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Phantom Capacity in Urban Pedestrian Networks πŸ™οΈπŸšΆβ€β™‚οΈ

An AI-Assisted Approach to Functional Walkability Using Street View Imagery and SAM 3

Paper Status Data Status Model

πŸ‘₯ Authors

Yusuf Eminoğlu 1 βœ‰οΈ, Hilmi Evren Erdin 1 βœ‰οΈ, Nurseli ŞanlΔ± 2 βœ‰οΈ

1 Department of City and Regional Planning, Faculty Of Architecture, Dokuz EylΓΌl University, Izmir
2 Department of City and Regional Planning, Graduate School, Istanbul Technical University, Istanbul πŸ“§ Contact: [yusuf.eminoglu@deu.edu.tr](mailto:yusuf.eminoglu@deu.edu.tr) | [evren.erdin@deu.edu.tr](mailto:evren.erdin@deu.edu.tr) | [sanli25@itu.edu.tr](mailto:sanli25@itu.edu.tr)

πŸ“– Abstract

Traditional urban planning frequently relies on nominal cadastral dimensions to evaluate pedestrian infrastructure, generating a measurement gap that ignores microscale street-level encroachments. This study introduces the concept of "phantom capacity"β€”the proportion of the designated pedestrian right-of-way functionally expropriated by static and transient obstaclesβ€”to measure actual walkability.

We evaluate 63 measurement nodes across seven distinct street axes in the dense urban center of Δ°zmir, TΓΌrkiye. By integrating Google Street View (GSV) imagery with the zero-shot panoptic segmentation capabilities of the Segment Anything Model 3 (SAM 3), we extract precise ground-plane obstacle footprints to calculate the Effective Clear Width (ECW).

The results demonstrate that approximately 35% of the surveyed pedestrian network is completely obstructed, driven primarily by vehicular parking and commercial furniture. Statistical analysis indicates that intra-morphology variance frequently exceeds inter-morphology variance (Kruskal–Wallis H = 7.474, p = 0.058), confirming that nominal street classifications fail to guarantee accessibility outcomes.

Keywords: Phantom Capacity; Urban Walkability; Segment Anything Model (SAM 3); Pedestrian Infrastructure; Google Street View (GSV).

πŸ“‚ Repository Structure

While the full manuscript is undergoing peer review, this repository serves as an open-access platform for the methodological outputs, high-resolution visual evidence, and supplementary materials associated with the study.

sam3paper/
β”œβ”€β”€ data/
β”‚   β”œβ”€β”€ images/                               # Original Google Street View (GSV) imagery
β”‚   └── processed/                            # SAM 3 processing outputs
β”‚       β”œβ”€β”€ annotated_images/                 # Visual overlays of detected features
β”‚       β”œβ”€β”€ pixel_masks/                      # Binary/categorical pixel masks for spatial calculations
β”‚       └── sam3_masks/                       # High-fidelity masks generated by SAM 3 capabilities
β”‚
β”œβ”€β”€ src/                                      # Open-Source Methodology Core Scripts
β”‚   β”œβ”€β”€ gsv_data_acquisition.py               # Algorithmic imagery harvesting via GSV API
β”‚   β”œβ”€β”€ sam3_panoptic_segmentation.py         # The primary zero-shot segmentation model logic
β”‚   β”œβ”€β”€ baseline_pixel_segmentation.py        # Semantic mapping benchmark logic
β”‚   β”œβ”€β”€ metrics_extraction.py                 # Extrapolating ECW and physical pixel dimensions
β”‚   └── spatial_bottleneck_analysis.py        # Root-cause inference and topological mapping 
β”‚
└── supplementary/                            # Extended datasets and figures
    β”œβ”€β”€ figs01.png                            # Supplementary Figure 1
    β”œβ”€β”€ figs02.png                            # Supplementary Figure 2
    β”œβ”€β”€ figs03.png                            # Supplementary Figure 3
    β”œβ”€β”€ TableS1_Node_Level_Data_p1.png        # Node-Level Metrics & Measurements (Part 1)
    └── TableS1_Node_Level_Data_p2.png        # Node-Level Metrics & Measurements (Part 2)

Highlights of the Open Data & Code:

  • src/: Our end-to-end framework. The provided Python scripts contain our primary SAM 3 architectural integration, enabling independent researchers to replicate the spatial geometry extraction methodology.
  • data/processed/sam3_masks/: Demonstrates the cutting-edge zero-shot segmentation of urban obstacles using Meta's SAM 3 framework.
  • supplementary/TableS1_...: Includes the extensive dataset (Table S1) covering exact spatial clearances and ECW (Effective Clear Width) metrics for 63 cross-sectional nodes. It was excluded from the main manuscript text due to word limit constraints, but remains pivotal for replication.

πŸ”¬ Methodology Brief

Our methodology hinges on an automated computer vision pipeline:

  1. Acquisition: Systematic sampling of ground-level vistas via Google Street View.
  2. Segmentation: Zero-shot panoptic extraction of pedestrian zones vs. urban obstacles utilizing SAM 3.
  3. Quantification: Algorithmic calculation of the Effective Clear Width (ECW) versus Nominal Width.
  4. Analysis: Extraction of the Phantom Capacity ratio to re-evaluate functional walkability in dense typologies.

πŸ“‘ Citation & License

If you utilize the datasets, segmentation masks, or the phantom capacity conceptual framework in your research, please cite this repository. A formalized DOI / bibtex citation will be available upon publication.

(C) 2026 Yusuf Eminoğlu, Hilmi Evren Erdin, Nurseli Şanlı - All rights reserved.