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Visual Analytics for AI-Generated Urban Infrastructure Maps

CS-GY 9223 - Fall 2025

Project Overview

Visual Analytics for AI-Generated Urban Infrastructure Maps

The Goal

Design and build visualization systems to help domain experts understand, debug, and analyze ML pipelines that generate urban infrastructure maps from aerial imagery.

The Tool

Tile2Net: A semantic segmentation system that extracts pedestrian networks from aerial imagery.

Motivation

Why Urban Infrastructure Maps Matter

Current Challenges

• Manual mapping is slow and expensive
• Data gaps in pedestrian infrastructure
• Outdated city datasets
• Accessibility planning requires detailed data

The Solution

Semantic Segmentation + Aerial Imagery

Automatically classify every pixel:

• Roads
• Buildings
• Sidewalks
• Crosswalks
• Vegetation

The Tile2Net Pipeline

State-of-the-art tool for extracting sidewalk and crosswalk networks from aerial imagery

The Challenge

The Black Box is Brittle

Multi-stage ML systems can introduce and compound errors at each stage:

Stage 1: Image Ingestion

• Different lighting conditions
• Seasonal variations
• Resolution mismatches

Stage 2: Segmentation

• Occlusions (trees, shadows)
• Faded markings
• Construction zones

Stage 3: Raster-to-Polygon

• Geometric artifacts
• Boundary smoothing errors
• Feature simplification issues

Stage 4: Polygon-to-Network

• Disconnected components
• Incorrect topology
• Jagged centerlines

The Problem

Limited Reliability

• Errors cascade through pipeline
• Hard to identify failure points
• Difficult to validate outputs
• Reduced trust from urban planners

Your Mission

Build interactive visualization tools to:

• Understand model behavior
• Debug pipeline stages
• Analyze output quality
• Increase trustworthiness

Project Ideas

Three Potential Directions

You can choose one direction or propose a hybrid/variation:

1. Idea A: The Segmentation Detective
   • Diagnose pixel-level segmentation failures
2. Idea B: The Network Quality Inspector
   • Assess topological and geometric quality
3. Idea C: The Urban Time Traveler
   • Analyze infrastructure evolution over time

Idea A: The Segmentation Detective

Goal & Key Questions

Goal

Build a visual analytics tool to diagnose and understand failures of the semantic segmentation model at the pixel and tile level.

Key Questions

• Where does the model succeed and where does it fail?
• What visual features correlate with specific error types?
• How does prediction confidence relate to accuracy?
• How do environmental factors (shadows, tree cover) impact performance?

Potential Features

Linked Views Dashboard

• Original aerial image
• Ground-truth annotations
• Model predictions
• Confidence maps

Error Overlay

• Highlight false positives
• Highlight false negatives
• Color-coded by error type

Interactive Tools

• Filter by error type
• Filter by confidence threshold
• Isolate problem regions

Magnification Lens

• Focus on specific regions
• View all layers simultaneously
• Compare predictions vs. truth

Idea B: The Network Quality Inspector

Goal & Key Questions

Goal

Create a visualization tool to assess the topological and geometric quality of the final pedestrian network graph.

Key Questions

• Where are breaks or disconnected components?
• Are intersections generated correctly?
• Can we flag geometric anomalies automatically?
• How does the network compare to baselines (e.g., OpenStreetMap)?

Potential Features

Quality-Coded Map

• Color edges/nodes by metrics
• Connectivity indicators
• Smoothness measures
• Segment length analysis

Automatic Flagging

• Dead-end detection
• Isolated subgraphs
• Click to inspect imagery

Network Comparison

• Diff view vs. OpenStreetMap
• Diff view vs. city data
• Overlay reference networks

Graph Metrics

• Node degree distribution
• Betweenness centrality
• Network connectivity
• High-level quality overview

Idea C: The Urban Time Traveler

Goal & Key Questions

Goal

Design a visual tool for analyzing the evolution of urban pedestrian infrastructure over time using historical aerial imagery.

Key Questions

• How has the pedestrian network changed over the last decade?
• Where have sidewalks/crosswalks been added or removed?
• Can we correlate changes with city planning initiatives?
• How do walkability metrics change over time?

Potential Features

Visual Diff Map

• Highlight changes between time periods
• Show added paths (green)
• Show removed paths (red)
• Show modified paths (yellow)

Timeline Animation

• Slider control
• Animate network evolution
• Year-by-year transitions

Temporal Dashboard

• Network map view
• Linked time-series charts
• Track metrics over time:
  • Total sidewalk length
  • Network density
  • Number of intersections

Case Study Example

Cambridge, MA: 23% change in crosswalks over 8 years

Getting Started

Resources: Codebase & Data

Primary Tool

Tile2Net

• Install and run the pipeline
• GitHub Repository

Aerial Imagery Sources

• NYC Planimetrics: High-res imagery for multiple years
• USGS NAIP: National aerial imagery
• Google Earth Engine: Vast satellite/aerial catalog

Ground Truth Data

For Comparison & Validation:

• OpenStreetMap (OSM)
  • Crowd-sourced global data
  • Detailed pedestrian features
• City GIS Portals
  • Official sidewalk data
  • Street centerlines
  • Planning datasets

Deliverables

What You Need to Submit

1. Project Proposal (Due Oct 21)

• Length: 1-2 pages
• Content: Chosen direction, research questions, datasets, timeline

2. Final Report (Due Dec 11)

• Length: 4-6 pages (conference paper format, e.g., IEEE VIS)
• Sections: Motivation, related work, system design, implementation, case study

What You Need to Submit (cont.)

3. Source Code

• GitHub repository with well-documented code
• Setup instructions
• Dependencies listed
• Example usage

4. Demo Video

• Length: 3-5 minutes
• Content:
  • System features showcase
  • Walk through usage scenario
  • Highlight key insights

Summary

Your Mission

The Challenge

Multi-stage ML pipelines for urban mapping are powerful but brittle.

The Solution

Build visual analytics tools to:

• Diagnose failures
• Validate outputs
• Build trust
• Enable exploration

Choose Your Path

• Idea A: Debug segmentation models
• Idea B: Inspect network quality
• Idea C: Track urban evolution

Impact

Help urban planners make data-driven decisions with trustworthy AI-generated maps.

Questions?

Next Steps

1. Review Tile2Net documentation
2. Explore available datasets
3. Choose your project direction
4. Start drafting your proposal