Modeling Subjective Urban Perception with Human Gaze
- URL: http://arxiv.org/abs/2605.00764v1
- Date: Fri, 01 May 2026 16:25:13 GMT
- Title: Modeling Subjective Urban Perception with Human Gaze
- Abstract summary: 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.
- Score: 60.73742038237224
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Urban perception describes how people subjectively evaluate urban environments, shaping how cities are experienced and understood. Existing computational approaches primarily model urban perception directly from street view images, but largely ignore the human perceptual process through which such judgments are formed. In this paper, we introduce Place Pulse-Gaze, an urban perception dataset that augments street view images with synchronized eye-tracking recordings and individual perception labels. Based on this dataset, we propose a Gaze-Guided Urban Perception Framework to study how gaze behavior contributes to the modeling of subjective urban perception. The framework systematically investigates three complementary settings: gaze-only modeling, gaze fusion with explicit semantic scene representations, and gaze fusion with implicit richer visual representations. Experiments show that gaze alone already carries useful predictive signals for subjective urban perception, and that integrating gaze with scene representations further improves prediction under both semantic and richer visual representations. Overall, our findings highlight the importance of incorporating human perceptual processes into urban scene understanding and open a direction for gaze-guided multimodal urban computing.
Related papers
- Life Style Levels: Neighborhood Delineation using Geospatial Data [0.0]
This study pro-poses a scalable, grid-based urban delineation framework using building morphology derived from open-source satellite imagery.<n>The proposed framework provides a scalable, interpretable, and cost-effective approach for granular urban affluence mapping across In-dian cities.
arXiv Detail & Related papers (2026-07-07T17:37:03Z) - City landscape in sight: A crowdsourced framework for unlocking urban-scale window view perceptions from real estate imagery [9.383884267784843]
City landscapes viewed through home windows influence quality of life, yet perceptions of actual window views at the urban scale remain understudied.<n>This study presents an approach for large-scale mapping of perceptions using 12,334 window view images (WVIs) collected from actual residential properties listed on real estate platforms in Wuhan, China.<n>A hybrid neural network model was trained to predict human perceptions of all crowdsourced WVIs and map their spatial distribution.
arXiv Detail & Related papers (2026-06-13T08:46:16Z) - Designing streetscapes from street-view imagery using diffusion models [18.860196708556995]
Street-view imagery (SVI) is widely used to quantify key indicators of urban environment, such as green- ery, sky, or road view indices.<n>Existing studies largely focus on measuring current streetscapes and rarely support the generation of alternative and non-existing urban scenarios.<n>We propose a gener-ative multimodal AI framework that synthesizes alternative streetscapes conditioned on targeted visual metrics.
arXiv Detail & Related papers (2026-05-17T16:20:30Z) - From Pixels to Predicates Structuring urban perception with scene graphs [4.7912190748710515]
This study proposes a three stage pipeline that transforms street view imagery (SVI) into structured representations for predicting six perceptual indicators.<n>In the first stage, each image is parsed using an open-set Panoptic Scene Graph model (OpenPSG) to extract object predicate triplets object.<n>In the second stage, compact scene-level embeddings are learned through a heterogeneous graph autoencoder (GraphMAE)<n>In the third stage, a neural network predicts perception scores from these embeddings.
arXiv Detail & Related papers (2025-12-22T10:02:53Z) - Do Street View Imagery and Public Participation GIS align: Comparative Analysis of Urban Attractiveness [0.0]
Street View Imagery (SVI) and Public Participation GIS (PPGIS) represent two prominent approaches for capturing place-based perceptions.<n>This study investigates the alignment between SVI-based perceived attractiveness and residents' reported experiences gathered via a city-wide PPGIS survey in Helsinki, Finland.
arXiv Detail & Related papers (2025-11-04T12:40:12Z) - AerialGo: Walking-through City View Generation from Aerial Perspectives [48.53976414257845]
AerialGo is a framework that generates realistic walking-through city views from aerial images.<n>By conditioning ground-view synthesis on accessible aerial data, AerialGo bypasses the privacy risks inherent in ground-level imagery.<n>Experiments show that AerialGo significantly enhances ground-level realism and structural coherence.
arXiv Detail & Related papers (2024-11-29T08:14:07Z) - 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) - Urban Scene Diffusion through Semantic Occupancy Map [49.20779809250597]
UrbanDiffusion is a 3D diffusion model conditioned on a Bird's-Eye View (BEV) map.
Our model learns the data distribution of scene-level structures within a latent space.
After training on real-world driving datasets, our model can generate a wide range of diverse urban scenes.
arXiv Detail & Related papers (2024-03-18T11:54:35Z) - City-Wide Perceptions of Neighbourhood Quality using Street View Images [5.340189314359048]
This paper describes our methodology, based in London, including collection of images and ratings, web development, model training and mapping.
Perceived neighbourhood quality is a core component of urban vitality, influencing social cohesion, sense of community, safety, activity and mental health of residents.
arXiv Detail & Related papers (2022-11-22T10:16:35Z) - GIMO: Gaze-Informed Human Motion Prediction in Context [75.52839760700833]
We propose a large-scale human motion dataset that delivers high-quality body pose sequences, scene scans, and ego-centric views with eye gaze.
Our data collection is not tied to specific scenes, which further boosts the motion dynamics observed from our subjects.
To realize the full potential of gaze, we propose a novel network architecture that enables bidirectional communication between the gaze and motion branches.
arXiv Detail & Related papers (2022-04-20T13:17:39Z) - From Paris to Berlin: Discovering Fashion Style Influences Around the
World [108.58097776743331]
We propose to quantify fashion influences from everyday images of people wearing clothes.
We introduce an approach that detects which cities influence which other cities in terms of propagating their styles.
We then leverage the discovered influence patterns to inform a forecasting model that predicts the popularity of any given style at any given city into the future.
arXiv Detail & Related papers (2020-04-03T00:54:23Z) - Indexical Cities: Articulating Personal Models of Urban Preference with
Geotagged Data [0.0]
This research characterizes personal preference in urban spaces and predicts a spectrum of unknown likeable places for a specific observer.
Unlike most urban perception studies, our intention is not by any means to provide an objective measure of urban quality, but rather to portray personal views of the city or Cities of Cities.
arXiv Detail & Related papers (2020-01-23T11:00:19Z)
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.