Job Description

[What the role is]

Support the enhancement and updating of SLA’s High-Resolution Map (HRMap) by developing methods to automate the discovery, detection, and collection of publicly available geospatial information. The intern will review existing HRMap layers and identify datasets that can be continuously monitored (e.g. new buildings, MRT stations, roads, and facilities). The intern will develop proof-of-concept workflows using web crawling, data extraction, and AI/LLM techniques to capture and structure update signals from public sources. In addition, the intern will enhance existing systems to detect building-level changes (e.g. new construction, demolition, redevelopment, or address updates) and support automated updates to the HRMap building layer. This project aims to improve data currency, reduce manual maintenance effort, and strengthen automated change detection capabilities for national mapping.

[What you will be working on]

• Review HRMap datasets to identify candidates for automated change detection. ​
• Develop web crawling and data extraction workflows from public information sources.
• Apply AI/LLM techniques to extract structured geospatial change information from unstructured text (e.g. news, announcements).
• Design and prototype logic for detecting building and infrastructure changes (e.g. new, modified, or removed features).
• Validate extracted data and prepare structured outputs for HRMap integration.
• Document workflows, methodologies, and findings.

[What we are looking for]

• Pursuing tertiary qualifications in Computer Science, Data Science, Artificial Intelligence, Information Systems, Geomatics, or related fields.
• Basic programming skills in Python.
• Interest in web scraping, data engineering, AI/LLMs, or geospatial technologies.
• Strong analytical and problem-solving abilities.
• Good communication skills and ability to work both independently and in a team.
• Relevant coursework or academic projects related to the role
• Initiative to seek feedback and continuous improvement
• Self-motivated and able to work independently
• Able to commit between October 2026 to March 2027 or November 2026 to April 2027.
• Please include your availability in your CV.