- A case study in Sagar
In the year 2050, seventy percent Indians would live in cities. To support all these people you need overall infrastructure, sustainable real estate, communications, and market viability. Moreover, you would need cities where information is the principal infrastructure and the basis for providing essential services to residents. In order to design such a Smart City, one needs a digital replica of the current city with all its assets. Moth eaten data has historically begeted chronically doomed planning for India Shining. A smart city requires oversight of the many working parts of an urban area from the public transit system to the radio towers on building tops. Smart city curators pull from thousands of sources to form a cohesive picture of their city with the needs of the people within it. Converting an established urban area into a smart city begins with a 3D reconstruction that accurately maps every entity within the city. The precision of this map is extremely important, because it is the foundation of the whole system.
Drones fulfil this fundamental need for better, cheaper, regularly updated geospatial information about what exists now with the use of photogrammetry, image processing and ground control points. The captured imagery could provide a base for collecting all the 2D and 3D features that are the last-mile problem in modelling and visualising the whole world.
The advantages of drones in Smart Cities:
Satellites have been used forever and we do not seek to substitute them, what drones do is provide even more dense, cheap, and repeated data. UAV devices will be another tool in the toolbox, as relevant as a telephone or total station. All of this translates to localised geospatial information equipped for change tracking. The major advantages of drone survey are usability, sharability, granularity, and startling savings on operational time (anywhere upto 80% as compared to traditional surveying methods depending on geographical challenges).
- Disruptive Efficiency while breaking the silos:
With the need to advance smart city programmes quickly, drones offer flexibility, allowing surveyors to map long corridors efficiently at the start of projects and collect in-depth data to aid decision-making at an earlier stage. Silos are never good for data and there is not a better lead silo than government department data. Instead of siloing vital data in different filing cabinets, computer systems, teams or buildings, seemingly disparate pieces of information are aligned, allowing policy makers to make better decisions to the benefit of all. Integration with IoT and MIS platforms ensures that you have a geospatial anchor to all your insights.
Drone Mapping capabilities are laying the foundations of Smart City solutions for even small but growing cities like Sagar. Consider the Smart City project of Sagar, with an area of interest of 1100 acres (4.4 Sq Km), it is supporting the ambitions of 2.73 lakh inhabitants of the city.
- Drone based maps and their use in planning:
High resolution and highly accurate maps at your fingertip enable you to work with a wide range of applications. The area of interest in Sagar is a mixture of congested built-up land, vacant space, heavy tree canopy, water bodies, and other types of infrastructure. The CAD extract of the features from the processed outputs are one of the examples to understand the depth of impact. Conventionally, planning for such projects happens in a software tool like AutoCAD. Now our challenge is to get all the results compatible with the existing systems. Interoperability of the data generated in the industry is one of the primary bottlenecks to successful smart cities. We bridge that gap between conventional methods and new technologies. All the extractions are compatible with any existing platform.
- Granularity: Why do Smart Cities need such dense data?
The level of detail in the Drone based imagery is not a luxury but much needed for all derived applications. For instance, taking advantage of the high resolution we are able to identify features like Manholes, electric poles, wells, and are even able to differentiate between the footpath and the adjoint roads.
The map and the computational challenges in reading them:
Maps are raw materials. At DronaMaps, our vision is to use point cloud data from drone imagery to inform automated geospatial feature extraction. To do it manually, such a feat implies that you would not need a team of 100 people to manually tag the geospatial data in 100 sqkm of urban area in 2 months. You can pull off the same in less than a couple of weeks automated. And this is only in preliminary stages.
Some Stats for Sagar:
By traditional surveying methods — getting ground measurements, sorting the data, and making the models (CAD drawings) would take minimum 9- 10 months to cover the current area of interest. We are able to finish the same work in 27 days which includes additional capabilities like a web application.
Features Identified and Geolocated in Sagar by a team of 4 in 2 weeks:
- Road network- Total length of road network mapped is 182992.4993 meter
- Footpaths- Total length covered: 11732.43977 meter
- Electric Poles- Number of electric poles identified: 1514
- Wells — Number of wells identified: 58
- Empty plots — Total number of empty plot identified: 1822, Total area of empty plots: 1.933037802 Sq Km
- Building footprints- Total area of building footprints identified: 1.426884096 Sq Km
- Trees- Number of trees identified: 20419
- Manholes- Number of Manholes identified: 726
- Water bodies- Total area of water bodies identified: 1.512594593 Sq Km
High-resolution 2-D map
Resolution: 2.26 cm/pixel
Accuracy: 5–7 cm
High-resolution elevation map (DSM)
Resolution: 6.78 cm/ pixel
Accuracy: 5-7 cm