Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning

• URL: http://arxiv.org/abs/2608.06934v1
• Date: Fri, 07 Aug 2026 08:07:06 GMT
• Title: Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning
• Abstract summary: This paper introduces a dataset of 29,870 walkability ratings from 1,196 respondents in Australia.<n>It proposes the first user-conditioned multimodal deep learning framework for walkability perception.
• Score: 0.12744523252873352
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Visual perception of walkability varies substantially across individuals, reflecting differences in personal characteristics, experiences, and preferences. Existing studies, however, often reduce these diverse judgements to aggregated scores, implicitly assuming uniform perception, and commonly rely on vehicle-mounted street-view imagery that does not reflect the pedestrian's visual experience. This paper introduces a dataset of 29,870 walkability ratings from 1,196 respondents, linking sidewalk-view imagery across urban, suburban, and regional Australian environments with individual rater attributes, and proposes the first user-conditioned multimodal deep learning framework for walkability perception, fusing visual features with respondent-level representations. A viewpoint-comparison study shows that sidewalk-view images receive significantly higher walkability ratings than matched street-view images, indicating that imagery source is a substantive design decision in perception surveys. The user-conditioned model improves rank agreement with observed ratings by 65% over an image-only baseline (quadratic weighted kappa 0.47 vs. 0.29), demonstrating that who is evaluating an environment carries predictive indication beyond image content alone. These findings support moving from aggregated, observer-independent walkability scores toward models that represent diverse users, enabling more inclusive assessment of pedestrian environments.

Related papers

• Organization of Valence and Arousal in Vision-Language Representations of Built Environments: Insights from the EMOIS Dataset [0.0]
  We introduce the Emotional Impression of Spaces dataset, comprising 1,544 real-world built-environment images.<n>Using Contrastive Language--Image Pre-training (CLIP) representations, we perform predictive and geometric analyses.<n>We show how learned representations can support qualitative interpretation of predicted affective values.
  arXiv  Detail & Related papers  (2026-09-06T23:12:30Z)
• Beyond Semantics: Modeling Factual and Affective Perceptual Experiences from Vision-Language Data [37.91467682358732]
  We introduce **PercepT** (**Percep**tion topic **T**ransformer), a two-stage architecture that tackles P-Topics modeling.<n>PercepT captures semantically meaningful perception experiences and significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2026-06-02T08:54:59Z)
• Modeling Subjective Urban Perception with Human Gaze [60.73742038237224]
  We introduce Place Pulse-Gaze, an urban perception dataset that augments street view images with synchronized eye-tracking recordings and individual perception labels.<n>We propose a Gaze-Guided Urban Perception Framework to study how gaze behavior contributes to the modeling of subjective urban perception.
  arXiv  Detail & Related papers  (2026-05-01T16:25:13Z)
• WalkCLIP: Multimodal Learning for Urban Walkability Prediction [4.403122905236942]
  Urban walkability is a cornerstone of public health, sustainability, and quality of life.<n>Recent studies have used satellite imagery, street view imagery, or population indicators to estimate walkability.<n>We introduce WalkCLIP, a framework that integrates these complementary viewpoints to predict urban walkability.
  arXiv  Detail & Related papers  (2025-11-26T22:15:33Z)
• RoboView-Bias: Benchmarking Visual Bias in Embodied Agents for Robotic Manipulation [67.38036090822982]
  We propose RoboView-Bias, the first benchmark specifically designed to quantify visual bias in robotic manipulation.<n>We create 2,127 task instances that enable robust measurement of biases induced by individual visual factors and their interactions.<n>Our results highlight that systematic analysis of visual bias is a prerequisite for developing safe and reliable general-purpose embodied agents.
  arXiv  Detail & Related papers  (2025-09-26T13:53:25Z)
• 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)
• Evaluating Multiview Object Consistency in Humans and Image Models [68.36073530804296]
  We leverage an experimental design from the cognitive sciences which requires zero-shot visual inferences about object shape. We collect 35K trials of behavioral data from over 500 participants. We then evaluate the performance of common vision models.
  arXiv  Detail & Related papers  (2024-09-09T17:59:13Z)
• Vision-Language Models under Cultural and Inclusive Considerations [53.614528867159706]
  Large vision-language models (VLMs) can assist visually impaired people by describing images from their daily lives. Current evaluation datasets may not reflect diverse cultural user backgrounds or the situational context of this use case. We create a survey to determine caption preferences and propose a culture-centric evaluation benchmark by filtering VizWiz, an existing dataset with images taken by people who are blind. We then evaluate several VLMs, investigating their reliability as visual assistants in a culturally diverse setting.
  arXiv  Detail & Related papers  (2024-07-08T17:50:00Z)
• 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)
• Exploring CLIP for Assessing the Look and Feel of Images [87.97623543523858]
  We introduce Contrastive Language-Image Pre-training (CLIP) models for assessing both the quality perception (look) and abstract perception (feel) of images in a zero-shot manner. Our results show that CLIP captures meaningful priors that generalize well to different perceptual assessments.
  arXiv  Detail & Related papers  (2022-07-25T17:58:16Z)
• Considering user agreement in learning to predict the aesthetic quality [35.255447771350404]
  We propose a re-adapted multi-task attention network to predict both the mean opinion score and the standard deviation in an end-to-end manner. With such loss, the model is encouraged to learn the uncertainty of the content that is relevant to the diversity of observers' opinions. Experiments have demonstrated that the proposed multi-task aesthetic model achieves state-of-the-art performance on two different types of aesthetic datasets.
  arXiv  Detail & Related papers  (2021-10-13T18:00:36Z)

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.