# An unsupervised approach to Geographical Knowledge Discovery using street level and street network images

## TL;DR

This paper introduces an unsupervised method called ConvPCA that extracts meaningful latent features from street-level and network images, aiding geographic knowledge discovery and urban characteristic prediction.

## Contribution

It presents a novel unsupervised approach, ConvPCA, for extracting interpretable latent variables from urban images for geographic analysis.

## Key findings

- ConvPCA achieves comparable accuracy to traditional dimension reduction methods.
- Latent components enable meaningful geographical and visual explanations.
- The approach predicts urban features like street quality effectively.

## Abstract

Recent researches have shown the increasing use of machine learn-ing methods in geography and urban analytics, primarily to extract features and patterns from spatial and temporal data using a supervised approach. Researches integrating geographical processes in machine learning models and the use of unsupervised approacheson geographical data for knowledge discovery had been sparse. This research contributes to the ladder, where we show how latent variables learned from unsupervised learning methods on urbanimages can be used for geographic knowledge discovery. In particular, we propose a simple approach called Convolutional-PCA(ConvPCA) which are applied on both street level and street network images to find a set of uncorrelated and ordered visual latentcomponents. The approach allows for meaningful explanations using a combination of geographical and generative visualisations to explore the latent space, and to show how the learned representation can be used to predict urban characteristics such as streetquality and street network attributes. The research also finds that the visual components from the ConvPCA model achieves similaraccuracy when compared to less interpretable dimension reduction techniques.