GEOVIT
Context-Aware AI Geolocation
Hybrid Architecture in Action
Watch how the Vision-OCR conflict resolution system processes ambiguous inputs and arrives at accurate geolocation through context-aware validation.
The AI Triad
Three specialized AI systems work in concert to transform street images into precise geographic coordinates.
Vision
ViT Analysis
State-of-the-art Vision Transformer models analyze street-level imagery to extract visual features and landmarks.
- Building architecture recognition
- Landmark identification
- Environmental context analysis
- Visual pattern matching
Reading
OCR + NLP
Advanced optical character recognition combined with natural language processing decodes Turkish street signs and text.
- Turkish text extraction
- Street sign detection
- Shop name recognition
- Multi-font handling
Deduction
Cross-Verification
Intelligent reasoning engine cross-references multiple data sources to pinpoint exact locations with high confidence.
- Multi-source verification
- Confidence scoring
- Geographic constraint solving
- Historical data matching
The Engineering Challenge
Standard geolocation models treat cities as monolithic entities. Istanbul, however, presents a unique challenge with 39 districts sharing similar architectural features. GeoViT solves this by introducing a 'Context-Aware' layer that validates visual data against OCR-extracted text from street signage.
The Problem
Standard geolocation models treat cities as monolithic entities. Istanbul, with 39 districts sharing similar Ottoman-era architecture, defeats conventional approaches.
Vision Limitation
Visual features alone yield ~34% confidence on ambiguous inputs. Historic neighborhoods in Kadıköy, Beşiktaş, and Fatih share nearly identical streetscapes.
OCR Signal
Street signage, municipality markers, and shop names provide ground-truth text signals. These are extracted via custom-tuned Tesseract pipelines.
Context-Aware Fusion
A logic layer validates visual predictions against OCR-extracted text. When conflicts arise, the system queries a vector database to resolve ambiguity.
Data Flow Architecture
Result: 94.2% District-Level Accuracy
By fusing visual embeddings with text-based context validation, GeoViT achieves state-of-the-art accuracy on Istanbul's complex urban landscape — a 36x improvement over random baseline (2.6%).
Coverage Map
Real-time visualization of analyzed locations across Istanbul. Each point represents a geolocated street-level image.
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Built on Real Data
GeoViT is trained on a comprehensive dataset of Istanbul street imagery, covering all 39 districts with verified location data.
Training Images
street-level photographs
District Coverage
all Istanbul districts
Model Accuracy
district-level precision
Dev Phases
iterative improvements
Training Data Distribution
Complete breakdown of the training dataset across all 39 Istanbul districts. Every data point is collected ethically from public street-view sources.
Images per District (Top 15)
Geographic Split
District Data
| District | Side | Images | Area (km²) | Accuracy | Density |
|---|---|---|---|---|---|
| 01Fatih | European | 4,250 | 15.6 | 96.2% | 272.4/km² |
| 02Beşiktaş | European | 3,890 | 17.8 | 95.8% | 218.5/km² |
| 03Kadıköy | Asian | 3,650 | 25.2 | 94.5% | 144.8/km² |
| 04Üsküdar | Asian | 3,420 | 35.6 | 93.8% | 96.1/km² |
| 05Beyoğlu | European | 3,180 | 8.7 | 95.1% | 365.5/km² |
Data collected from public street-view APIs between 2023-2024. All images are processed in compliance with data protection regulations.
Project Evolution
From a simple prototype to a sophisticated geolocation system. Follow the engineering journey through 21 iterative development phases.
Initial Prototype
Basic geolocation for Fatih and Beşiktaş districts using ViT models.
Multi-District Expansion
Extended coverage to 15 districts with improved accuracy.
Big Data Integration
Scaled to 60,000+ images across all Istanbul districts.
Detective Mode
OCR integration for Turkish text and geocoding verification.
Multi-Modal Fusion
Combined visual, textual, and contextual features for enhanced accuracy.
Human-Eye Mode
Pedestrian zone analysis and human-centric scene understanding.