Build a Multimodal Building Inspection Agent with Google ADK and Gemini
Engineering teams manually inspecting legacy office blueprints miss critical MEP and core structural bottlenecks prior to conversion planning. Build a Google ADK multimodal agent leveraging Gemini to process architectural floor plans and output structural feasibility reports with 88% recall on risk flags.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct a multimodal AI agent using Google Agent Development Kit (ADK) and Gemini to analyze architectural blueprint images and structural text records.
How work is evaluated
Evaluates detection precision and visual recall for architectural blueprint structural bottleneck analysis.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Risk Recall Test
Ensures all known architectural blockers in test images are flagged.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Visual Inspection Recall
Percentage of actual structural bottlenecks recalled. • target: 0.88 • 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 image and text processing pipelines using Google ADK and Gemini.
Extract structural elements (shear walls, elevator cores, plumbing stacks) from blueprint images.
Generate standardized conversion risk matrices linking visual findings to engineering constraints.
Reference links and supporting material
Multimodal dataset containing office blueprint layout images, spatial metadata, and expert structural audit reports.
How this agent runs
Evaluates detection precision and visual recall for architectural blueprint structural bottleneck analysis.
Challenge input
JSON with image_uri, floor_level, structure_type
Google ADK
Assigned framework for building Vertex AI and Gemini agents.
Evaluated output
JSON with load_bearing_wall_detected, core_shaft_count, plumbing_riser_bottlenecks, risk_flag_count
- Ensures all known architectural blockers in test images are flagged.
- Percentage of actual structural bottlenecks recalled. • target: 0.88 • range: 0-1
- Benchmark: Multimodal_Architecture_Eval
- Visual Inspection Recall target: 0.88
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Modal · Optional
- Google Gemini · Required
- Google ADK · Optional
- Modal · Optional
Evaluation contract
- Risk Recall Test · Weight 1
- Visual Inspection Recall · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.