AI Ranks Warsaw and Kraków Behind Regional Cities for Street Quality

Methodology Behind the Urban Rankings

The ranking, published on June 18, 2026, relies on a proprietary algorithm developed by researchers at the Warsaw University of Technology in collaboration with data scientists from UrbanMeta. The system processed over 50,000 high-resolution images captured via street-level mapping services, evaluating each thoroughfare against six core metrics: facade maintenance, sidewalk width, tree canopy density, noise pollution levels, furniture placement, and visual clutter.

Unlike traditional surveys that rely on human sentiment, this project aimed to quantify urban design elements through objective digital observation. The technical framework utilizes deep learning convolutional neural networks (CNNs), which are trained to recognize specific architectural features and environmental markers. By training the model on datasets tagged for “livability” and “pedestrian comfort,” the researchers created a standardized scoring system that operates independently of local administrative definitions of a “well-designed” street.

The model assigns a numerical value to street quality by identifying patterns associated with high-density pedestrian activity and historical preservation. By removing subjective bias, we can identify which specific urban design choices correlate with higher livability scores.

Dr. Marek Kowalski, Lead Data Scientist at UrbanMeta

This methodology represents a shift in urban planning, moving away from qualitative “walkability audits” typically conducted by city planning students or municipal consultants. By using machine vision, UrbanMeta can analyze thousands of kilometers of streets in a matter of hours—a task that would take human surveyors months to complete. The algorithm specifically penalizes “visual clutter,” defined as an overabundance of disjointed signage, temporary barriers, and improperly placed street furniture that interrupts the natural flow of pedestrian movement.

Why Warsaw and Kraków Scored Lower

While Warsaw and Kraków remain the most visited cities in Poland, the AI report highlighted significant disparities in their street-level performance compared to smaller, mid-sized cities like Wrocław and Gdynia.

Warsaw struggled primarily with visual clutter and excessive signage, which the algorithm penalized heavily. The high density of commercial advertisements in the city center lowered its overall score in the “architectural harmony” category. In contrast, Kraków faced deductions due to the high volume of foot traffic relative to sidewalk capacity in the historic district, which the AI flagged as a persistent infrastructure bottleneck.

The report notes that while these cities boast significant historical assets, the “bottleneck effect” created by mass tourism in Kraków and the rapid commercialization of Warsaw’s main arteries resulted in lower scores for user experience compared to the more balanced street layouts found in cities like Poznań. The algorithm identifies these bottlenecks by measuring the ratio of sidewalk width to the average density of pedestrian movement captured in the imagery, flagging areas where the physical infrastructure is consistently overwhelmed by the volume of users.

Comparative Performance Across Polish Cities

The study categorized cities into three tiers based on their aggregate scores. The findings suggest a shift in how urban planners should prioritize infrastructure investments. The high performance of Wrocław, for instance, is attributed to a successful integration of “blue-green” infrastructure—the combination of water features and vegetation—that the model correlates with reduced ambient noise and higher reported user satisfaction in other European cities.

The data shows that cities prioritizing “micro-greenery”—the strategic placement of plants in dense urban corridors—consistently outperformed those that focused solely on large-scale park developments. This finding aligns with broader urban design research suggesting that the “street-level” experience is defined more by the immediate presence of greenery—such as street trees and planters—than by the total acreage of large, isolated municipal parks.

The Future of AI-Driven Urban Planning

Municipal authorities in the affected cities have responded to the findings with varying degrees of caution. Officials from the Warsaw City Planning Office indicated that while the AI data provides a useful diagnostic tool, it does not account for the logistical challenges of managing a rapidly growing metropolitan population. The tension remains between the “ideal” streetscape as envisioned by the algorithm and the functional requirements of a capital city that must accommodate high volumes of daily commuters, public transport, and commercial logistics.

The researchers plan to release an updated version of the assessment in late 2026, which will incorporate real-time air quality sensors to determine if there is a direct correlation between street aesthetics and environmental health. This expansion is designed to address one of the primary limitations of the current dataset: the inability to measure intangible environmental factors like particulate matter or heat island intensity. By layering sensor data onto visual analysis, the team intends to provide a holistic view of how street design influences the micro-climate of city centers.

As cities continue to integrate digital tools into their development strategies, the tension between historical preservation and modern efficiency will likely remain a central point of debate for local governments. The UrbanMeta project is part of a growing trend of “algorithmic urbanism,” where data-driven insights are increasingly used to justify capital expenditure on infrastructure, prompting ongoing discussions about the role of technology in shaping the public realm.

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