shape (even under vegetation). Being aircraft based, it has the same benefits and limitations as outlined for aerial photography. Aerial cameras are often co-mounted with the LiDAR to provide the “what” to LiDAR’s measurement of the “where”.
6. UAVs: Unmanned Aerial Vehicles (UAVs) offer a viable platform for capturing smaller areas of a smart city. Often UAVs provide infill or update capability to city-wide aircraft surveys. Current technology provides good quality 3D photographic solutions. LiDAR-based UAVs are still somewhat limited to smaller sensors and short sortie durations. Most jurisdictions in the Region impose limitations (or bans) on UAVs, as the world aviation authorities grapple with regulating these new platforms.
7. Terrestrial LiDAR: Terrestrial LiDAR is often employed to define building interiors to support Smart Buildings (or Building Information Models – BIMs). Room sizes, window dimensions and building services are all important components of a Smart Building (which contribute to a Smart City). LiDAR can also be captured from a moving vehicle (“Mobile Laser Scanning”), which is well suited to defining the complex assets and infrastructure along urban road corridors.
8. Terrestrial imagery: Terrestrial imagery can also be captured in a stationary or mobile mode, and adds the landuse characteristics to LiDAR’s asset definition. The imagery can be used to add photorealism to building models, or deployed in a “Streetview” 360° configuration.
9. Building plans: Existing data is more prevalent at building level (either footprints or building plans), although often only in hardcopy form.
10. Field Survey: Field survey remains the backstop for other surveys, offering the most flexibility (and slowest acquisition) to supplement the other survey methods.
Case study: Johannesburg
Another example of solving the “data for smart cities” question was adopted by the City of Johannesburg (CoJ). The City wanted all departments to improve their urban management and decision making, and realised the need for spatial data to underpin these upgrades. The City Planning Directorate identified transport corridors, economic growth nodes and district redevelopments that required 3D modelling with GIS integration and visualisation CoJ commissioned LiDAR, Aerial Photography, Oblique aerial photography, rendered 3D building models and building footprints for their 2950 km2 metropolitan area. The oblique photography made the building models look photo-realistic, and they also provided a tool to monitor property valuations.
The CBD areas were captured with Oblique imagery to provide the geometry and detailed textures of fully rendered 3D building models. Beyond the CBD, building footprints were digitised from the newly created digital orthophoto imagery. Height attributes were assigned to each building footprint, calculated from the ground and non-ground LiDAR datasets. The height attribute allowed simple building shapes to be extruded to their derived height attribute. The 3D models, transport corridors, cadastral parcel data and other layers were added to enable highly interactive visualisation of the city with scenario and recorded fly through simulations.
Data talk
Authorities want and need to better manage and utilise their urban environment, and are calling on a range of professional skills to create the tools to deploy smart city concepts. The tools, sensors, services and processes are quite well developed, andare currently being implemented in the US and Europe. There is no reason why these processes cannot be rolled out to improve the management of cities in the Asia-Pacific region, except for one reason. The “missing ingredient” is the dearth of suitable spatial and textural data upon which these clever processes can operate.
There is a range of technology now available across Asia-Pacific which can help fill that void. The skill is to assess the project and site requirements and deploy the right mix to best meet those needs.
