Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T16:43:52.244483Z
Paper Citation Record · LEDGER
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images
As of 22 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2508.21565.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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Source: cited_works
Reference resolution
52 of 52 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation fa73e3e2-8e30-475d-92a4-f9dd7715655b · outbound
This paper cites Google street view: Capturing the world at street level.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Google street view: Capturing the world at street level
Reference 1
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Observation 3aab4094-9239-493d-864c-2edac5b47858 · outbound
This paper cites Evaluation methods for landscapes with greenery.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Evaluation methods for landscapes with greenery
Reference 2
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Observation a1fb3897-2151-4dca-947c-80252575d21d · outbound
This paper cites Browning, Jiaying Dong, Kuiran Zhang, Shuai Yuan, H¨useyin Ertan ˙Inan, Olivia McAnirlin, Dani T.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Browning, Jiaying Dong, Kuiran Zhang, Shuai Yuan, H¨useyin Ertan ˙Inan, Olivia McAnirlin, Dani T
Reference 3
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Observation e8d66e39-f41c-437c-a787-4cba57e8efd7 · outbound
This paper cites The green window view index: automated multi-source visi- bility analysis for a multi-scale assessment of green window views.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images The green window view index: automated multi-source visi- bility analysis for a multi-scale assessment of green window views
Reference 4
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Observation 74d718cc-de8b-481c-9e96-cbea460508ac · outbound
This paper cites Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom
Reference 5
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Observation afb504ce-f976-4375-bcc5-24f1e60f4a60 · outbound
This paper cites End-to- end object detection with transformers.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images End-to- end object detection with transformers
Reference 6
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Observation c147cb24-a1c3-4ea8-bd4f-47b197d61d61 · outbound
This paper cites Spatialvlm: Endowing vision-language models with spatial reasoning capabilities.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Spatialvlm: Endowing vision-language models with spatial reasoning capabilities
Reference 7
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Observation a1ea861b-0d50-4ccb-a644-ed1ec0c13e3a · outbound
This paper cites Evaluating implied urban nature vital- ity in san francisco: An interdisciplinary approach combining census data, street view images, and social media analysis.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Evaluating implied urban nature vital- ity in san francisco: An interdisciplinary approach combining census data, street view images, and social media analysis
Reference 8
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Observation 9251068b-ef49-40f2-95f1-c7f4d905fe27 · outbound
This paper cites Visual chain- of-thought prompting for knowledge-based visual reasoning.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Visual chain- of-thought prompting for knowledge-based visual reasoning
Reference 9
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Source-reported events for the cited work
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Observation 034e278e-a75c-4733-bb7b-0bc140cb65fd · outbound
This paper cites The cityscapes dataset for semantic urban scene understanding.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images The cityscapes dataset for semantic urban scene understanding
Reference 10
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Source-reported events for the cited work
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Observation 8ac9be47-7346-48d5-963b-985d87dbaeb4 · outbound
This paper cites Instructblip: Towards general-purpose vision-language models with instruction tuning.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Instructblip: Towards general-purpose vision-language models with instruction tuning
Reference 11
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This paper cites Feeling Nature: Measuring perceptions of biophilia across global biomes using visual AI,.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Feeling Nature: Measuring perceptions of biophilia across global biomes using visual AI,
Reference 12
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How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Unresolved cited work
Reference 13
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Observation 896a4ee2-26e0-48dc-a808-025e6a3038c5 · outbound
This paper cites The processing of negation and polarity: An overview.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images The processing of negation and polarity: An overview
Reference 14
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Observation 1e2386f4-122c-4e00-96f1-806d80b07ccd · outbound
This paper cites Vqa-lol: Visual question answering under the lens of logic.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Vqa-lol: Visual question answering under the lens of logic
Reference 15
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How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Unresolved cited work
Reference 16
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How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Unresolved cited work
Reference 17
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This paper cites Natural Adversarial Examples.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Natural Adversarial Examples
Reference 18
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This paper cites Which street is hotter? street morphol- ogy may hold clues -thermal environment mapping based on street view imagery.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Which street is hotter? street morphol- ogy may hold clues -thermal environment mapping based on street view imagery
Reference 19
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Observation c20955f7-af37-40ca-b428-c52899b1d746 · outbound
This paper cites Hudson and Christopher D.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Hudson and Christopher D
Reference 20
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Source-reported events for the cited work
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Observation ef39f770-dd50-4905-b031-0f75a8d8f645 · outbound
This paper cites Lawrence Zitnick, and Ross Girshick.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Lawrence Zitnick, and Ross Girshick
Reference 21
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Observation a516bb51-ec3e-4461-9433-bf4296dd86a9 · outbound
This paper cites Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
Reference 22
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Observation f1b6b3ae-0fe0-4dbc-a7a4-73c76d0983dc · outbound
This paper cites Large language models are zero-shot reasoners.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Large language models are zero-shot reasoners
Reference 23
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Observation 321bfdfc-88b5-43fd-9f77-bad28e8087ba · outbound
This paper cites Understanding counterfactuality: A review of experimental evidence for the dual meaning of counterfactuals.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Understanding counterfactuality: A review of experimental evidence for the dual meaning of counterfactuals
Reference 24
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This paper cites Blip- 2: Bootstrapping language-image pre-training with frozen im- age encoders and large language models.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Blip- 2: Bootstrapping language-image pre-training with frozen im- age encoders and large language models
Reference 25
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Observation 8a271aaf-a8e8-4376-8eda-b2f5c4d56c29 · outbound
This paper cites Assessing street-level urban greenery using google street view and a modified green view index.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Assessing street-level urban greenery using google street view and a modified green view index
Reference 26
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Observation 73a49552-63b5-4c00-9117-49fdd9df3423 · outbound
This paper cites Generalizing vision-language models to novel domains: A comprehensive survey.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Generalizing vision-language models to novel domains: A comprehensive survey
Reference 27
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This paper cites Eyes can deceive: Benchmarking counterfactual rea- soning abilities of multi-modal large language models.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Eyes can deceive: Benchmarking counterfactual rea- soning abilities of multi-modal large language models
Reference 28
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Observation 3ce008d1-5d7e-4e8a-8b80-0ffb16569066 · outbound
This paper cites Evaluating human perception of building exteriors using street view imagery.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Evaluating human perception of building exteriors using street view imagery
Reference 29
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Observation 54854b56-3587-41bf-9ac8-c81ee1c67148 · outbound
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How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Improved baselines with visual instruction tuning
Reference 30
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This paper cites Efficacy of Synthetic Data as a Benchmark.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Efficacy of Synthetic Data as a Benchmark
Reference 31
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This paper cites Review of methods used to es- timate the sky view factor in urban street canyons.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Review of methods used to es- timate the sky view factor in urban street canyons
Reference 32
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This paper cites Objective scoring of streetscape walkability related to leisure walking: Statistical modeling approach with semantic segmentation of google street view images.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Objective scoring of streetscape walkability related to leisure walking: Statistical modeling approach with semantic segmentation of google street view images
Reference 33
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This paper cites Streetscore - predicting the perceived safety of one mil- lion streetscapes.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Streetscore - predicting the perceived safety of one mil- lion streetscapes
Reference 34
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This paper cites The mapillary vistas dataset for semantic understanding of street scenes.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images The mapillary vistas dataset for semantic understanding of street scenes
Reference 35
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Observation 714a7014-80c6-4efc-8125-cbfcf2e43b1b · outbound
This paper cites Counterfactual vqa: A cause- effect look at language bias.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Counterfactual vqa: A cause- effect look at language bias
Reference 36
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This paper cites Evaluating the subjective percep- tions of streetscapes using street-view images.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Evaluating the subjective percep- tions of streetscapes using street-view images
Reference 37
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Source-reported events for the cited work
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This paper cites Learning transferable visual models from natural language supervision.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Learning transferable visual models from natural language supervision
Reference 38
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This paper cites Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
Reference 39
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Source-reported events for the cited work
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Observation c7917aaf-da19-4f57-91ae-3ff0fdab65e3 · outbound
This paper cites Visual cot: Advancing multi-modal language models with a compre- hensive dataset and benchmark for chain-of-thought reason- ing.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Visual cot: Advancing multi-modal language models with a compre- hensive dataset and benchmark for chain-of-thought reason- ing
Reference 40
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This paper cites Is synthetic data all we need? benchmarking the robustness of models trained with synthetic images.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Is synthetic data all we need? benchmarking the robustness of models trained with synthetic images
Reference 41
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Source-reported events for the cited work
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This paper cites Svensson.
Reference 42
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Source-reported events for the cited work
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Observation c045f472-8ee5-47ff-8d08-9841f4de7986 · outbound
This paper cites A new benchmark: On the util- ity of synthetic data with blender for bare supervised learn- ing and downstream domain adaptation.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images A new benchmark: On the util- ity of synthetic data with blender for bare supervised learn- ing and downstream domain adaptation
Reference 43
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Source-reported events for the cited work
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Observation 5ed35ed8-5924-49c6-82f0-ecdeb80ea2b2 · outbound
This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reference 44
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This paper cites Negation: A Pink Elephant in the Large Language Models' Room?.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Negation: A Pink Elephant in the Large Language Models' Room?
Reference 45
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This paper cites Al- varez.
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:43:52.630835Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a065ad02-441c-4927-83fa-cdfbcdeeb656 · outbound
This paper cites Self-consistency improves chain of thought reasoning in lan- guage models.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Self-consistency improves chain of thought reasoning in lan- guage models
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:43:52.598326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ff0f101b-8ba0-49a6-833d-513bb6a8efaa · outbound
This paper cites Chi, Quoc V.
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:43:52.572855Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0802696a-1af0-4e2f-8a0a-665d74fa90cc · outbound
This paper cites Alvarez, and Ping Luo.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Alvarez, and Ping Luo
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:43:52.536709Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 20365222-5643-4cc0-89c8-6a25d8ace7c8 · outbound
This paper cites Improve vision language model chain-of-thought reasoning, 2024.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images Improve vision language model chain-of-thought reasoning, 2024
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:43:52.514988Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation df454ef8-5092-4cbf-81b2-d66f19ccb8fa · outbound
This paper cites NegVQA: Can Vision Language Models Understand Negation?.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images NegVQA: Can Vision Language Models Understand Negation?
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:43:52.235796Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f516c9c9-7109-428c-9898-a64ab16fc56e · outbound
This paper cites A study on the impact of visible green index and vegetation structures on brain wave change in residential landscape.Urban Forestry & Urban Greening, 64:127299, 2021.
How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images A study on the impact of visible green index and vegetation structures on brain wave change in residential landscape.Urban Forestry & Urban Greening, 64:127299, 2021
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:43:52.491168Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Pith citing papers
No inbound Pith citation observations are available.