Climate Heat Stress Risk Assessment using Mastra AI and o3
Rising European temperatures require real-time risk assessment for agricultural and municipal property portfolios under extreme thermal stress. Build an automated risk scoring workflow using Mastra AI and o3 model reasoning to generate explainable heat vulnerability indexes. Achieve a Mean Absolute Error under 0.05 on risk scores.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an automated climate vulnerability scoring service using Mastra AI TypeScript workflows paired with OpenAI o3 model reasoning.
How work is evaluated
Evaluates property heat vulnerability index predictions against geospatial ground truth.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
score_range_check
Vulnerability score must be between 0.0 and 1.0.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
mae
Mean Absolute Error on vulnerability index score • target: 0.04 • range: 0-0.2
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct Mastra AI agent workflows in TypeScript with structured state memory
Integrate o3 model reasoning for complex geospatial climate vulnerability scoring
Process raster geospatial thermal maps and municipal vulnerability indices
Achieve MAE < 0.05 against ground-truth climate vulnerability metrics
Reference links and supporting material
Geospatial dataset of 2,000 European urban properties with microclimate features and historical thermal loss values.
Challenge setup
Evaluates property heat vulnerability index predictions against geospatial ground truth.
Challenge input
JSON object with latitude, longitude, building_type, urban_heat_island_index, and green_cover_pct
Mastra AI
Offers TypeScript-native workflow state engines and tool integrations.
o3
Provides step-by-step logical reasoning over complex geospatial risk relationships.
Evaluated output
JSON object with vulnerability_score (float 0-1) and primary_risk_factor (string)
- Vulnerability score must be between 0.0 and 1.0.
- Mean Absolute Error on vulnerability index score • target: 0.04 • range: 0-0.2
- Benchmark: ClimateVulnerabilityBench
- Mae target: 0.04
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- Mastra AI · Optional
- Baseten · Optional
- o3 · Required
- Mastra AI · Optional
Evaluation contract
- score_range_check · Weight 1
- mae · Weight 1
Metrics
- mae · Target 0.04
Benchmarks
- ClimateVulnerabilityBench
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with latitude, longitude, building_type, urban_heat_island_index, and green_cover_pct
Output: JSON object with vulnerability_score (float 0-1) and primary_risk_factor (string)
Execution harness: JavaScript
Evaluation tools
- Mastra AI · Agent Framework
- o3 · Models
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