Developing an AI-Based Image Classifier for School Infrastructure Baseline Data Collection in Large Scale Disaster Risk Analysis

Project Sponsor


This project will develop a risk-informed classification system to support AI computer vision algorithms for assessing seismic vulnerabilities in schools. The project will be led by the World Bank’s Global Program for Safer Schools (GPSS), the NYU Disaster Risk Analysis Lab, and the NYU AI4CE. The project’s main goal is to develop a simplified vulnerability classification system based on existing detailed taxonomy from the Global Library of School Infrastructure (GLOSI), to support AI-based computer vision tools to reduce structural vulnerability data collection time and costs in large building portfolios. We envision that the simplified classification will enable more reliable AI computer vision tools to empower communities to be engaged in governments’ disaster risk management efforts more easily, make risk analysis more accessible and informed by up-to-date baseline information worldwide and guide large-scale school safety and resilience investments more efficiently.

Category: Urban Infrastructure

Project Description & Overview

This project will focus on a simplified classification system to support easier and more reliable extraction of structural classifications (features) from pictures of schools using computer vision algorithms. The structural classifications will be set according to the Global Library of School Infrastructure (GLOSI) structural taxonomies ( GLOSI taxonomies are key to defining structural vulnerabilities in school buildings that are considered in large-scale earthquake risk analysis. The project will use the vulnerability data of GLOSI typologies, and school inventory data from ~2000 schools collected in World Bank projects. The project will have the below main parts:

– Methodology Development: Redefining and simplifying GLOSI categories through potential clustering based on seismic vulnerability similarities (e.g., brittle materials); and redefining intermediate labels (e.g., proxy variables like material, building component sizes) to support a hierarchical classification logic to better predict GLOSI categories.

– Data Pre-processing: Curating the school dataset using the simplified GLOSI categories and the developed intermediate labels.

– Computer Vision Analysis: Adjust and retrain the existing computer vision models following the developed intermediate labels.

– Measuring risk errors: Assessing the accuracy/uncertainty with the inventory with the developed simplified GLOSI categories compared to the original detailed inventory, in terms of risk metrics using existing risk software.


1. Vulnerability curves of relevant school building typologies.
2. School inventory dataset with ~2000 schools and their current GLOSI categories. Note: The dataset is proprietary and will be provided under NDA.
3. Existing AI computer vision model prototype for reference.


The team needs at least one person with a background in disaster risk analysis, and preferable computer vision and machine learning. For computer vision, this requirement means a student that took or is taking a class equivalent to CSCI-GA.2271-001 or ROB-GY 6203. A team member with a strong programming background (especially with hands-on deep learning experience) will increase the success rate of the project. For disaster risk analysis, this requirement means a student that took or is taking a course equivalent to CUSP-CX 8006. Please get in touch with professor Ceferino for further inquiries about competencies.

Learning Outcomes & Deliverables

Learning Objectives:
1. Develop hands-on experience with AI and computer vision tools for seismic resilience.
2. Understand the data requirements for conducting regional disaster risk analysis and defining seismic vulnerabilities.

1. Simplified classification system on school building vulnerabilities for computer vision uses.
2. Progress and final reports.
3. A final presentation with wider GPSS team.