Skip to main content
Challenge

Build a CityJSON 2.0 Material and Surface Reflectance Auditor

Inspect 3D city models in CityJSON 2.0 format to verify surface material definitions and appearance mappings. Calculate urban surface solar reflectance indices to identify heat-island mitigation opportunities across city districts.

EngineeringHosted by Vera
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Inspect 3D city models in CityJSON 2.0 format to verify surface material definitions and appearance mappings. Calculate urban surface solar reflectance indices to identify heat-island mitigation opportunities across city districts.

Urban Sustainability Analyst

Load CityJSON 2.0 models containing Building objects and appearance material dictionaries.

Extract surface material associations, diffuse colors, and albedo properties across roof surfaces.

Compute average solar reflectance index for each building envelope and district cluster.

Generate a structured retrofit prioritization report for low-reflectance, high-heat roofs.

Heatmap visualization showing building roof polygons colored by albedo with filterable district tables.

Utilize standard CityJSON 2.0 sample models with appearance materials or mock CityJSON files with color arrays. Starter examples below are synthetic. They are not downloaded records.

CityJSON Extracts appearance object definitions, materials, and surface mapping indices from CityJSON 2.0 Section 6 and Section 1.

semantic-kernel, pydantic, matplotlib. Semantic Kernel orchestrates multi-step geospatial data queries and analytical synthesis prompts reliably. API-based hosted LLM for reasoning across geometric materials and urban thermal guidelines.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Extracts material properties from CityJSON appearance object dictionaries.

Calculates solar reflectance values across building roof geometry surfaces.

Flags roofs with solar reflectance below 0.30 as candidates for cool-roof retrofits.

```json [ { "input": { "city_objects": { "bldg-north": { "type": "Building", "geometry": [ { "type": "MultiSurface", "material": { "roof": "dark_asphalt" } } ] } }, "appearance": { "materials": [ { "name": "dark_asphalt", "diffuseColor": [ 0.1, 0.1, 0.1 ], "reflectance": 0.12 } ] } }, "expected_output": { "audited_buildings": 1, "cool_roof_candidates": [ { "building_id": "bldg-north", "reflectance": 0.12, "recommendation": "High priority for high-albedo coating retrofit" } ] } }, { "input": { "city_objects": { "bldg-south": { "type": "Building", "geometry": [ { "type": "MultiSurface", "material": { "roof": "white_membrane" } } ] } }, "appearance": { "materials": [ { "name": "white_membrane", "diffuseColor": [ 0.85, 0.85, 0.85 ], "reflectance": 0.75 } ] } }, "expected_output": { "audited_buildings": 1, "cool_roof_candidates": [] } } ] ```

Outputs advisory urban planning analytics; does not alter municipal building permitting statuses. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Outputs advisory urban planning analytics; does not alter municipal building permitting statuses. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Extracts material properties from CityJSON appearance object dictionaries. Calculates solar reflectance values across building roof geometry surfaces. Flags roofs with solar reflectance below 0.30 as candidates for cool-roof retrofits.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

Complete workflow

Load CityJSON 2.0 models containing Building objects and appearance material dictionaries. Extract surface material associations, diffuse colors, and albedo properties across roof surfaces. Compute average solar reflectance index for each building envelope and district cluster. Generate a structured retrofit prioritization report for low-reflectance, high-heat roofs.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

Correctness and evidence

Extracts material properties from CityJSON appearance object dictionaries. Calculates solar reflectance values across building roof geometry surfaces. Flags roofs with solar reflectance below 0.30 as candidates for cool-roof retrofits.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

Heatmap visualization showing building roof polygons colored by albedo with filterable district tables.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Outputs advisory urban planning analytics; does not alter municipal building permitting statuses.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Load CityJSON 2.0 models containing Building objects and appearance material dictionaries.

  • Extract surface material associations, diffuse colors, and albedo properties across roof surfaces.

  • Compute average solar reflectance index for each building envelope and district cluster.

  • Generate a structured retrofit prioritization report for low-reflectance, high-heat roofs.

Resources and assets

Reference links and supporting material

Dataset notes

Utilize standard CityJSON 2.0 sample models with appearance materials or mock CityJSON files with color arrays. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

Semantic Kernel

Agent Systems · Agent Frameworks

Evaluated output

No output checks are configured.

Evaluation checks
  • Load CityJSON 2.0 models containing Building objects and appearance material dictionaries. Extract surface...
  • Extracts material properties from CityJSON appearance object dictionaries. Calculates solar reflectance val...
  • Heatmap visualization showing building roof polygons colored by albedo with filterable district tables.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 1 configured tool

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Semantic Kernel · Optional

Evaluation contract

  • Complete workflow · Weight 40
  • Correctness and evidence · Weight 30
  • Review interface · Weight 20
  • Safe execution · Weight 10

Reference cases

  • 2 public reference cases

Workflow tasks

No workflow tasks are configured.

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a CityJSON 2.0 Material and Surface Reflectance Auditor