Product System / Portfolio Ready

Dallas 3D Urban Geometry Lab

Reproducible downtown Dallas model for height provenance, line-of-sight coverage, and A* routing.

Research system / portfolio readyStrongExperimentPublished record

Project Dossier

Portfolio ReadyStrong
Category
Product System
Type
Experiment
Status source
Project model

A structured read on the build.

The page uses approved project data and keeps current-state boundaries visible.

Portfolio Ready

Dallas 3D turns 1,553 OpenStreetMap building footprints into a traceable LOD1-style city model, then uses the geometry for sampled visibility coverage and fixed-altitude path-planning experiments.

Stack, status, evidence, and public actions are rendered from the typed project record.

Oblique Blender render of the Dallas LOD1 building model with an A-star route and selected visibility observers

Category

Product System

Type

Experiment

Priority

Strong

Overview

What this project is

Dallas 3D turns 1,553 OpenStreetMap building footprints into a traceable LOD1-style city model, then uses the geometry for sampled visibility coverage and fixed-altitude path-planning experiments.

GeospatialComputational geometry3D modelingPath planning

Problem

Why it matters

A visually varied city mesh is not enough for defensible geometry research when most building heights are missing and generated values are not traceable.

Solution

Approach

A reproducible pipeline that validates and projects OSM geometry, labels every height by provenance, exports an inspectable city mesh, and runs transparent 2.5D coverage and routing baselines.

Architecture

System shape and stack

Data Integrity

Uncertainty stays visible

Explicit OSM heights receive high confidence, levels-derived heights receive medium confidence, and the deterministic typology-and-area fallback remains labeled low confidence instead of manufacturing random high-rises.

Visibility

Coverage through real building prisms

A 2.5D ray test evaluates sampled aerial observer-to-ground-target lines against building footprints and heights, then a greedy set-cover baseline selects six observers with 93.33% sampled coverage.

Path Planning

Altitude changes the obstacle field

The fixed-altitude experiment rasterizes buildings that violate vertical clearance and applies eight-neighbor A* to produce a 4.99 km route with a 1.015× detour ratio.

Research Boundary

A geometry lab, not flight guidance

The project documents its flat-terrain, flat-roof, sampled-coverage, and fixed-altitude assumptions and does not claim legal, safe, continuous, or globally optimal UAV operation.

  • Python
  • GeoPandas
  • Shapely
  • Trimesh
  • OSMnx
  • Blender
  • Pytest
  • GitHub Actions

Technical Highlights

Visible technical signal

  • Deterministic height enrichment with source and confidence on all 1,553 buildings
  • Six greedy observer samples cover 93.33% of 240 sampled targets
  • A* produces a 4.99 km route through an altitude-dependent obstacle grid

What It Proves

Builder signal

Ability to connect geospatial data engineering, computational geometry, algorithm design, 3D tooling, research boundaries, and reproducible presentation.

Boundaries

Context that should stay visible

Building footprints and tags © OpenStreetMap contributors, available under the ODbL.

Experiment metrics are reproducible outputs from the checked-in configuration. They are not real-world flight-performance claims.