It is not always greener on the other side: Greenery perception across demographics and personalities in multiple cities

• URL: http://arxiv.org/abs/2512.17186v1
• Date: Fri, 19 Dec 2025 03:01:40 GMT
• Title: It is not always greener on the other side: Greenery perception across demographics and personalities in multiple cities
• Authors: Matias Quintana, Fangqi Liu, Jussi Torkko, Youlong Gu, Xiucheng Liang, Yujun Hou, Koichi Ito, Yihan Zhu, Mahmoud Abdelrahman, Tuuli Toivonen, Yi Lu, Filip Biljecki,
• Abstract summary: Quantifying and assessing urban greenery is consequential for planning and development.<n> Evaluation can be broadly grouped into objective (e.g., measuring the amount of greenery) and subjective (e.g., polling the perception of people) approaches.<n>In this work, we advance the state of the art by measuring differences and explaining them through human, geographic, and spatial dimensions.
• Score: 2.572192601842781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantifying and assessing urban greenery is consequential for planning and development, reflecting the everlasting importance of green spaces for multiple climate and well-being dimensions of cities. Evaluation can be broadly grouped into objective (e.g., measuring the amount of greenery) and subjective (e.g., polling the perception of people) approaches, which may differ -- what people see and feel about how green a place is might not match the measurements of the actual amount of vegetation. In this work, we advance the state of the art by measuring such differences and explaining them through human, geographic, and spatial dimensions. The experiments rely on contextual information extracted from street view imagery and a comprehensive urban visual perception survey collected from 1,000 people across five countries with their extensive demographic and personality information. We analyze the discrepancies between objective measures (e.g., Green View Index (GVI)) and subjective scores (e.g., pairwise ratings), examining whether they can be explained by a variety of human and visual factors such as age group and spatial variation of greenery in the scene. The findings reveal that such discrepancies are comparable around the world and that demographics and personality do not play a significant role in perception. Further, while perceived and measured greenery correlate consistently across geographies (both where people and where imagery are from), where people live plays a significant role in explaining perceptual differences, with these two, as the top among seven, features that influences perceived greenery the most. This location influence suggests that cultural, environmental, and experiential factors substantially shape how individuals observe greenery in cities.

Related papers

• Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos [79.03150233804458]
  Real-world images entangle race and gender with correlated factors such as background and clothing, obscuring attribution.<n>We propose a textbfface-only counterfactual evaluation paradigm<n>We generate counterfactual variants by editing only facial attributes related to race and gender, keeping all other visual factors fixed.
  arXiv  Detail & Related papers  (2026-01-11T14:35:06Z)
• Inconsistent Affective Reaction: Sentiment of Perception and Opinion in Urban Environments [1.4395184780210915]
  This study presents novel methodologies for identifying and elucidating sentiment inconsistency.<n>A dataset encompassing 140,750 Baidu and Tencent Street view images to measure perceptions, and 984,024 Weibo social media text posts to measure opinions.
  arXiv  Detail & Related papers  (2025-10-08T16:07:23Z)
• Global urban visual perception varies across demographics and personalities [1.3651611781068242]
  We conducted a largescale urban visual perception survey of streetscapes worldwide using street view imagery.<n>We examined how demographics -- including gender, age, income, education, race and ethnicity, and personality traits -- shape perceptions.<n>This dataset reveals demographic- and personality-based differences across six traditional indicators.
  arXiv  Detail & Related papers  (2025-05-19T06:35:11Z)
• When Does Perceptual Alignment Benefit Vision Representations? [76.32336818860965]
  We investigate how aligning vision model representations to human perceptual judgments impacts their usability. We find that aligning models to perceptual judgments yields representations that improve upon the original backbones across many downstream tasks. Our results suggest that injecting an inductive bias about human perceptual knowledge into vision models can contribute to better representations.
  arXiv  Detail & Related papers  (2024-10-14T17:59:58Z)
• Towards Geographic Inclusion in the Evaluation of Text-to-Image Models [25.780536950323683]
  We study how much annotators in Africa, Europe, and Southeast Asia vary in their perception of geographic representation, visual appeal, and consistency in real and generated images. For example, annotators in different locations often disagree on whether exaggerated, stereotypical depictions of a region are considered geographically representative. We recommend steps for improved automatic and human evaluations.
  arXiv  Detail & Related papers  (2024-05-07T16:23:06Z)
• On the need to move from a single indicator to a multi-dimensional framework to measure accessibility to urban green [0.0]
  We investigate the extent to which the use of a single metric provides a reliable assessment of green accessibility in a city. The results suggest that, due to the complex interaction between the spatial distribution of greenspaces in an urban center and its population distribution, the use of a single indicator might lead to insufficient discrimination.
  arXiv  Detail & Related papers  (2023-08-10T12:37:13Z)
• ColorSense: A Study on Color Vision in Machine Visual Recognition [57.916512479603064]
  We collect 110,000 non-trivial human annotations of foreground and background color labels from visual recognition benchmarks.<n>We validate the use of our datasets by demonstrating that the level of color discrimination has a dominating effect on the performance of machine perception models.<n>Our findings suggest that object recognition tasks such as classification and localization are susceptible to color vision bias.
  arXiv  Detail & Related papers  (2022-12-16T18:51:41Z)
• Discovering Underground Maps from Fashion [80.02941583103612]
  We propose a method to automatically create underground neighborhood maps of cities by analyzing how people dress. Using publicly available images from across a city, our method finds neighborhoods with a similar fashion sense and segments the map without supervision.
  arXiv  Detail & Related papers  (2020-12-04T23:40:59Z)
• Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetation [0.0]
  This study proposes an improved indicator of greenery visibility for analytical use (standardized GVI; sGVI) It is shown that the sGVI, a weighted form of GVI aggregated to an area, mitigates the bias of densely located measurement sites. Also, by comparing sGVI and NDVI at city block level, we found that sGVI captures the presence of vegetation better in the city center, whereas NDVI is better in capturing vegetation in parks and forests.
  arXiv  Detail & Related papers  (2020-08-01T09:58:22Z)
• Placepedia: Comprehensive Place Understanding with Multi-Faceted Annotations [79.80036503792985]
  We contribute Placepedia, a large-scale place dataset with more than 35M photos from 240K unique places. Besides the photos, each place also comes with massive multi-faceted information, e.g. GDP, population, etc. This dataset, with its large amount of data and rich annotations, allows various studies to be conducted.
  arXiv  Detail & Related papers  (2020-07-07T20:17:01Z)
• Survey on Visual Sentiment Analysis [87.20223213370004]
  This paper reviews pertinent publications and tries to present an exhaustive overview of the field of Visual Sentiment Analysis. The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view. A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways.
  arXiv  Detail & Related papers  (2020-04-24T10:15:22Z)
• Measuring Spatial Subdivisions in Urban Mobility with Mobile Phone Data [58.720142291102135]
  By 2050 two thirds of the world population will reside in urban areas. This growth is faster and more complex than the ability of cities to measure and plan for their sustainability. To understand what makes a city inclusive for all, we define a methodology to identify and characterize spatial subdivisions.
  arXiv  Detail & Related papers  (2020-02-20T14:37:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.