A Japanese government panel agreed Thursday to promote digital transformation to tackle the aging of public infrastructure, including water supply and sewage systems. This followed a high-profile road collapse incident in Yashio, Saitama Prefecture, last month, which is believed to have been caused by a broken sewage pipe. At a meeting of the digital administrative and fiscal reform panel, Prime Minister Shigeru Ishiba, who heads the group, instructed related officials to urgently work on the use of digital technologies for water and sewage systems to ensure that their operations by local governments are sustainable. He called for introducing such technologies within about three years, against the previous deadline of five years. For water and sewage systems, satellites and artificial intelligence systems will be used to collect and analyze data on temperature, geology and other factors to identify areas where water leaks may occur.

You'd probably notice if the car that cut you off or pulled up beside you at a light didn't have a driver. In the UK, self-driving cars are still required by law to have a safety driver at the wheel, so it is difficult to notice them. But car companies have been testing automated vehicles on UK roads at least since 2017. Self-driving cars use Artificial Intelligence (AI) technology to steer themselves and navigate around obstacles. This technology is being introduced in many different ways, for example in cameras that detect whether people are speeding or using mobile phones while driving.

Clean water and sanitation are essential for health, well-being, and sustainable development, yet significant global disparities remain. Although the United Nations' Sustainable Development Goal 6 has clear targets for universal access to clean water and sanitation, data coverage and openness remain obstacles for tracking progress in many countries. Nontraditional data sources are needed to fill this gap. This study incorporated Afrobarometer survey data, satellite imagery (Landsat 8 and Sentinel-2), and deep learning techniques (Meta's DINO model) to develop a modelling framework for evaluating access to piped water and sewage systems across diverse African regions. The modelling framework demonstrated high accuracy, achieving over 96% and 97% accuracy in identifying areas with piped water access and sewage system access respectively using satellite imagery. It can serve as a screening tool for policymakers and stakeholders to potentially identify regions for more targeted and prioritized efforts to improve water and sanitation infrastructure. When coupled with spatial population data, the modelling framework can also estimate and track the national-level percentages of the population with access to piped water and sewage systems. In the future, this approach could potentially be extended to evaluate other SDGs, particularly those related to critical infrastructure.

Traditionally, policymakers and urban planners haven't had access to city data that can reveal complex patterns and relationships between factors that influence urban development. In some cases, data is too laborious or costly to measure at frequent time intervals, and in others, unexpected or unforeseen circumstances such as a pandemic like COVID-19 are responsible for invalidating earlier forecasts. But this is changing rapidly, with emerging technologies unlocking new possibilities for urban planning. Advances in emerging technologies like Artificial Intelligence and Machine Learning can help us understand our cities better and derive useful insights from real-time data collected through automated models that provide a much closer view of the situation on-ground compared to traditional approaches. These insights can properly assess public interests and help policymakers in making decisions that are more sustainable.