Title : Predicting human perception of the urban environment in a spatiotemporal urban setting using locally acquired street view images and audio clips

Objectives
Predict human perception of urban environments within spatial-temporal contexts using street images and local audio clips.
Explore the relationship between visual and auditory features of urban settings and how they are perceived by individuals.

Strategies and Methods
Data Collection: Utilize street-level images and audio clips as input data for modeling human perception.
Spatial-Temporal Analysis: Analyze spatial and temporal data to predict human reactions to diverse urban environments.
Machine Learning Models: Employ deep learning models, particularly convolutional neural networks (CNNs), for visual and auditory analysis.
Feature Extraction: Use image and audio processing algorithms to extract relevant environmental features and human responses.

Applications
Improve urban design and planning by considering human perception of city environments.
Optimize public spaces and streetscapes to enhance user experience.
Assess the impact of auditory and visual characteristics on public satisfaction and comfort.
Integrate with smart city systems to predict citizen responses at different times of the day.

Models and Algorithms
Deep Learning Models: For image and audio processing to extract environmental features.
Spatial-Temporal Analysis Algorithms: To simulate human perception over time and space.
Predictive Models: Combine visual and auditory data to offer comprehensive evaluations of urban settings.

Results
Combining visual and auditory data effectively predicts human perception of urban spaces.
Findings show that auditory and visual features significantly influence human emotions and behaviors in urban areas.
These insights can be applied to design better urban spaces that cater to public comfort and satisfaction.

Challenges and Limitations
Difficulty in collecting large-scale, accurate real-world urban data.
Time-consuming processing of complex visual and auditory datasets.
Limited generalizability of models to different urban and cultural environments.
Technical challenges in accurately merging audio and visual data.

Conclusion
This study highlights the importance of combining visual and auditory data to predict human perception of urban environments. Machine learning models can guide the design of urban spaces to improve the experience of residents and visitors.

Future Work
Develop more advanced models that incorporate additional environmental features (e.g., social and economic factors).
Improve model accuracy using larger and more diverse datasets.
Create intelligent tools for urban design and human behavior analysis based on environmental features.
Compare global datasets with local results to achieve a comprehensive understanding of urban perceptions.