Encountered this architecture challenge while designing a building renovation lead identification system for a client. The same LLM visual pipeline proved reusable across multiple industries with zero additional development cost.
TL;DR
Feed Google Street View + Satellite images to an LLM, extract structured building attributes (glass type, orientation, facade material, condition), and generate a renovation potential score. The same pipeline — without modification — can produce targeted leads for window film installers and window covering retailers. POC cost for 150 buildings: ~$125.
Why This System
The US commercial building renovation market relies on manual canvassing to identify targets — low efficiency, limited coverage. The window film and solar shading industries face the same problem: no tool exists to identify high-intent buildings based on physical attributes.
Core insight: building facade images contain quantifiable commercial signals. LLM Vision can extract these signals zero-shot. Build once, reuse across industries.
Pipeline Design
8-step pipeline. Input: address or ZIP code. Output: scored building lead CSV.
Step 1 — Geocoding
Google Geocoding API converts user input to lat/lng coordinates.
Step 2 — Building Discovery
Google Places Nearby Search (radius = 500m) with type filters: office buildings, hotels, apartments, industrial facilities.
Step 3 — Deduplication & Filtering
Deduplicate by place_id, remove results missing geometry data.
Step 4 — Dual Image Retrieval (Parallel)
- Satellite: Maps Static API, zoom=18, 640×640px
- Street view: Street View Static API, 640×640px, fov=90
- Street View Metadata API (free) checks coverage before requesting images — avoids unnecessary billing
Step 5 — LLM Visual Perception
Two separate prompts (not merged — merging reduces structured output stability):
- Prompt 1 (satellite): floor count, building volume, window density
- Prompt 2 (street view): facade material, glass type, condition, occlusion level
{
"material": "glass_curtain_wall / brick / concrete / mixed",
"glass_type": "single / double / unknown",
"condition": "good / fair / poor",
"orientation": "N / S / E / W / mixed",
"estimated_age": "0-10 / 10-20 / 20+ years",
"occlusion_level": "none / minor / major",
"confidence": "high / medium / low"
}
Step 6 — Scoring Engine
Fixed-weight rule engine, 0–100 score:
| Condition | Score Added |
|---|
| glass_type = single-pane | +40 |
| material = aged (brick/concrete) | +20 |
| estimated_age > 20 years | +20 |
| condition = poor | +20 |
Confidence discount: medium × 0.8 / low × 0.6
Step 7 — Lead Enrichment
Places Details API called only for top 30% scored buildings. Returns: name, address, phone, website.
Step 8 — CSV Export
Full field output with empty manual annotation column for human review.
Street View Fallback Strategy
Buildings are never discarded. Three-tier graceful degradation:
| Scenario | Handling |
|---|
| Street view available | Full dual-image analysis |
| No street view coverage | Satellite-only analysis; material/glass marked unknown; confidence auto-downgraded to medium |
| Heavy occlusion | Same as above; flagged as priority manual annotation sample |
Commercial Extension
LLM-extracted fields are generic physical attributes — not bound to the renovation use case. Same fields, different filter conditions, different buyer.
Primary Verticals
Window Film Installers
Target signals: glass_type=single + orientation=W/S + window_density=high
Single-pane glass means poor thermal insulation. West/south-facing facades receive peak solar gain. High window density increases project scale. All three signals combined identify buildings with strong intent for window film installation.
Window Covering / Blind Retailers
Target signals: orientation=W/S + window_density=high (floor-to-ceiling windows → high order value)
The same high-scoring buildings are valuable to multiple non-competing buyers. One scored building can be sold as a lead to both verticals simultaneously.
Lead Flow
ZIP code input
↓
Building discovery + LLM analysis
↓
Top 30% scored buildings
↓
Field signals matched per vertical:
• Window film: glass_type=single + orientation=W/S
• Window coverings: orientation=W/S + window_density=high
↓
Sellable leads per vertical
One pipeline, multiple verticals. A 150-building POC scan costing ~$125 can generate leads for 2+ industries simultaneously. Marginal cost per additional vertical approaches zero.
Key Design Decisions
Why two separate prompts instead of one merged prompt?
Merging prompts when processing multiple images increases structured JSON instability — field omission rates rise. Two independent prompts each focus on a single image type, producing more reliable output.
Why exclude LangChain?
The system is a fixed linear pipeline with no dynamic decision-making. Introducing an agent framework adds debugging complexity with no real benefit.
Why not train a custom model?
UCL 2024 research confirms GPT-4 Vision can extract building age from facade images zero-shot, with no pre-labeling required. POC phase validates the zero-shot accuracy baseline first — fine-tuning only if needed.
POC Execution Plan
- Scale: 1–2 US cities, 2–3 ZIP codes each, targeting 100–150 buildings
- Validation: Manual ground-truth labeling → compare with LLM output → calculate per-field accuracy
- Decision gate: proceed to full development only after baseline accuracy is confirmed acceptable
Estimated API Cost — 150 Buildings
| API | Cost |
|---|
| Geocoding | ~$5 |
| Places Nearby Search | ~$10 |
| Street View Static | ~$35 |
| Maps Static (satellite) | ~$25 |
| LLM Vision | ~$50 |
| Total | ~$125 |
Research References
1. Housing Passport — World Bank (2019)
World Bank-supported project using street view + ML to automatically identify vulnerable buildings and generate a 'Housing Passport' record for each.
- Validates technical feasibility of street view + ML for building material and condition identification
- Processing speed: ~$1.50 per 300,000 images/hour after training
- Key difference: that project used proprietary street imagery + custom-trained models; this system uses Google Street View + LLM Vision zero-shot (no pre-labeling required, but accuracy must be POC-validated)
2. UCL — Zero-Shot Building Age Classification Using GPT-4 (ISPRS 2024)
University College London research using GPT-4 Vision for zero-shot building age classification from facade images, with no labeled training data required.
- Overall accuracy: 39.69% (coarse-grained classification)
- Mean absolute error: 0.85 decades (~8–9 years)
- Confirms LLM Vision can extract building age from facade images without training
- Glass type recognition has no published benchmark — accuracy must be measured in POC
Interested in a similar AI-powered lead generation system? Let's talk
