Field Mapping & Ground Truth Associate
Field Mapping & Ground Truth Associate — Urbanomy
OpalAI | Urbanomy Project
Los Angeles, CA + Travel to U.S. Cities
Field-Based | Part-Time / Full-Time / Contract
About Urbanomy
Urbanomy is OpalAI's AI-powered platform for mapping and understanding streets, sidewalks, transportation infrastructure, and urban environments.
We combine street-level imagery, LiDAR, GNSS/RTK, GIS, and artificial intelligence to create detailed digital representations of cities and ground-truth datasets for transportation safety, infrastructure assessment, and AI model development.
We are looking for a Field Mapping & Ground Truth Associate to conduct street and sidewalk surveys and create high-quality real-world datasets across multiple U.S. cities.
The Role
This is a hands-on field position for someone with academic or practical experience in GIS, surveying, transportation engineering, geomatics, geography, remote sensing, or related fields.
You will walk and drive designated routes, collect geospatial and visual data, document street infrastructure, verify real-world conditions, and create ground-truth datasets used to train and evaluate Urbanomy's AI models.
Applicants must have successfully completed relevant university coursework and must have access to their own field data-collection hardware.
What You'll Do
- Conduct field surveys of streets, sidewalks, intersections, and transportation infrastructure.
- Walk and/or drive predefined mapping routes.
- Capture high-quality geotagged imagery and video.
- Collect and verify GPS/GNSS positioning data.
- Operate cameras, mobile mapping equipment, and field survey applications.
- Perform systematic ground-truth surveys of urban infrastructure.
- Follow standardized data-collection procedures across different cities.
- Verify that required streets and sidewalks have been completely captured.
- Review field data for accuracy, positioning, image quality, and completeness.
- Re-survey locations when collected data does not meet project requirements.
- Organize, document, and upload large field datasets.
- Work with OpalAI's AI and engineering teams to validate mapping results.
- Compare AI-generated detections with actual field conditions.
- Create and verify ground-truth labels for computer-vision models.
Infrastructure You Will Survey
Field surveys may include identification and documentation of:
- Streets and roadway conditions
- Sidewalks
- Intersections
- Curb ramps
- ADA/accessibility conditions
- Crosswalks
- Bicycle lanes
- Traffic signs
- Traffic signals
- Lane and pavement markings
- Bus stops
- Parking and loading zones
- Street furniture
- Utility infrastructure
- Road and sidewalk defects
- Trees and vegetation
- Pedestrian infrastructure
- Bicycle infrastructure
- Construction and temporary obstructions
- Other transportation and public-right-of-way assets
Required Academic Background
Applicants must have successfully completed at least one relevant university-level course in areas such as:
- Geographic Information Systems (GIS)
- Surveying
- Geomatics
- Geospatial Science
- Remote Sensing
- Photogrammetry
- Civil Engineering
- Transportation Engineering
- Urban Planning
- Geography
- Spatial Data Science
- Robotics
- Computer Vision
- Autonomous Systems
Applicants should list the course name, university, and relevant project or field experience when applying.
UCLA Students
We especially encourage applications from UCLA students and recent graduates with relevant coursework from the following programs.
UCLA Geography / Geospatial / MAGIST
Relevant coursework includes areas such as:
- GEOG 401 — Applied Spatial Data Science
- GEOG 410 — Geospatial Databases and Data Management
- GEOG 411 — Geospatial Imagery Analysis
- Programming for Spatial Data Science
- GIS and spatial analysis
- Remote sensing and imagery analysis
- Geospatial data management
- Web and cloud GIS
Students in UCLA's Master of Applied Geospatial Information Systems and Technologies (MAGIST) program are strongly encouraged to apply.
UCLA Luskin School of Public Affairs — Urban Planning / Transportation Policy & Planning
Relevant coursework includes:
- UP 221 — GIS and Spatial Data Science
- UP M213 — Urban Data Science
- UP M250 — Transportation and Land Use: Urban Form
- UP 254 — Bicycle and Pedestrian Planning
- Transportation methods and applications
- Transportation planning
- Urban data analysis
- GIS and spatial analysis
UCLA Samueli School of Engineering — Civil & Environmental Engineering
Students studying Transportation Engineering are strongly encouraged to apply.
Relevant coursework includes:
- C&EE 180 — Introduction to Transportation Engineering
- C&EE C181 — Traffic Engineering Systems: Operations and Control
- C&EE C183 — Transportation Sustainability
- C&EE C184 — Transportation System and Policy Outcomes
- C&EE C185 — Transportation Systems Analysis
- C&EE C186 — Intelligent Transportation Systems
Students from other universities with equivalent coursework are equally encouraged to apply.
Required Personal Hardware
Candidates must have access to their own basic field data-collection equipment.
At minimum, applicants should have:
- A modern smartphone with GPS and a high-quality camera
- A laptop capable of handling imagery, mapping, and field datasets
- External SSD or adequate portable storage
- Portable battery/power bank
- Appropriate mounting or carrying equipment for field collection
Applicants should preferably also own or have regular access to at least one of the following:
- Insta360 or another 360° camera
- GoPro or equivalent action camera
- GNSS/GPS receiver
- RTK GNSS equipment
- LiDAR scanner
- Depth camera such as Intel RealSense
- Surveying equipment
- Drone or other geospatial data-collection hardware
Applicants must list the make and model of the hardware they own or have access to when applying.
OpalAI may provide specialized sensors and mapping equipment for specific Urbanomy campaigns, but candidates are expected to have their own basic field kit.
Required Qualifications
- Successfully completed relevant GIS, surveying, transportation, remote-sensing, geospatial, or related coursework.
- Experience collecting or working with geospatial data.
- Strong attention to detail and data quality.
- Ability to carefully follow standardized survey protocols.
- Comfortable working outdoors for extended periods.
- Comfortable walking several miles during field surveys.
- Comfortable working safely around roads, vehicles, cyclists, and pedestrians.
- Familiarity with maps, geographic coordinates, and GPS.
- Ability to document field conditions clearly.
- Ability to manage and transfer large image/video datasets.
- Access to required personal field hardware.
- Reliable transportation preferred.
- Valid driver's license preferred.
- Ability to travel for Urbanomy mapping projects is strongly preferred.
Preferred Technical Skills
Experience with any of the following is a plus:
- ArcGIS Pro
- ArcGIS Field Maps
- Survey123
- QGIS
- Google Earth
- OpenStreetMap
- Mapillary
- Python
- GNSS / RTK
- LiDAR
- Photogrammetry
- Point-cloud processing
- Remote sensing
- Image annotation
- Computer vision
- Spatial data science
- Mobile mapping systems
- 360° imagery collection
What Success Looks Like
You consistently deliver accurate, complete, geographically aligned, and well-documented ground-truth data with minimal need for re-collection.
Your work enables Urbanomy's engineering team to compare AI-generated maps against real-world conditions and build reliable transportation and infrastructure datasets that cities and agencies can use.
Why Join Urbanomy
This role gives you hands-on experience at the intersection of:
AI + GIS + Mapping + Transportation + Robotics + Urban Infrastructure
You will work directly with the OpalAI team on Urbanomy, a real-world transportation mapping and safety project supported by the U.S. Department of Transportation.
The role is particularly well suited for UCLA students, graduate students, recent graduates, and early-career professionals who want practical field experience with the real-world data behind mapping systems, computer vision, autonomous systems, and physical AI.
How to Apply
Please submit:
- Resume
- Relevant coursework — include course names and university
- Hardware you personally own or have access to — include make/model
- Relevant GIS, mapping, surveying, or field projects
- Software experience — ArcGIS, QGIS, Python, etc.
- Availability for field work and travel
- Driver's license/vehicle availability, if applicable
Candidates with a portfolio, map, GIS project, field survey, or example geospatial dataset are encouraged to include it.