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.
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.
How work is evaluated
Evaluates multimodal damage classification precision from paired satellite images and property metadata.
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.
valid_json_schema
Output complies with required multimodal assessment structure.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
damage_grade_accuracy
Accuracy of structural damage severity classification (NONE, MODERATE, SEVERE) • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
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.
Reference links and supporting material
Dataset of 200 post-flood satellite image pairs with associated elevation and parcel metadata.
Challenge setup
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
- Output complies with required multimodal assessment structure.
- Accuracy of structural damage severity classification (NONE, MODERATE, SEVERE) • target: 0.9 • range: 0-1
- Benchmark: MultimodalGeoBench
- Damage Grade Accuracy target: 0.9
- 1 public reference case
- Python execution harness
- Python 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.
View technical recipe
Configured tools
- Modal · Required
- Google · Optional
- Google ADK · Optional
- Modal · Required
Evaluation contract
- valid_json_schema · Weight 1
- damage_grade_accuracy · Weight 1
Metrics
- damage_grade_accuracy · Target 0.9
Benchmarks
- MultimodalGeoBench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: Multimodal payload containing image_url and parcel_metadata
Output: JSON with flood_inundated (bool), estimated_depth_ft, and severity_grade
Execution harness: Python
Evaluation tools
- Google ADK · Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.