Intern, Geodesy & National Mapping

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[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.

[岗位简介]
通过开发自动化方法，支持SLA高分辨率地图(HRMap)的增强和更新，以实现公开可用地理空间信息的发现、检测和收集。实习生将审查现有的HRMap图层，并确定可以持续监控的数据集（例如新建筑、地铁站、道路和设施）。实习生将使用网络爬虫、数据提取和AI/LLM技术，开发概念验证工作流程，以捕获和结构化来自公共源的更新信号。此外，实习生将增强现有系统，以检测建筑级别的变化（例如新建设、拆除、重建或地址更新），并支持HRMap建筑图层的自动更新。此项目旨在提高数据的时效性，减少人工维护工作量，并加强国家地图制作的自动变化检测能力。

[您将从事的工作]
审查HRMap数据集，确定可用于自动变化检测的候选数据。
从公共信息源开发网络爬虫和数据提取工作流程。
应用AI/LLM技术从非结构化文本（例如新闻、公告）中提取结构化的地理空间变化信息。
设计和检测建筑和基础设施变化的逻辑原型（例如新的、修改过的或已移除的特征）。
验证提取的数据，并为HRMap集成准备结构化输出。
记录工作流程、方法和发现。

[我们正在寻找]
正在攻读计算机科学、数据科学、人工智能、信息系统、测绘学或相关领域的 tertiary 资格。
具备Python基本编程技能。
对网络爬虫、数据工程、AI/LLMs或地理空间技术有兴趣。
具备强大的分析和解决问题的能力。
具备良好的沟通能力，能够独立工作和团队协作。
与该角色相关的课程或学术项目
主动寻求反馈和持续改进
自我激励，能够独立工作
能够在2026年10月至2027年3月或2026年11月至2027年4月之间投入工作。
请在您的简历中注明您的可用时间。

⚑ 岗位信息收录自公开招聘页（ATS 官方），以雇主官网为准