The role of synthetic data in creating truly smart cities

The need for data continues to rise as operational efficiency and process automation grow more and more important across the board, from small-scale initiatives to smart cities. Since achieving them using real-world data is difficult, synthetic data is being highlighted as a solution to these shortcomings. Smarter AI will have a bright future if synthetic and real-world data can be combined to develop smart cities.

AI has repeatedly demonstrated its value in fostering innovation. Still, if we want to meet rising demand and solve the limitations of real-world data, we need to reevaluate how machine learning (ML) models are trained. Enhancing artificial intelligence (AI) usage requires annotations for particular use cases. This used to rely on people, making it expensive and time-consuming. This emphasizes the need for a better solution and concerns about privacy issues and deployment for unusual circumstances.

What is synthetic data?

According to Gartner, artificial intelligence models will predominate over real-world data by 2030. Synthetic data generated by computer simulations can imitate the characteristics of real-world data and match it statistically and architecturally. The capacity to simulate any edge case imagined can help overcome the lack of anomalous event data. Due to its machine-generated nature, it can be used to correct for existing bias in the underlying data while simultaneously saving important time and effort.

Model training should be wider than just one type of data source. To effectively advance training at a lower cost, with greater applicability, and without privacy or bias issues, the two may complement one another. However, developing smarter AIs capable of identifying and preventing edge cases in smart cities will require synthetic data.

What synthetic data means for smart cities

The concept of smart cities has been debated for many years. The idea prioritizes improving the quality of life for citizens and transforming urban settings for greater efficiency. This is accomplished by applying technology designed to increase sustainability and safety. However, it appears that there is never a shortage of urban pollution, traffic accidents, or street crime. We need progress.

Cities can now be made safer and greener thanks to technology. Synthetic data is crucial because deployment is largely what it comes to. Vision systems require significant quantities of precisely annotated, high-quality, and privacy-compliant data. As was mentioned, this is only sometimes possible, especially if we only use data from the real world, which is unlikely to be easily accessible.

Synthetic data offers several benefits for training these systems to make more informed decisions. There are no restrictions on using sensitive data, datasets can be customized for marginal cases that might not be feasible with real data, and insights can be acquired quickly and cheaply. Biases can be reduced, which will help predictive modeling become even more accurate. In the end, synthetic data is a crucial tool for encouraging the adoption of AI for smart cities.

Safer cities

What frightens urbanites the most? It could be argued that it is a violent crime involving a gun or a knife. Additionally, with the growth of hybrid working, employees can choose where to work. Public officials are encouraged to ensure that investments are made to reflect favorably on their cities' consideration of citizen wellbeing, from individual safety and public health to extreme events like natural disasters.

While dealing with typical, easily predictable scenarios can be handled using only real-world data, there is a gap for handling anomalous circumstances regarding protection. In control centers, video is being streamed on hundreds of monitors. Human controllers can't monitor every screen. An AI system trained to record and identify incidents can free up much labor for jobs requiring human supervision. Data will therefore become more and more important in the effort to prevent crime.

The use of smart city technologies, such as license plate recognition, gunshot detectors, body cameras, and crowd monitoring, alters how city management operations are carried out. Data and AI have the power to revolutionize safety protocols. Building stronger communities and mutual trust between governing bodies and citizens will result from proactively reimagining public safety, but this depends on technology that can be adaptable. The key to achieving this is using AI solutions powered by synthetic data, which also help to increase public trust in local government officials and gain a comprehensive understanding of the population.

Better life quality

Smarter cities are designed to be more pleasant places to live, with things like easier parking, fewer traffic jams, timely road repairs, effective waste removal, etc. This emphasis on technology should be balanced by focusing on people, not the other way around.

A truly intelligent system capable of handling any situation can be built through the ability to gather larger amounts of data using synthetic data across various scenarios. Smooth operation depends on your ability to translate this information into practical insights. With insights into the community's most pressing issues and the capacity to act quickly, artificial intelligence powered by synthetic data accelerates the process from information to action.

Implementing smart services often starts with smart lighting. Utilizing adaptive technologies, such as streetlights that only use energy when necessary and can detect human presence, can significantly reduce electricity consumption. Synthetic data can significantly lower electricity costs, improve security, and account for various circumstances, including weather. This simplifies the process and clarifies how important it is to put sustainability at the top of the priority list.

There is more opportunity for transparency because synthetic data does not have the same privacy concerns as actual data. Synthetic data makes the notion that citizens should be at the center of service creation a reality. By doing this, optimization and action alignment with citizen needs are ensured.

Building cost-effective smarter cities with synthetic data

It will ultimately come down to how much money technology will save in the long run to persuade the authorities to adopt it. Although they have a significant price tag now, smart cities will eventually become more cost-effective. Real-world data is expensive in terms of time, effort, and resources in addition to financial costs. Unlike real data, synthetic data can be produced more affordably and is always available.

It is important to recognize that various datasets are required to optimize training models. However, synthetic data will play a leading role in developing smarter AI to create globally smarter and safer cities.