Master’s in MapOps Engineering: HD Map Production and Maintenance
Online master's programme of 1,900 hours in MapOps engineering for HD map production and maintenance, structured in 12 modules covering data capture, processing, map feature extraction, data models and formats, quality control, change detection, crowdsourcing, distribution, cloud infrastructure and governance, with an assigned technical tutor.
Master’s in MapOps Engineering: HD Map Production and Maintenance — studying from Singapore
Singapore is a leading testbed for autonomous vehicles, with public road trials of driverless shuttles and robotaxis in districts such as one-north and Punggol. The Land Transport Authority coordinates the regulatory framework for autonomous mobility, while the Centre for Advanced Robotics at Nanyang Technological University and the Singapore Autonomous Vehicle initiatives drive research. Engineers work on perception, sensor fusion, vehicle software and simulation. Employers include automotive tier-one suppliers, technology firms and research institutes active across the region.
Fees for Singapore: S$9,880 (Singapore dollar), with instalments and employer sponsorship available.
Relevant bodies in Singapore
Typical roles
Questions from Singapore
Which Singapore authorities govern autonomous vehicle testing, and does SEIUM's qualification help with that?
Singapore's autonomous vehicle trials are regulated by the Land Transport Authority (LTA), which issues approvals for testing on public roads. Engineers may also register with the Professional Engineers Board (PEB). SEIUM's qualifications are private qualifications, not state degrees, and not accredited by any national body. Whether they are recognised for these roles is for employers and the relevant authorities to decide.
Which Singapore employers hire for ADAS and software-defined vehicle work?
Opportunities exist with autonomous vehicle trial operators, mobility companies, automotive electronics suppliers, and software and systems firms serving the vehicle industry. Singapore's smart mobility push supports these roles. Employers generally value demonstrable software skills, such as projects in embedded code, simulation or safety validation. Your final project can show this, but hiring decisions rest with each employer.
Is this workable part-time from Singapore, and can it be funded by an employer?
Yes, the programme is 100% online and taught in English, so you can study from home in Singapore without any student visa. Expect a heavy load: a master's is 1,900 hours, a postgraduate diploma 800 hours and a specialist course 300 hours. Fees are payable in instalments, and employer sponsorship is possible, though approval depends on your company's training policy.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Plan data capture campaigns for HD maps.
Process and georeference mobile mapping data.
Extract map features using automated techniques.
Apply standard map data models and formats.
Implement quality control and updating systems.
Design scalable production and distribution pipelines.
Develop a complete MapOps project.
Who it's for
- MapOps engineers.
- Geospatial data engineers.
- LiDAR processing engineers.
- Cartographic quality managers.
- Data pipeline engineers.
- Map production managers.
- Engineering or science graduates looking to enter cartography.
What you'll work with
- ASAM OpenDRIVE (road network description format)
- ISO 20524-1 (Geographic Data Files, GDF 5.1)
- ISO 19157-1 (geographic data quality)
- ISO 17572-3 (dynamic location referencing, OpenLR)
- ISO 26262 (functional safety of road vehicles)
- ISO 21448 (safety of the intended functionality, SOTIF)
- Regulation (EU) 2016/679 (General Data Protection Regulation, GDPR)
- ISO 19115-1 (geographic information metadata)
- Lanelet2
- QGIS
- PostGIS
- Apache Spark
- CloudCompare
12 modules · 1900 h
1.1HD map value chain1.2Provider business models1.3OEM customer requirements1.4Maps for L2+ versus L41.5Production costs per kilometre1.6Global scalability of production1.7Organisation of a MapOps team1.8Map life cycle1.9Quality and freshness indicators1.10Case study: production plan for a city
2.1Mobile mapping vehicles2.2Capture LiDAR and cameras2.3Reference GNSS/INS systems2.4Capture route planning2.5Platform calibration2.6Big data management2.7Capture with a production fleet2.8Aerial and satellite capture2.9Privacy requirements2.10Lab: capture campaign plan
3.1Post-processed trajectory computation3.2Georeferencing of point clouds3.3Multi-pass adjustment3.4Image anonymisation3.5Filtering of dynamic objects3.6Orthoimage generation3.7Fusion of heterogeneous sources3.8Accuracy control with ground control points3.9Cloud pipelines3.10Practical: multi-pass adjustment with a point cloud
4.1Extraction of road markings4.2Detection of road edges and kerbs4.3Extraction of signs and traffic lights4.4Reconstruction of lane topology4.5Extraction with deep learning4.6Feature vectorisation4.7Speed and priority attributes4.8Semi-automatic feature editing4.9Sampling-based quality control4.10Workshop: lane extraction with neural networks
5.1OpenDRIVE and its structure5.2Lanelet2 and traffic rules5.3NDS.Live and modular services5.4GDF format ISO 205245.5Localisation layer schemas5.6OpenLR location referencing5.7Map versioning5.8Conversion between formats5.9Schema validation5.10Lab: OpenDRIVE to Lanelet2 conversion
6.1Absolute accuracy metrics6.2Completeness metrics6.3Topological consistency of the network6.4Automatic rule validation6.5Human sampling review6.6Validation through real driving6.7Defect management6.8Quality according to ISO 191576.9Quality reports6.10Practical: topological validation rules
7.1Sources of change in the road network7.2Fleet-based change detection7.3Roadworks and authority data7.4Comparison between map and perception7.5Prioritisation of updates7.6Incremental tile-based updating7.7Release cycles7.8Freshness management7.9Change latency metrics7.10Workshop: change detection pipeline
8.1Sensor data from series-production vehicles8.2Aggregation of observations8.3Consensus-based geometry estimation8.4Quality of crowdsourced data8.5Bandwidth and data costs8.6Privacy and consent8.7Data-sharing models8.8Fleet-generated maps8.9Near-real-time maps8.10Case study: large-scale fleet map
9.1Tile-based map distribution9.2OTA map updates9.3Map APIs and services9.4In-vehicle cache9.5Version management across the fleet9.6Backward compatibility9.7Security of the distribution chain9.8Service level agreements9.9Service monitoring9.10Practical: tile API design
10.1MapOps cloud architecture10.2Pipeline orchestration10.3Geospatial databases10.4Distributed processing with Spark10.5Labelling and editing tools10.6Automation with machine learning10.7Compute and storage costs10.8DevOps for maps10.9Pipeline observability10.10Lab: pipeline with PostGIS
11.1Functional safety of maps11.2Map integrity in SOTIF11.3Cybersecurity of map data11.4Personal data protection11.5Legal restrictions on mapping11.6Data licensing11.7Data governance11.8Liability for errors11.9Audits and certifications11.10Workshop: map risk analysis
12.1Definition of the area and requirements12.2Data capture plan12.3Processing and extraction pipeline12.4Data model and formats12.5Quality control system12.6Updating strategy12.7Distribution architecture12.8Cost evaluation and conclusions12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
The programme is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so you can combine it with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Lanelet2, QGIS, PostGIS, Apache Spark and CloudCompare, in free, academic or evaluation versions. Testing is studied through real procedures for capture campaigns with a mapping vehicle, map validation through real driving and change detection with fleet data. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ASAM OpenDRIVE, ISO 20524-1, ISO 19157-1, ISO 17572-3 (OpenLR), ISO 26262, ISO 21448, Regulation (EU) 2016/679 (GDPR), ISO 19115-1.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- Internet connection and a computer capable of running the programme's software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Lanelet2, QGIS, PostGIS, Apache Spark and CloudCompare, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software, and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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