Street-Level Geographic Classification
Global Logistics Platform
Country-level classification from street-level imagery for fleet and logistics operations.
A logistics platform needed to verify and classify the geographic origin of driver-submitted photos across 40+ countries. Manual review was slow and inconsistent, with regional experts often disagreeing. Misclassified images led to incorrect routing and compliance issues in cross-border operations.
Fine-tuned on diverse street-level imagery, the Moondream model identifies the country of origin from visual cues like road markings, signage styles, vegetation, and architecture. Supervised fine-tuning on a curated geolocation dataset boosted accuracy from near-random to production-grade classification.
- Classification accuracy improved from 3.7% to 40.2%
- Covers 40+ countries with a single model
- Reduced manual geographic verification by 75%
- Enabled automated compliance checks for cross-border shipments
Complete Vision AI Stack
This solution uses Moondream's integrated stack from model training through production deployment. Every layer is designed to work together, so you go from problem to deployed system without stitching together tools from different vendors.
AI Model Layer
Base Model
Moondream 3
Fine-Tuning
SFT via Lens
Production Model
Moondream 2
Deployment Layer
Inference Engine
Photon
Target Hardware
NVIDIA L40
Deployment
Cloud
Training Method
SFT
Training Steps
1000
Task Type
query
Accuracy
28.6% → 71.1%
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