Urban SDK vs. Manual Surveys: Time and Accuracy Gains for Transportation Departments

Urban SDK helps organizations diagnose roadways faster and more accurately by using AI, satellite imagery, and connected vehicle data.

Collecting transportation data has historically been a laborious task. Traffic counts, road measurements, sidewalk inventories – these often required teams on the ground, clipboards or sensors in hand, working for weeks or months to cover a city. Urban SDK’s approach to data collection turns this paradigm on its head, using technology like satellites, AI, and connected vehicle data to gather the same information faster, more frequently, and often with greater accuracy. 

In this article, we compare the conventional method of manual surveys with the modern Urban SDK platform, highlighting the time savings and accuracy improvements that transportation departments can achieve.

The Burden of Manual Road Surveys

Every transportation department is familiar with the challenges of manual data collection. Whether it’s a traffic engineering team doing speed studies with radar guns, or interns measuring sidewalk widths with measuring wheels, the process is time-consuming, expensive, and prone to gaps.

 Key drawbacks of traditional surveys include:

1. High labor and cost 

Manual road inventories require staff or contractors physically in the field. This might mean driving every road with specialized equipment or walking every block for a sidewalk audit. It’s slow and costly – one study found sidewalk inventories cost $86 to $826 per mile to complete, even for small cities. Large agencies spend tens of thousands of dollars and hundreds of staff hours on comprehensive surveys.

2. Data quickly becomes outdated

Cities are dynamic. By the time a field survey finishes collecting data on one end of town, new developments or changes might have occurred on the other end. As a report noted, “by the time a manual survey is finished, parts of the city may have changed,” making it difficult to keep data current. This lag means decisions are often based on stale information.

3. Incomplete coverage and detail

Even well-funded efforts can miss details. Field crews might skip low-traffic side streets or overlook minor features. Some agencies try to crowdsource data or use spot checks, but that can leave blind spots. For example, a team might note whether a sidewalk exists but not capture its width or condition. Important measurements can be omitted, and minor roads or features might be unaccounted for.

4. Human error and inconsistency

Manual data entry and observation can introduce errors. Different surveyors might classify things inconsistently (what counts as “minor cracking” in pavement, for instance). Transcription mistakes or faulty equipment calibration can slip in. The result is data that may require extensive cleaning – or may simply be less precise.

5. Labor-intensive processing

After collection, there is the task of compiling notes, spreadsheets, or CAD drawings. Merging datasets from different teams, correcting errors, and formatting for analysis is tedious. Many agencies still rely on “manual auditing and tracing” of assets in GIS, which one expert described as so laborious that it often gets skipped until absolutely necessary. This creates a bottleneck where even after field work, turning data into actionable insights is slow.

The net effect of these issues is that manual surveys often cannot scale to meet today’s planning needs. Departments might do a full traffic count program only once every few years, or update their road condition index sporadically. Meanwhile, decisions have to be made in the interim without solid data. As urban areas grow and change faster than ever, this reactive, infrequent model is increasingly problematic.

Urban SDK’s Automated Approach

Urban SDK offers a fundamentally different method: leverage technology to collect data continuously and citywide, with minimal need for field labor. How is this possible? Urban SDK combines satellite and aerial imagery, machine learning, and connected vehicle data to generate rich datasets. Here are some core aspects of the approach:

1. Satellite and aerial imagery analysis

Instead of sending crews out to measure physical features, Urban SDK uses high-resolution imagery to detect and measure road attributes. Through advanced computer vision (AI algorithms), the platform can identify features like road edges, lane markings, sidewalks, bike lanes, medians, and more from above. It can even classify pavement types and delineate the width of each lane or shoulder. This means a task that would take surveyors months (driving and measuring every road) can be done in a fraction of the time by AI. The entire jurisdiction’s roads are analyzed in one sweep, ensuring no segment is missed.

2. Connected vehicle and GPS data

For traffic metrics such as speed, volume, and travel times, Urban SDK taps into anonymized data from millions of connected vehicles and mobile devices. This eliminates the need for setting up physical traffic counters or sensors. Instead of manual counts on a few roads, the platform can estimate hourly speeds and volumes on virtually every road in the city by analyzing telemetry data. This provides continuous coverage – not just one-day counts, but ongoing data streams that update regularly.

3. Automated updates

Because imagery and connected data are continuously collected (satellites might pass overhead weekly; vehicles generate data daily), Urban SDK can update the road inventory and traffic datasets far more frequently than manual methods. Essentially, it enables a living dataset. For example, if a new bike lane is added or a road is reconfigured, updated imagery will reflect that change and the AI can update the inventory. If traffic patterns shift due to a new development or seasonal changes, the data feeds will show that in near real-time.

4. Centralized cloud platform

All this data is processed and delivered through a cloud-based platform that city staff can access from the office (or anywhere). There’s no need to manually merge spreadsheets; the data comes already integrated and geospatially referenced. Users get intuitive dashboards and maps where they can query any road segment and see its characteristics, traffic stats, and even safety metrics.

Time Savings: From Months to Minutes

The difference in speed between manual surveys and Urban SDK’s automated collection is dramatic. What once took months of field work can now be done in days or even hours of processing. 

At Urban SDK, we use AI to generate a full citywide inventory of sidewalks, bike lanes, and other roadway features in a fraction of the time it would take traditional survey teams. One city that needed a road asset data update—a project that normally ties up staff for an entire summer—received fresh, GIS-ready data for the entire network within a week through our data subscription.

‍

Another angle is the time to access information. With manual processes, if a question arises (say, “How many miles of 10-foot lanes do we have, and where are they?”), it might take days of pulling files or sending someone out to measure. 

With Urban SDK, an analyst can query that in seconds and instantly visualize it on a map. Transportation departments report major efficiency gains, as routine inquiries or analyses that once took weeks can be turned around almost immediately. In the words of one municipal official, having the data at their fingertips means they are not making nearly as many trips to the field anymore. Complaints that used to require a special study can now be answered with a few clicks, freeing staff to focus on solutions rather than data gathering.

Crucially, faster data collection doesn’t mean sacrificing quality – in fact it enables better quality through currency. Cities can get monthly or quarterly updates to key metrics instead of yearly at best. This means decisions (like timing traffic signals or identifying speeding hotspots) are based on current conditions, not outdated info. When budgets or safety plans need to be justified, the data is readily available and recent, strengthening the case for action.

Accuracy and Detail: Human vs. Machine

A common concern is whether an automated, AI-based survey can be as accurate as human eyes on the street. The experience so far shows that Urban SDK’s data is exceptionally accurate – often matching or exceeding human-collected data in quality. Here’s why:

1. Consistency

AI algorithms apply the same criteria everywhere, eliminating the inconsistency between different surveyors. If the model is trained to identify a “bike lane” based on certain pavement markings and width, it will do so uniformly citywide. Humans might interpret marginal cases differently, but the AI has one rulebook. This leads to a very uniform dataset where every segment is classified with the same logic.

2. Precision measurements

Using high-resolution imagery and Lidar data, features can be measured down to a very fine resolution (centimeters). Urban SDK provides precise measurements down to the foot for features like lane width or sidewalk width. Survey crews with tape measures could achieve similar precision in theory, but not across an entire city in a timely way. More often, field data ends up being rough estimations for the sake of speed. The automated approach doesn’t face that trade-off.

3. Comprehensiveness

Machines don’t get tired or skip over things. If there’s a tiny side street with one house, it will still be processed by the algorithm. This comprehensive coverage ensures no gaps in the inventory. Many cities have “known unknowns” – perhaps they never fully catalogued all private streets, or they lack inventory of older neighborhood sidewalks. The satellite-driven inventory can capture it all in one sweep, discovering assets that might have been overlooked.

4. Capturing hidden or hard-to-survey features

Some infrastructure is challenging to survey on the ground – for example, wide intersections, complex interchanges, or roads obscured by foliage. Aerial imagery combined with infrared or other spectral data can sometimes detect road features under tree canopy that a ground survey might miss (e.g., a sidewalk under dense trees). 

With Urban SDK, cities can detect roadway features even under tree cover or in low-light conditions—areas where older systems often miss critical data. Our connected vehicle insights also reveal traffic patterns on roads that might otherwise be overlooked, eliminating the guesswork from planning.

In Contra Costa County, for instance, our AI mapping uncovered previously undocumented bike lanes and sidewalk segments—filling data gaps and giving planners a more complete picture of their infrastructure.

5. Validated accuracy

Urban SDK aligns its methods with ground truth whenever possible. The platform has been tested against known datasets and has shown parity with manual counts and surveys. According to a 2024 study, combining mobile LiDAR and satellite imagery with AI yielded a “scalable pedestrian infrastructure inventory” with high accuracy, validating the approach. Transportation departments that have adopted Urban SDK often do spot checks – and they find the data holds up. In fact, because the data can be updated frequently, it may reflect recent changes more accurately than a once-a-year field audit.

The result is that cities can trust the data while benefiting from the convenience. One city engineer commented that having this comprehensive, high-quality data at our fingertips means staff can now focus on solving problems rather than gathering data. And importantly, they noted the data quality is as good as – if not better than – a human survey. Fewer errors, more detail, and regularly updated information all contribute to superior outcomes.

Transforming Transportation Department Workflows

The combination of speed and accuracy gains has a transformative effect on how transportation agencies operate:

1. Resource reallocation 

Hours that would have been spent setting up counters or walking miles of roadway can be redirected to analysis, design, and community engagement. Planners and engineers can work on solutions and planning rather than data collection logistics. This is crucial at a time when many public agencies face staffing constraints – essentially, automation acts as a force multiplier for a small team.

2. Faster decision cycles

When a question or issue arises – say a council member asks for a traffic calming study on a neighborhood street – staff can respond in days rather than months. They might pull up Urban SDK’s data, find that the street indeed shows 85th percentile speeds 10 MPH over the limit, and prepare a recommendation almost immediately. This responsiveness builds trust with the public and officials, showing that the department is data-driven and timely. One police department noted they could now validate citizens’ speeding complaints much quicker and pinpoint enforcement needs almost in real-time.

3. Proactive management 

With continuous data, departments can move from reactive mode (responding after a problem fully manifests) to proactive mode. For example, rather than waiting for multiple crashes to occur at a spot, the agency can monitor the collision risk index of roads and intervene at high-risk locations before the next crash. They can also quickly evaluate the impact of interventions – if a new bike lane is installed, the team can watch the speed data and crash data over the following months via Urban SDK to see if speeds dropped and collisions went down.

4. Better communication and transparency

Having concrete data readily available makes it easier to communicate decisions. Transportation departments can show constituents charts and maps of the data to explain why a certain change is needed (e.g., “85% of drivers on this road are speeding, which is why we’re adding traffic calming”). The objectivity of data can turn contentious debates into collaborative problem-solving.

In summary, Urban SDK’s platform offers a powerful alternative to manual surveys, delivering comprehensive, up-to-date road and traffic data with unprecedented efficiency. Cities that leverage it have reported significant time savings and improved accuracy in their data, leading to smarter investments and safer streets. While manual expertise will always play a role – engineers still need to ground-truth certain conditions and exercise judgment – the heavy lifting of data gathering can now be offloaded to technology.

Transportation officials should consider the opportunity cost of sticking to legacy methods. Every week spent counting cars or measuring lanes by hand is time that could be spent designing improvements. By embracing automated data solutions with Urban SDK, agencies can redirect their energy toward analysis and action, confident that their data is current and credible. The result is a more agile department that can keep pace with the demands of modern city mobility.

Ready to modernize your data collection? Urban SDK offers transportation departments a chance to leap from the 20th-century clipboard approach to a 21st-century AI-driven workflow. The payoff is evident in time saved, accuracy gained, and better outcomes for the traveling public. It’s not just about doing things faster – it’s about doing more with the limited time and budget agencies have, ultimately delivering safer and more efficient transportation systems.

‍