Intern, Geodesy & National Mapping

About the role

This internship supports 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 goal is to improve data currency, reduce manual maintenance effort and strengthen automated change detection for national mapping.

Key responsibilities

• Review existing HRMap layers and identify datasets that can be continuously monitored, such as new buildings, MRT stations, roads and facilities.
• Develop proof-of-concept workflows using web crawling, data extraction and AI/LLM techniques to capture and structure update signals from public sources.
• Enhance systems to detect building-level changes (new construction, demolition, redevelopment, address updates) and support automated updates to the HRMap building layer.
• Apply AI/LLM techniques to extract structured geospatial change information from unstructured text such as news and announcements.
• Design and prototype logic for detecting building and infrastructure changes, validate extracted data and prepare structured outputs for HRMap integration.
• Document workflows, methodologies and findings.

Required profile

• Pursuing tertiary qualifications in Computer Science, Data Science, Artificial Intelligence, Information Systems, Geomatics or a related field.
• Relevant coursework or academic projects related to the role.
• Strong analytical and problem-solving abilities, good communication skills, and the ability to work independently and in a team.
• Initiative to seek feedback and continuous improvement; self-motivated.
• Availability between October 2026 and March 2027 or November 2026 and April 2027; availability should be stated in the CV.

Required skills

• Basic programming skills in Python.
• Interest in web scraping, data engineering, AI/LLMs or geospatial technologies.