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Challenge

Build a Multimodal Flood and Satellite Exposure Analytics Agent with Google ADK

Satellite imagery and multimodal sensor signals are crucial for rapid flood loss verification following extreme storms. Build a multimodal flood assessment agent using Google Agent Development Kit (ADK) and Gemini 1.5 Pro. Achieve 90%+ alignment with expert structural damage classifications on satellite and aerial drone imagery.

Machine LearningHosted by Vera
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Challenge brief

What you are building

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

Develop a multimodal agent using Google ADK and Gemini models to process post-disaster satellite imagery alongside property risk records.

Delivery guide

How work is evaluated

Evaluation

Evaluates multimodal damage classification precision from paired satellite images and property metadata.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

valid_json_schema

Output complies with required multimodal assessment structure.

Binary check

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

Dimension 2

damage_grade_accuracy

Accuracy of structural damage severity classification (NONE, MODERATE, SEVERE) • target: 0.9 • range: 0-1

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

  • Implement multimodal agents using Google Agent Development Kit (ADK).

  • Leverage Gemini 1.5 Pro multimodal vision processing capabilities.

  • Combine satellite visual inputs with tabular elevation and hydrological metrics.

  • Validate inundation depth predictions against ground truth survey markers.

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of 200 post-flood satellite image pairs with associated elevation and parcel metadata.

How this agent runs

Evaluates multimodal damage classification precision from paired satellite images and property metadata.

Challenge input

Multimodal payload containing image_url and parcel_metadata

Google ADK

Google standard SDK for Gemini multi-modal agent workflows.

Evaluated output

JSON with flood_inundated (bool), estimated_depth_ft, and severity_grade

Checks for
  • Output complies with required multimodal assessment structure.
  • Accuracy of structural damage severity classification (NONE, MODERATE, SEVERE) • target: 0.9 • range: 0-1
Proof of success
  • Benchmark: MultimodalGeoBench
  • Damage Grade Accuracy target: 0.9
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Environment
  • Modal · Required
Action Space
  • Google · Optional
  • Google ADK · Optional
Policy Serving
  • Modal · Required

Evaluation contract

  • valid_json_schema · Weight 1
  • damage_grade_accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Build a Multimodal Flood and Satellite Exposure Analytics Agent with Google ADK