Simplifying the Identification of School Infrastructure Vulnerability at Scale

Using AI algorithms and photographic images from school buildings in Nepal and Kyrgyzstan, two international public universities collaborated with a team from the World Bank to develop a technical solution to the long-standing problem of identifying the most vulnerable school building infrastructures in hard-to-reach areas of developing countries. With this solution, an estimated 875 million children and teachers at risk of being injured can be better protected from natural disaster harms.

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The California Polytechnic State University, San Luis Obispo (Cal Poly DxHub) and Munich University of Applied Sciences (MUAS DTLab), both supported by Amazon Web Services (AWS), collaborated with the World Bank’s Global Program for Safer Schools (GPSS) to design a process that could save time and money in determining the structural type of school buildings to assess their vulnerability. Worldwide, natural disasters like earthquakes and cyclones put more than 1,000,000 school buildings in low- and middle-income countries at risk of collapsing, and an estimated 875 million children and educators at risk of being injured. Yet gathering the necessary baseline information to prioritize risk mitigation investments is a time-consuming, expensive task that requires experts to travel and inspect school infrastructure in remote areas.

One of the biggest challenges in identifying at-risk building structures is the lack of high-quality data about school building inventory, and the absence of efficient mechanisms to update and manage this information. The data collection process to assess school infrastructure is commonly done through field inspections conducted by engineers, which are usually costly and time-consuming. Therefore, innovative and more efficient approaches to collect baseline data are essential to strengthen the capacity of developing countries to scale up safer school activities

GPSS seeks to improve the safety of schools by strategically prioritizing investments based on factors such as building vulnerability and the region’s earthquake hazard level to which they are exposed. However, there’s a lack of high-quality data about building structural characteristics and no efficient way to analyse and manage this data once it’s collected. The process of labelling, managing, and assessing the photographic data is also time-consuming, expensive, and hard to do across tens of thousands of schools.

The California Polytechnic State University’s DxHub and Munich University of Applied Science’s DTLab work directly with governments and other public sector organizations, designing free, open-source solutions. Their goal is to utilize the deep subject matter expertise of the public sector, the technical and innovative expertise of a cloud technology company, and the diverse disciplinary knowledge existing across universities to bring innovative solutions to challenging public sector problems. Mirroring the real-world, these solutions typically require the involvement of experts with different subject matter expertise and perspectives.

In collaboration, these public sector organizations designed and demonstrated a mobile application that guides school administrators and other community members through photographic data collection. The photos are uploaded to the cloud where an algorithm determines the building category, height range and main structural system. The results are remotely reviewed by a trained engineer for accuracy. From there, the data are aggregated and provided to planners and decision-makers to prioritize risk mitigation investments quickly.

The goal of this data collection effort is to establish a set of standardized photos that are compatible with AI algorithm inputs. The teams worked together to demonstrate the use of AI and ML methods towards a more effective way of assessing school buildings in areas difficult to access by the World Bank evaluators.

While the teams worked on these solutions in their courses, the Cal Poly DxHub supported the collaboration with cloud technology credits and resources, as well as technical and project management support. In addition, the DxHub, in collaboration with the faculty member teaching these courses, developed an international student exchange opportunity with MUAS, a partner university in Europe. The purpose of the exchange was to provide students with the additional opportunity to collaborate internationally and consider how varying international contexts and perspectives could improve the development of this international solution. U.S.-based students were to travel to Europe and work with a group of international peers to further develop the initial implementations by the HCI and AI/ML teams towards a deployable solution within a Software Development class. Unfortunately, days before a delegation of four students and one advisor was to leave for a trip to Europe to facilitate the handover of the project, COVID-19 restrictions led to a cancellation of the trip. Fortunately, this collaboration remains intact, and this next step in the work will be resumed once travel restrictions are fully lifted.

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Year: 2021
Level of government: Regional/State government


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