Optimizing Urban Perception
Integrating Image Segmentation and Machine Learning in London
Project Overview
This research explores the relationship between street view imagery and crime rates in London using advanced computer vision and machine learning techniques. By analyzing the visual characteristics of urban environments, we aim to identify patterns that correlate with criminal activity and develop predictive models for urban safety assessment.
The project combines image segmentation, regression analysis, and reinforcement learning to create an automated framework for crime rate prediction based on street view data. This approach provides valuable insights for urban planning, policy-making, and community safety initiatives.
Research Question
Core Question
How can we quantify the correlation between street views and crime rates?
Can visual features in urban environments predict areas with higher crime incidence?
Research Goal
Develop automated tools for crime rate prediction using street view imagery
Create actionable insights for urban planning and safety improvement
Methodology
Data Collection
Crime data & Street view images from London
Segmentation
PSPNet with ADE20k dataset for image analysis
Regression
Random Forest & XGBoost for prediction
Optimization
SAC reinforcement learning for improvement
Image Segmentation
Original & Segmented Street View
PSPNet Architecture
PSPNet
Pyramid Scene Parsing Network with Pyramid Pooling Module for semantic segmentation
ResNet-50
50-layer CNN backbone with residual connections to avoid vanishing gradients
ADE20k Dataset
20,000+ images with 150 semantic categories for comprehensive scene understanding
Regression Analysis
Linear Regression
Random Forest
XGBoost
Reinforcement Learning Optimization
SAC (Soft Actor-Critic)
Reward Mechanism
Interactive Platform
Platform Features
- Upload street view images
- Real-time image segmentation
- Crime rate prediction
- AI-powered optimization
Technical Stack
Backend:
- • Flask server
- • PyTorch models
- • OpenCV processing
Frontend:
- • React interface
- • Real-time updates
- • Interactive visualization
Results & Evaluation
Key Achievements
- • Successfully correlated street view features with crime rates
- • Developed automated workflow for crime analysis
- • Innovative reward mechanisms in reinforcement learning
- • Created scalable platform for real-time analysis
Limitations
- • Limited database scale and diversity
- • Segmentation model precision needs improvement
- • Overall R-squared values below 0.5
- • Reinforcement learning requires optimization
