Across industries such as urban administration, public data management, real estate, construction, and media archiving, there is a growing need to efficiently process and organize thousands to millions of images. Stackr is an AI-powered solution designed to automatically analyze and structure large-scale image data, transforming raw photos into meaningful, searchable information.
Solution Overview
When images of urban environments, buildings, and landscapes are uploaded,
Stackr’s AI automatically understands the visual content and performs the following tasks:
Analysis of key objects and scenes within images
Automatic extraction of keywords such as building names and locations
Generation of detailed scene descriptions (captions)
Detection and extraction of visible text (e.g., signs, plaques)
Automatic face anonymization (blurring) for privacy protection
This enables full automation of manual processes such as classification, tagging, and validation,
significantly improving both speed and accuracy of data processing.
Core Features
Automated Image Metadata Generation
AI analyzes key elements such as buildings, roads, and natural environments
Automatically extracts keywords including place names, building names, and categories
Generates human-readable scene descriptions
Produces structured metadata optimized for search, indexing, and data management
Automated Privacy Protection (Face Anonymization)
Detects all human faces within images
Automatically applies blur to protect identity
Ensures consistent quality even at large scale
Essential for public data release and compliance with privacy regulations
Optimized for Large-Scale Processing
Batch processing of thousands to millions of images
High-performance pipeline with parallel processing
Deployable in both cloud and on-premise environments
Detects all human faces within images
Automatically applies blur to protect identity. (150 faces)
Key AI Modules
Stackr goes beyond basic image analysis by combining
multimodal AI, search technology, and recognition systems to achieve high accuracy.
① Image Retrieval Module (Vector Similarity Search)
Converts visual features into high-dimensional vectors using a vision encoder
Measures similarity between images to identify buildings and locations
Applies Content-Based Image Retrieval (CBIR) technology
Benefits:
Accurate building and location identification
Grouping of visually similar images
Improved keyword generation accuracy
② Text Extraction Module (OCR + Correction Pipeline)
Precisely detects and extracts text from signs, plaques, and building exteriors
Uses OCR for text recognition, followed by dictionary-based correction
Enhances accuracy by compensating for limitations of multimodal models
Benefits:
Reliable extraction of building and organization names
Improved search and classification quality
Increased usability of structured data
③ Face Detection & Anonymization Module
Automatically detects faces within images
Robust performance across various angles and distances
Applies automatic blur to detected face regions
Benefits:
Compliance with privacy regulations
Reduced legal risk in public data usage
Fully automated anonymization workflow
Expected Benefits
By adopting Stackr, organizations can achieve:
Maximized Operational Efficiency
→ Reduce manual processing time by over 90%Improved Data Quality
→ Consistent and accurate metadata generationEnhanced Search & Usability
→ Enable keyword-based search and visual similarity retrievalReduced Legal Risk
→ Automated face anonymization for privacy compliance
Use Cases
Urban infrastructure and facility management (public sector)
Smart city and digital twin projects
Real estate and construction site documentation
Media and news archive management
Tourism and mapping data platforms