Using Artificial Intelligence for City Infrastructure Monitoring
Published: 19 March 2021
Cities can use AI and satellite imagery for rapid data collection to prevent illegal structures and ensure sustainable urban planning.
Rapid economic growth following the Industrial Revolution encouraged more people to live in urban areas where opportunities abound. A 2018 report by the United Nations Department of Economic and Social Affairs notes that 55% of the world’s population currently lives in urban areas, and that proportion is expected to increase to 68% by 2050. Using population forecast data from 233 countries, the figures showed that close to 90% of the increase in urbanization will take place in emerging economies in Asia and Africa.
Accommodating such rapid growth, however, has led to the expansion of unauthorized development, urban vulnerability to external forces such as natural disasters, and a lack of adequate social infrastructure.
The use of artificial intelligence and satellite imagery can assist governments to collect data at a faster pace, and ensure better and more sustainable city developments.
Unauthorized development is an issue that impacts the safety and wellbeing of residents. For example, inadequate monitoring of the progress of building construction can lead to structures being located in high-risk areas such as those prone to floods.
There have been many cases where unauthorized buildings have been built on designated public open spaces under comprehensive architectural plans. These unauthorized developments can be discovered early on through regular monitoring of urban areas.
Statistics of demographics, commercial/manufacturing industries, and infrastructure are commonly used for urban development planning. However, due to the difficulty of retaining up-to-date information in rapidly growing cities, many developing and middle-income countries do not possess such high-quality statistical information. The lack of these statistics can prevent effective urban planning, and impede necessary services such as infrastructure development.
With high resolution satellite images analyzed using AI, cities can extract high-quality statistics that can be used for sustainable urban development.
Some examples of AI analysis are:
Getting precise information on narrow roads can be hard because of shadows or dead angle of huge buildings and trees. AI can analyze road footprints from high resolution satellite images, and identify changes such as new construction and demolition by comparing satellite images from different time periods.
Building detection in dense urban areas
AI can detect unauthorized buildings such as informal settlements in rapidly growing cities. It can identify building changes from satellite images taken at different time periods.
Car detection in parking area/harbor facilities
Satellite images of cars in parking areas at commercial districts and harbor facilities can be analyzed by AI. Economic activities can be monitored indirectly by counting the number of cars in a designated area.
Paddy field detection
AI can analyze paddy fields. Blue lines show the extracted paddy field polygons in the images below. It can show agricultural land use and estimate yield of crops.
Land use classification
AI analysis can assist in classifying land use. Compared with the traditional image processing of maximum likelihood method, AI classification based on deep learning can classify land use more accurately by using the ground surface’s texture information from satellite images. It can also identify land use changes by comparing satellite images from two different time periods.
Estimate number of households
In this sample case, the number of households is estimated at a mesh size of 100 meters. After counting the number of buildings within the mesh, it is possible to estimate the number of households by multiplying the ratio of households to buildings in each municipality, city or town. Although this is a rough estimate, it is very useful in rapidly developing urban areas because urban planners have difficulties in collecting this information.
Satellite images and AI can aid developing and middle-income countries in data collection services, which will allow for more efficient urban development planning.
The use of AI and satellite images is gaining momentum as the technology to deploy in putting together a regular collection of up-to-date statistics in urban areas. Specifically, satellite images can visualize infrastructure, such as roads and buildings, while AI can extract information from the images to create statistics, such as the location, number, and size of structures. Additionally, statistics, such as land use information, traffic volume via counting vehicles on the road, vegetation coverage, and building characteristics, can be automatically extracted from satellite images.
Satellite data is open data, which means it is publicly available. Governments can obtain the data from a provider (e.g. National Environmental Satellite Data and Information Service) or reseller in the market.
To develop an AI model and extract statistical information, it is necessary to (i) put the data infrastructure in place; (ii) collect satellite data; (iii) create training data for the AI model; and (iv) tune the AI model by using the training data.
Satellite providers and private companies also provide statistical information, which may be the fastest option for those lacking the capacity to develop an AI model.
Y. Nachmany and H. Alemohammad. 2019. Detecting Roads from Satellite Imagery in the Developing World. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop.
United Nations Department of Economic and Social Affairs. 2019. The 2018 Revision of World Urbanization Prospects. New York: United Nations.
Leave your question or comment in the section below:
YOU MIGHT ALSO LIKE
The views expressed on this website are those of the authors and do not necessarily reflect the views and policies of the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. By making any designation of or reference to a particular territory or geographic area, or by using the term “country” in this document, ADB does not intend to make any judgments as to the legal or other status of any territory or area.