Urban Planning Tech in India Needs Overhaul, Says Nithya Ramesh

Venkatesh Kannaiah: How has tech transformed urban planning in India?

Nithya Ramesh: Tech has transformed urban planning in the Global North and East with data-driven decision-making, Internet of Things, digital twins, and databases that cover everything from land title to property tax.

For instance, a city like Raleigh in North Carolina, USA, with a population of five lakh, has a GIS database for every property. You get details of the current owner, past sales, tax paid, utilities, all with a single click. They are then able to make decisions on land pooling and the redevelopment of areas. In India, urban planning remains outdated, and most cities don’t have master plans, and those that do, neither implement nor enforce them. In India, a city master plan might take a decade for preparation and notification, and by that time, the city has changed substantially, making the master plan redundant.

Take the case of Bengaluru. The master plan expired in 2015, and now, with the formation of the Greater Bengaluru Authority, it is yet not clear if there will be one master plan or five master plans. Even that may take another five years to be notified, and by then the ground has shifted beneath our feet. Urban planning in India needs a complete overhaul in the way we do things, starting with a revision of the Town and Country Planning Act, governance reforms, and the way our institutions plan, implement, and enforce the rules.

We could use tech tools in a big way for the overhaul and modernisation of the way we do city planning today.

Venkatesh Kannaiah: Can you talk about a few technologies that are having an impact on urban planning?

Nithya Ramesh: First is the GIS mapping of cities. Many Indian cities do it, but it lacks timely updates and is also not easily shared, even between government departments. Very often, mapping is done repeatedly with no improvement to the data or accessibility to the same. But the key steps lie after the mapping, in consolidating data from different sources and putting it to use.

Cities globally are also using AI and prediction-based software to map out future traffic patterns, growth areas, and plan for urbanisation, including for utilities and other services. Other uses are to slice the data available on the GIS maps for analytics and decision-making.

Second is the use of tech tools for community engagement on urban planning and decision-making. Apart from the identification and design of projects, tech can also be used to create community groups for monitoring, maintenance, and budgetary allocation.

Other tools, perhaps not widely used in India, that are shaping urban planning are those that help with transportation. From mapping roads including future requirements, to planning for people movement, freight movement, and even catching traffic violations.

AI-optimised cameras fixed onto public buses are able to monitor streets effectively and charge vehicle owners for violations. Data collected from sensors atop streetlights is now used for mobility planning. In India, there are several startups exploring AI for mobility, road safety, and road maintenance, but they are not yet mainstream.

Venkatesh Kannaiah: Can you tell us about tech implementation challenges in urban planning in India?

Nithya Ramesh: Perhaps the one tech that India has adopted on a large scale is GIS mapping. Atal Mission for Rejuvenation and Urban Transformation (AMRUT) master plans are GIS-based. But that’s where it stops.

GIS is a tool that can be used for layered spatial analytics and decision making, but in most Indian cities, we are now using it as a static mapping tool that rarely gets updated. The GIS databases, let alone being open source, are not shared even amongst government departments, resulting in inefficiencies and poor decision-making.

For instance, we prepared the master plans for three AMRUT towns in Tamil Nadu between 2017 and 2024. National Remote Sensing Centres provided the GIS maps of the towns. As master plan consultants, we deployed teams and ensured ground truthing of the data. The maps were then updated, and the updated GIS database was submitted to the NRSC. The master plans for these towns were submitted for notification in 2023-24. They still have not been notified, while the ground truthing has become obsolete, as cities are dynamic.

The GIS database of cities needs to become open source, updated in real time, and needs to be used for decision-making and not merely a checkbox for approving city master plans.

Before using prediction and decision-making tools, an urgent need is data consolidation and closing the data gaps between different agencies. We need a way to collect different sets of data, overlay them in the same place, slice and dice them as needed, and update regularly.

For instance, if someone is planning a road scheme for a state, the challenge is that the urban development department of the state normally does not have data such as the length of the road, hierarchy (whether arterial or sub-arterial), or road ownership (owned by which government authority). It may also not have data on quality and when money was last spent on the roads.

Imagine if data has to be sought from each utility agency – sewage, power, water supply, etc., to understand which roads have what lines. If the city has a GIS-based master plan, the land use adjacent to the road may be available, but it might be a decade old. This is the information we need to plan for a road scheme. Other information, such as which roads flood the most, where accidents frequently occur, and where congestion issues are, can and should be added to this to aid decision-making.

Venkatesh Kannaiah: What ails Indian city roads? How much of it is a tech problem?

Nithya Ramesh: While we need to get the basics of the tech right, I think we also need to enable it to improve efficiency. Infrastructure works take a really long time in India. Tech and AI can be used to minimise human effort.

For instance, before we start a road project, we need to commission topographical surveys. This is currently a human effort, using a theodolite, not much different from the original surveyors of the East India Company. Other countries have satellite data, high-resolution drone photography data,and detail that you can use directly to start designing. We have to wait for at least a week to 10 days to get one kilometre of road surveyed in India. There is better tech elsewhere, and we need to start using the same.

Most guidelines and schedules of rates are detailed documents. AI can help in pulling out relevant information to help engineers in road design and construction.

If tech and AI are used for project management and site monitoring by urban local governments, it could save time and human effort.

Venkatesh Kannaiah: In the field of urban design, tell us about your projects which created an impact?

Nithya Ramesh: There is the Tender SURE project. We wrote the first guidelines for the design and implementation of urban roads in 2011 and demonstrated their utility with a proof of concept with 10 kilometres of roads in Bengaluru. A survey conducted in 2022 has shown that it has led to better walkability, safer and more drivable roads, less flooding, and cheaper maintenance.

We have now taken it to Uttar Pradesh as a statewide scheme named CM GRIDS (Chief Ministers Green Roads Infrastructure Scheme) with guidelines, capacity building of engineers, consultants, and contractors to build 300 km of urban roads across 17 municipal corporations. These will be the first-of-its-kind roads in the state to have all utilities underground, continuous footpaths, and dust reduction due to end-to-end design. We are now hoping to advocate for the adoption of a national mission on urban roads to develop urban roads at scale, similar to how the country has developed highways and rural roads.

Venkatesh Kannaiah: Could tech platforms be used for participatory planning?

Nithya Ramesh: Currently, tech platforms are used for surveys and to collect data, but there is an opportunity to leverage them for participatory planning, where citizens can participate remotely. We could also leverage it for discussion forums and educational content, where citizens can learn about the road/other work and monitor the same.

Venkatesh Kannaiah: Tell us about a project of yours and how tech is being used to create impact.

Nithya Ramesh: We are working with a partner to create tools to make road selection faster and provide technical support through a chatbot for the CM GRIDs project in UP. The road selection tool minimises engineers’ time in selecting roads for redevelopment. Instead of engineers having to visit every road in the selected areas, AI filters and shortlists roads based on footfalls, land use, and public transport connectivity. The engineers then have to visit a few roads to finalise the roads for redevelopment.

The chatbot, tentatively called Road Bhaiyaa, is being developed to help engineers with design compliance and technical details. The chatbot will answer FAQs on road design and implementation. Engineers can use it when on site or during the design phase to clear doubts and check the contractor’s work.

We are also working with the government and on a roads portal for the CM GRIDS scheme to speed up design approvals and money disbursements. The portal will have all the guidelines, design, and will have visualisations and details for the general public by city and road. It will enable engineers to upload designs for approval and site photos during construction. It will help the state government to monitor progress and release funds. The portal has just been launched and is expected to speed up approval time.

Venkatesh Kannaiah: Tell us about the urban planning/design ecosystem and the tech innovation ecosystem in India. How are they aligned?

Nithya Ramesh: No, they are not aligned, but there is a lot of potential. Tech could be a silver bullet for a lot of urban challenges, for instance, in filling data gaps, getting all data in one place, easing interagency coordination, updating the data in real time, and analysing voluminous data. It could also help in predicting outcomes to enable better decision-making.