Bing Maps Case Study- Global Geospatial Operations

Client: Microsoft Bing Maps

Project Summary

Successfully scaled geospatial data acquisition and production pilot project into a steady state program producing high quality imagery and LiDAR data for:

• >90% of US roads for Microsoft’s Streetside product,
• >75% of US population and dozens of international cities for their photorealistic 3D city product.

Established processing and production workflows to train ML models on building footprint, road sign, license place, face, and other object detection for incorporation into proprietary base maps.

The Challenge

Microsoft Bing Maps sought to develop geospatial data products rivaling Google, specifically Streeside, global ortho, and 3D cities. After a successful pilot project, they needed to scale up the original vendor team of 50 operators to meet aggressive production goals while accounting for the seasonality of data collection. Initial snags in Microsoft’s software required a radical increase in manual production capacity to hit the project deadline on time, within budget, and to quality specs.

The Strategy

Core to our strategy was aligning our team of contractors around how awesome our project was. I accomplished this by clearly defining how each function was vital to reaching our overarching objectives and celebrating our accomplishments publicly and regularly as a combined vendor and Microsoft FTE team.

Moreover, by honing the ideal operator profile, aggressively recruiting, establishing a well defined org structure, and developing a detailed training program we scaled the production team from 50 to over 180 within four weeks. Space constraints and quality demands limited our ability to add more staff to accommodate the necessary production targets. By implementing creative incentives and adopting a leader-leader management philosophy, I effectively managed the rapidly established team while yielding the necessary average of 63 hours of production per operator per week for a sustained 12 week period.

At the same time, I facilitated feedback loops between our vendor production team and Microsoft’s program management and engineering teams to not only fix the initial software development snags but drive radical improvements in tooling and processing pipelines, reducing the cost of 3D city production by 15% year over year.

Upon accomplishing our initial delivery of 40 Streetside and 70 3D cities, we transitioned the team to steady state operations by carefully managing collection backlogs and production staffing to retain our talent while meeting quality requirements.

I developed a long term hiring and resource plan, creating detailed models of data collection capacities across North America based on staffing levels, seasonally variable sun angle, foliage, and daylight hours. These models enabled efficient staffing, minimal attrition, and the successful delivery of each milestone. This included scaling our Streetside collection fleet from a dozen sensors to >120 sensors staffed by over 300 drivers operating regionally across North America.?

Cross functional communication and coordination was paramount to ensuring smooth data transitions from acquisition to processing, production, QC, staging, and publication. By establishing clear organizational structures, cooperative leadership between functional roles, and providing opportunities for feedback and advancement we drove efficiencies and yielded <5% voluntary attrition over the life of the project.

Functional areas included:

Aerial Data Collection and Production

• AOI Definition
• Aerial Acquisition
• Ingest
• AT
• DSM Production
• 3D Edit

Streetside Data Collection and Production

• AOI Definition
• Streetside data acquisition
• Dispatch
• Ingest
• Imagery QC

Machine Learning Training

• Data center operations
• Object ground truthing and classification

Implementations

• Development of a Comprehensive Hiring and Onboarding Strategy
• Development of Cross Functional Organizational Structure
• Development Feedback Loops
• Cutting Edge Equipment Procurement Schedule, Including Buying Out NVIDIA GPUs
• Introduction of Creative Incentive Plans to Boost Team Production While Maintaining Morale
• Adoption of a Leader-Leader Management Philosophy to Foster Innovation and Team Scalability
• Implementation of Long-Term Resource Planning Models

The Results

Despite initial software setbacks, our team not only successfully met the initial aggressive delivery target on time and within budget, but went on to deliver on time, to spec, and on budget for 24 continuous months.

We produced high-definition 3D maps covering 75% of the US population, Streetside maps for over 90% of US roads within 2 years, and trained the models for Microsoft’s building footprint program. The project was subsequently sold to Uber.

KPIs

75% Coverage of US Population with High-Resolution 3D Imagery

90% Coverage of US Roads with High-Resolution Streetside Imagery

400% Scaled Production Team by 400% within 4 weeks

< 3% Voluntary Attrition

15% Reduction in cost of 3D city production year over year

>300 Drivers Staffed Across North America