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‎projects/streetlens.html‎

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<title>StreetLens</title>
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#bibtex {
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<header>
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<h1>StreetLens: Enabling Human-Centered AI Agents for </br> Neighborhood Assessment from Street View Imagery</h1>
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<h1><b>StreetLens</b>: Enabling Human-Centered AI Agents for </br> Neighborhood Assessment from Street View Imagery</h1>
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<div class="profiles">
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<div class="profile">
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<img src="https://jina-kim.github.io/static/img/profile.jpg" alt="Member 1">
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<p>Jina Kim<sup>†</sup></p>
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<p><a href="https://jina-kim.github.io/" target="_blank" >Jina Kim</a><sup>†</sup></p>
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</div>
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<div class="profile">
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<img src="https://media.licdn.com/dms/image/v2/D5603AQHKh0707oxu-Q/profile-displayphoto-shrink_200_200/profile-displayphoto-shrink_200_200/0/1667706524797?e=2147483647&v=beta&t=IzVKrrM_8FL-saM_3ac1WIuQpH5PBxxL6A8_u5ekegg" alt="Member 2">
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<p>Leeje Jang<sup>†</sup></p>
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<p><a href="https://leejejang.github.io/" target="_blank" >Leeje Jang</a><sup>†</sup></p>
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</div>
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<div class="profile">
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<img src="https://knowledge-computing.github.io/images/people/yaoyichiang.jpg" alt="Member 3">
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<p>Yao-Yi Chiang<sup>†</sup></p>
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<p><a href="https://yaoyichi.github.io/" target="_blank" >Yao-Yi Chiang</a><sup>†</sup></p>
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</div>
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<div class="profile">
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<img src="https://pop.umn.edu/sites/pop.umn.edu/files/2023-06/Guanyu%20Wang.jpg" alt="Member 4">
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<p>Guanyu Wang<sup>‡</sup></p>
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<p><a href="https://pop.umn.edu/people/guanyu-wang" target="_blank" >Guanyu Wang</a><sup>‡</sup></p>
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</div>
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<div class="profile">
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<img src="https://fsos.umn.edu/sites/fsos/files/styles/x_small_portrait/public/2023-07/Michelle%20Pasco.jpg?h=be0248d6&itok=DbpDr1CV" alt="Member 5">
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<p>Michelle Pasco<sup>‡</sup></p>
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<p><a href="https://fsos.umn.edu/people/faculty/michelle-pasco" target="_blank" >Michelle Pasco</a><sup>‡</sup></p>
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</div>
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<div class="affiliations" style="margin-top:0px; text-align:center; font-size:0.9em;">
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</header>
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<section>
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<h2>Colab Notebooks</h2>
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<h2>Introduction</h2>
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<div style="margin: 50px;">
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<p>
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StreetLens is a human-centered, researcher-configurable workflow, that embeds relevant social science expertise in a vision language model (VLM) for scalable neighborhood environmental assessments.
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StreetLens mimics the process of trained human coders by grounding the analysis in questions derived from established interview protocols, retrieving relevant street view imagery (SVI), and generating a wide spectrum of semantic annotations from objective features (e.g., the number of cars) to subjective perceptions (e.g., the sense of disorder in an image).
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</p>
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</section>
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<hr>
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<h2>Jupyter Notebooks</h2>
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<div style="text-align:center; margin-bottom: 30px;">
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<a href="https://colab.research.google.com/drive/NOTEBOOK_ID_1" target="_blank" style="margin: 0 15px; padding: 10px 20px; background:#3498db; color:white; text-decoration:none; border-radius:8px;">Notebook 1</a>
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<a href="https://colab.research.google.com/drive/NOTEBOOK_ID_2" target="_blank" style="margin: 0 15px; padding: 10px 20px; background:#2ecc71; color:white; text-decoration:none; border-radius:8px;">Notebook 2</a>
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</div>
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</section>
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<hr>
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<h2>Try Out StreetLens</h2>
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<div class="buttons">
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}
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</script>
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<hr>
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<h2>Paper BibTeX</h2>
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<h2>BibTeX</h2>
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<div id="bibtex">
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@misc{kim2025streetlensenablinghumancenteredai,
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title={StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery},

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