Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding

01/29/2020
by   Zhecheng Wang, et al.
3

Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods. Existing works relied on either inter- or intra-region connectivities to generate neighborhood representations but failed to fully utilize the informative yet heterogeneous data within neighborhoods. In this work, we propose Urban2Vec, an unsupervised multi-modal framework which incorporates both street view imagery and point-of-interest (POI) data to learn neighborhood embeddings. Specifically, we use a convolutional neural network to extract visual features from street view images while preserving geospatial similarity. Furthermore, we model each POI as a bag-of-words containing its category, rating, and review information. Analog to document embedding in natural language processing, we establish the semantic similarity between neighborhood ("document") and the words from its surrounding POIs in the vector space. By jointly encoding visual, textual, and geospatial information into the neighborhood representation, Urban2Vec can achieve performances better than baseline models and comparable to fully-supervised methods in downstream prediction tasks. Extensive experiments on three U.S. metropolitan areas also demonstrate the model interpretability, generalization capability, and its value in neighborhood similarity analysis.

READ FULL TEXT

page 5

page 6

page 7

research
05/06/2021

Learning Neighborhood Representation from Multi-Modal Multi-Graph: Image, Text, Mobility Graph and Beyond

Recent urbanization has coincided with the enrichment of geotagged data,...
research
07/18/2018

Take a Look Around: Using Street View and Satellite Images to Estimate House Prices

When an individual purchases a home, they simultaneously purchase its st...
research
02/25/2023

Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Prediction

Monitoring sustainable development goals requires accurate and timely so...
research
01/04/2023

Detecting Neighborhood Gentrification at Scale via Street-level Visual Data

Neighborhood gentrification plays a significant role in shaping the soci...
research
09/20/2023

A representation-learning approach for insurance pricing with images

Unstructured data are a promising new source of information that insuran...
research
05/30/2019

Unsupervised Classification of Street Architectures Based on InfoGAN

Street architectures play an essential role in city image and streetscap...
research
11/01/2021

Hex2vec – Context-Aware Embedding H3 Hexagons with OpenStreetMap Tags

Representation learning of spatial and geographic data is a rapidly deve...

Please sign up or login with your details

Forgot password? Click here to reset