Paper Citation Record · LEDGER

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

As of 11 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2608.06934.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.06934 v1

Coverage vector

measured 39 of 39 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 4239f637-aaff-4141-bfce-ea9db7449c95 · outbound

This paper cites Validation of Walk Score for estimating neighborhood walkability: An analysis of four US metropolitan areas,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Validation of Walk Score for estimating neighborhood walkability: An analysis of four US metropolitan areas,

Reference 1

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Observation 9fd30368-7b24-4467-b887-4651b2e58bda · outbound

This paper cites Social inequalities in neighborhood visual walkability: Using street view imagery and deep learning technologies to facilitate healthy city planning,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Social inequalities in neighborhood visual walkability: Using street view imagery and deep learning technologies to facilitate healthy city planning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:14.489547Z

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Observation 0e226d1d-5c32-4f1d-89b7-abcacf5e795b · outbound

This paper cites How does pedestrian accessibility vary for different people? Development of a perceived user-specific accessibility measure for walking (PAW),.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning How does pedestrian accessibility vary for different people? Development of a perceived user-specific accessibility measure for walking (PAW),

Reference 3

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Observation dc158475-d66f-4def-9599-6a280169301d · outbound

This paper cites Understanding urban perception with visual data: A systematic review,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Understanding urban perception with visual data: A systematic review,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:14.453951Z

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Observation 9f0b25b5-1afb-4875-a28a-f9c2ad8cde93 · outbound

This paper cites Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:14.437274Z

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Observation d049e1b3-a541-4dcc-9965-1835055aa17e · outbound

This paper cites Global urban visual perception varies across demograph- ics and personalities,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Global urban visual perception varies across demograph- ics and personalities,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:14.419723Z

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Observation 70cc77ab-2069-4dad-bb75-5c455092122d · outbound

This paper cites Geographic identity and perceptions of walkable space,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Geographic identity and perceptions of walkable space,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:14.403707Z

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Observation a5409875-d07b-4a80-b29a-d2440e8d2ac4 · outbound

This paper cites Translat- ing street view imagery to correct perspectives to enhance bikeability and walkability studies,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Translat- ing street view imagery to correct perspectives to enhance bikeability and walkability studies,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:14.386783Z

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Observation 2a973ebc-38d3-45d9-b968-73c6d487489b · outbound

This paper cites Designing effective image-based surveys for urban visual perception,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Designing effective image-based surveys for urban visual perception,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:14.368206Z

Source-reported events for the cited work

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Observation 07c0d994-809c-4a88-ae65-2bd86c9440c1 · outbound

This paper cites Measuring the unmeasurable: Urban design qualities related to walkability,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Measuring the unmeasurable: Urban design qualities related to walkability,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:14.346106Z

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Observation 8ae98151-14e5-4511-9fbd-fe1105d599c9 · outbound

This paper cites Deep learning the city: Quantifying urban perception at a global scale,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Deep learning the city: Quantifying urban perception at a global scale,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:14.327643Z

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Observation befe1a94-8f52-4860-84d7-691f0777a92d · outbound

This paper cites Practical multicriteria urban bicycle routing,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Practical multicriteria urban bicycle routing,

Reference 12

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Observation 215ed903-fac5-43fe-b894-6a9d541c790f · outbound

This paper cites Response quality checks,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Response quality checks,

Reference 13

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Observation fe0f4823-8e8f-4611-afa5-89bc7074ace3 · outbound

This paper cites Towards automatic assess- ment of perceived walkability,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Towards automatic assess- ment of perceived walkability,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:14.271276Z

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Observation 4baebf89-a382-4183-bef0-21b6a1e6fa34 · outbound

This paper cites Per- sonalized image aesthetics assessment with rich attributes,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Per- sonalized image aesthetics assessment with rich attributes,

Reference 15

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Observation bfbf1bf5-bd3e-4df0-9a0e-060f3aff0f0f · outbound

This paper cites LANISTR: Multimodal Learning from Structured and Unstructured Data.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning LANISTR: Multimodal Learning from Structured and Unstructured Data

Reference 16

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Observation b2162784-83ee-46b9-9e35-13eba74892d6 · outbound

This paper cites MetaFormer baselines for vision,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning MetaFormer baselines for vision,

Reference 17

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Observation a2cd4c40-6190-468c-b004-c33252e1c784 · outbound

This paper cites Revisiting deep learning models for tabular data,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Revisiting deep learning models for tabular data,

Reference 18

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Observation 415a02f3-9f8f-447e-b234-232a77ff733a · outbound

This paper cites Rank consistent ordi- nal regression for neural networks with application to age estima- tion,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Rank consistent ordi- nal regression for neural networks with application to age estima- tion,

Reference 19

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Observation 035faf1d-f173-4ddf-8423-5c64c6e9cb8b · outbound

This paper cites LAPIS: A novel dataset for personalized image aesthetic assessment,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning LAPIS: A novel dataset for personalized image aesthetic assessment,

Reference 20

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Observation 1142fbf8-f292-41f0-b7f5-0ceebcdd1d78 · outbound

This paper cites How rating scales influence responses’ reliability, extreme points, middle point and respondent’s preferences,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning How rating scales influence responses’ reliability, extreme points, middle point and respondent’s preferences,

Reference 21

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Observation aaf5f9ee-6f5d-4078-89b9-b61da00499f7 · outbound

This paper cites Measuring streetscape perceptions from driveways and side- walks to inform pedestrian-oriented street renewal in D ¨usseldorf,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Measuring streetscape perceptions from driveways and side- walks to inform pedestrian-oriented street renewal in D ¨usseldorf,

Reference 22

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Observation 21d84596-18cd-4ae8-a670-159a501a9e44 · outbound

This paper cites Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data

Reference 23

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This paper cites A neural approach to automated essay scor- ing,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning A neural approach to automated essay scor- ing,

Reference 24

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Observation 57b06dc8-2e9b-46b0-95db-29b859de168b · outbound

This paper cites Deep learning fundus image analysis for diabetic retinopathy and macular edema grading,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Deep learning fundus image analysis for diabetic retinopathy and macular edema grading,

Reference 25

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Observation 311bd260-48df-4ba5-8955-9afb3efde3b5 · outbound

This paper cites Random forests,.

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

Reference 26

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Observation c86fe6b2-e3ab-4f0c-ac11-dd03e2d81981 · outbound

This paper cites Which cycling environment appears safer? Learning cycling safety perceptions from pairwise image comparisons,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Which cycling environment appears safer? Learning cycling safety perceptions from pairwise image comparisons,

Reference 27

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This paper cites Personalized image aesthetics,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Personalized image aesthetics,

Reference 28

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Observation 19309f3a-dcf8-4dd8-96f7-d867db615ae7 · outbound

This paper cites Street view imagery in urban analytics and GIS: A review,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Street view imagery in urban analytics and GIS: A review,

Reference 29

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This paper cites Deep residual learning for image recognition,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Deep residual learning for image recognition,

Reference 30

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Observation ec6ab4e1-18fd-42e1-8e77-31854a929374 · outbound

This paper cites Swin Transformer: Hierarchical vision transformer using shifted windows,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Swin Transformer: Hierarchical vision transformer using shifted windows,

Reference 31

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This paper cites A ConvNet for the 2020s,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning A ConvNet for the 2020s,

Reference 32

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This paper cites Deep neural networks for rank- consistent ordinal regression based on conditional probabilities,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Deep neural networks for rank- consistent ordinal regression based on conditional probabilities,

Reference 33

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This paper cites Attention is all you need,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Attention is all you need,

Reference 34

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Observation 5aa75782-b97c-481b-b8f2-b92f0c6b7969 · outbound

This paper cites Crowd- sourced NeRF: Collecting data from production vehicles for 3D street view reconstruction,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Crowd- sourced NeRF: Collecting data from production vehicles for 3D street view reconstruction,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:13.959250Z

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Observation 4d35e7ac-7677-449c-8003-48034acb9bf7 · outbound

This paper cites Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance

Reference 36

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Observation ae7c83a4-6a79-48dd-822b-ae14d0b1635f · outbound

This paper cites Adaptive personalized travel information systems: A Bayesian method to learn users’ personal preferences in multimodal transport networks,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Adaptive personalized travel information systems: A Bayesian method to learn users’ personal preferences in multimodal transport networks,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:13.943011Z

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Observation 5b5635d0-1b38-4cce-94bf-ef7525081d82 · outbound

This paper cites Deep learning for intelligent transportation systems: A survey of emerging trends,.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Deep learning for intelligent transportation systems: A survey of emerging trends,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:06:13.926639Z

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Observation c7f7e653-e7e6-4502-9396-04eb255856ea · outbound

This paper cites AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models

Reference 39

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