Artificial intelligence for smart cities: insights from Ho Chi Minh City’s spatial development

It’s amazing to see what technology can do these days! Satellites provide daily images of almost every location on earth, and computers can be trained to process massive amounts of data generated from them to produce insightful analysis/information. This is just one of the demonstrations of artificial intelligence (AI). AI can go beyond just reading images captured from space, it can help improve lives overall.

For urban governance, machine learning and AI are increasingly used to provide near real-time analysis of how cities change in practice – for example, through the conversion of green areas into built-up structures. By teaching computers what to look for in satellite images, rapidly expanding sources of satellite data (public and commercial), together with machine learning algorithms, can be leveraged to quickly reveal how actual city development aligns with planning and zoning or which communities are most prone to flooding. This provides insights beyond the basic satellite snapshots and time-lapse visualizations that can now be readily generated for any areas of interest.

But the barriers to applying these technologies can still seem daunting for many cities around the world. It’s not always clear how exactly to analyze this massive amount of satellite data, nor how to get access to it.

An ongoing collaboration between the World Bank’s Governance and Land & Geospatial Teams, together with the Development Economics Research Group, is helping connect emerging technology and machine learning to key development stakeholders in Ho Chi Minh City (HCMC), Vietnam. Specific types of machine learning algorithms “teach” computers to automatically detect and classify different types of land cover and land use across space and time, and then generate compelling insights, analytics and visualizations. In this type of machine learning, which is called supervised machine learning, computers are trained what to look for in a satellite image based on reference, or training data. This data consists of examples that can be collected “on the ground” through surveys, or even from existing classifications or from data collected from comparable cities. Based on this data, the machine predicts the spatial distribution of these types of land cover and land use, and this prediction can then be analyzed and translated into smart city management.

While this type of geo-analysis is becoming more pronounced (including in Bank reports), the HCMC initiative combined three features for applied innovation. First, the starting point of the work was to focus on using free imagery and geospatial tools (from NASA, the European Space Agency and Google), rather than going for a “blue chip” or “black box” consultant report. Second, a hands-on review and training was conducted for government officials and students. Finally, the results of this analysis were overlaid with administrative data such as Zoning Boundaries, and linked to HCMC Smart City and Open Data initiatives.

HCMC’s built-up land has rapidly expanded in the past 20 years. Administratively, HCMC covers just over 2,100 km2, about 6 times the size of Washington, DC. The classification produced by the machine learning approach suggested that, in parallel to a rapid population growth that occurred between 2000 and 2015, the city`s built-up land cover has expanded dramatically, with some of its two dozen districts showing more than 70% increase in built-up land cover. These trends were also expressed in the amount of light emitted at night from the city, which was highly correlated with its economic development.

While “for-free” satellite imagery (i.e.