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<!DOCTYPE html>
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<head>
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</head>
<body>
<div class="container">
<div style="text-align: center; margin-top: 3rem; display: flex; align-items: center; justify-content: center;">
<img src="images/logo.png" alt="Overview" style="width: 4rem; margin-bottom: 0.8rem; margin-right: 1rem;">
<h1>MapEval</h1>
</div>
<h2 style="text-align: center; margin-top: -1.5rem; font-weight: 500; font-size:x-large;">A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models</h2>
<h2 style="text-align: center; margin-top: -.5rem; font-weight:900; font-size:x-large; color: orange; background-color: black;">
🏆 ICML'25 Spotlight Paper 🏆
</h2>
<h4 style="text-align: center; margin-top: -0.5rem; font-weight: 500">
<a href="https://mahirlabibdihan.github.io">Mahir Labib Dihan</a>
<sup style="color: blue">1</sup>,
<a href="https://github.com/tanvirsaad">Md Tanvir Hassan</a>
<sup style="color: blue">1</sup> <sup>*</sup>,
<a href="https://openreview.net/profile?id=~MD_TANVIR_PARVEZ1">Md Tanvir Parvez</a>
<sup style="color: green">2</sup> <sup>*</sup>,
<a href="https://hasebul.github.io/shawon_portfolio/">Md Hasebul Hasan</a>
<sup style="color: blue">1</sup>,
<a href="https://openreview.net/profile?id=~Md_Almash_Alam1">Md Almash Alam</a>
<sup style="color: orange">3</sup>,
<a href="https://www.aamircheema.com/">Muhammad Aamir Cheema</a>
<sup style="color: purple">4</sup>,
<a href="https://sites.google.com/site/mohammedeunusali/">Mohammed Eunus Ali</a>
<sup style="color: blue">1</sup> <sup style="color: purple">4</sup> <sup style="color: black; font-family: 'Roboto', sans-serif; font-weight: 120; font-size: 1rem; margin-left: 2px;">†</sup>,
<a href="https://rizwan09.github.io/">Md Rizwan Parvez</a>
<sup style="color: orangered">5</sup>
</h4>
<p style="text-align: center; margin-top: -1rem; font-weight: 500; font-size: medium;">
<sup style="color: blue">1</sup>
Department of Computer Science and Engineering<br>
Bangladesh University of Engineering and Technology (BUET)
</p>
<p style="text-align: center; margin-top: -1.2rem; font-weight: 500; font-size: medium;">
<sup style="color: green">2</sup>
Statistics, Islamic University Bangladesh
</p>
<p style="text-align: center; margin-top: -1.2rem; font-weight: 500; font-size: medium;">
<sup style="color: orange">3</sup>
Bangladesh Computer Council (BCC)
</p>
<p style="text-align: center; margin-top: -1.2rem; font-weight: 500; font-size: medium;">
<sup style="color: purple">4</sup>
Monash University
</p>
<p style="text-align: center; margin-top: -1.2rem; font-weight: 500; font-size: medium;">
<sup style="color: orangered">5</sup>
Qatar Computing Research Institute (QCRI)
</p>
<span
style="
font-weight: 500;
display: flex;
align-items: center;
text-align: center;
justify-content: center;
font-size: medium;
margin-top: -1.2rem;
"
>
<h1
style="
font-family: 'Roboto', sans-serif;
font-weight: 150;
font-size: 1.2rem;
margin-right: 4px;
"
>
†
</h1>
Corresponding to <a href="mailto:mohammed.eunus.ali@gmail.com"
>mohammed.eunus.ali@gmail.com</a
>
</span>
<span
style="
font-weight: 500;
display: flex;
align-items: center;
text-align: center;
justify-content: center;
font-size: medium;
margin-top: -1.2rem;
"
>
<h1
style="
font-family: 'Roboto', sans-serif;
font-weight: 150;
font-size: 1.2rem;
margin-right: 4px;
"
>
*
</h1>
Equal Contribution
</span>
<div style="margin-top: 1rem"></div>
<span class="link-block" style="margin-top: -0.5rem">
<!-- <a href="https://arxiv.org/abs/2501.00316" class="external-link" style="background-color:#363636; display: flex; align-items: center; padding-top: 8px; padding-bottom: 8px;">
<i class="ai ai-arxiv"></i>
<span>arXiv</span>
</a> -->
<a
href="https://www.researchgate.net/publication/387670063_MapEval_A_Map-Based_Evaluation_of_Geo-Spatial_Reasoning_in_Foundation_Models"
class="external-link"
style="
background-color: #363636;
display: flex;
align-items: center;
padding-top: 8px;
padding-bottom: 8px;
"
>
<i class="fas fa-file-alt" style="margin-right: 4px"></i>
<span>Paper</span>
</a>
<!-- <a href="https://huggingface.co/papers/2501.00316" class="external-link"
style="background-color:#363636; display: flex; align-items: center; padding-top: 8px; padding-bottom: 8px;">
<i class="fas fa-file-alt"></i>
<span>Paper</span>
</a> -->
<a
href="https://github.com/MapEval"
class="external-link"
style="
background-color: #363636;
display: flex;
align-items: center;
padding-top: 8px;
padding-bottom: 8px;
"
>
<i class="fab fa-github" style="margin-right: 4px"></i>
<span>Code</span>
</a>
<a
href="https://huggingface.co/MapEval"
class="external-link"
style="
background-color: #363636;
display: flex;
align-items: center;
padding-top: 8px;
padding-bottom: 8px;
"
>
<!-- <span class="material-symbols-outlined icon">folder</span> -->
<!-- <p style="font-size:18px; margin: 0; margin-right: 4px;">🤗</p> -->
<i class="fa-solid fa-images" style="margin-right: 4px"></i>
<span>Dataset</span>
</a>
<a
href="#results"
class="external-link"
style="
background-color: #363636;
display: flex;
align-items: center;
padding-top: 8px;
padding-bottom: 8px;
"
>
<!-- <p style="font-size:18px; margin: 0; margin-right: 4px;">🏆</p> -->
<i class="fa-solid fa-trophy" style="margin-right: 4px"></i>
<span>Leaderboard</span>
</a>
<a
href="https://mapqator.github.io/project"
class="external-link"
style="
background-color: #363636;
display: flex;
align-items: center;
padding-top: 8px;
padding-bottom: 8px;
"
>
<i class="fa-solid fa-location-dot" style="margin-right: 4px"></i>
<span>MapQaTor</span>
</a>
</span>
<div
style="
margin-top: 2rem;
padding: 1.25rem;
border-radius: 0px;
background: #1f1f1f;
border: 1px solid #3a3a3a;
color: #e8e8e8;
"
>
<h2 style="margin: 0 0 0.75rem 0; text-align: center">
MapQA Ecosystem
</h2>
<p style="text-align: center; margin-top: 0; margin-bottom: 1.25rem;">
A unified ecosystem for map-based question answering and geo-spatial reasoning.
</p>
<div
style="
display: flex;
flex-wrap: wrap;
gap: 0.75rem;
justify-content: center;
"
>
<!-- MapQaTor -->
<a
href="https://arxiv.org/abs/2412.21015"
class="external-link"
style="
background-color: #363636;
display: flex;
align-items: center;
padding: 10px 14px;
border-radius: 8px;
"
>
<i class="fa-solid fa-location-dot" style="margin-right: 6px"></i>
<span><strong>MapQaTor</strong> — ACL 2025</span>
</a>
<!-- MapEval -->
<a
href="https://arxiv.org/abs/2501.00316"
class="external-link"
style="
background-color: #363636;
display: flex;
align-items: center;
padding: 10px 14px;
border-radius: 8px;
"
>
<i class="fa-solid fa-map" style="margin-right: 6px"></i>
<span><strong>MapEval</strong> — ICML 2025</span>
</a>
<!-- MapAgent -->
<a
href="https://arxiv.org/abs/2509.05933"
class="external-link"
style="
background-color: #363636;
display: flex;
align-items: center;
padding: 10px 14px;
border-radius: 8px;
"
>
<i class="fa-solid fa-robot" style="margin-right: 6px"></i>
<span><strong>MapAgent</strong> — EACL 2026</span>
</a>
</div>
</div>
<!-- <embed src="files/overview.pdf" type="application/pdf" width="100%" height="600px" /> -->
<img
src="images/overview.svg"
alt="Overview"
style="width: 100%; margin-top: 1rem; margin-top: 2rem"
/>
<p>
Overview of MapEval. On the left, we show the annotation process, where
an expert gathers either visual snapshots or textual data from Google
Maps to create multiple-choice questions with ground truth labels. On
the right, we depict the evaluation process and input/output for the
three benchmark tasks in MapEval.
</p>
<h2 style="text-align: center">Introduction</h2>
<p>
Recent advancements in foundation models have improved autonomous tool
usage and reasoning, but their capabilities in map-based reasoning
remain underexplored. To address this, we introduce MapEval, a benchmark
designed to assess foundation models across three distinct
tasks—textual, API-based, and visual reasoning—through 700
multiple-choice questions spanning 180 cities and 54 countries, covering
spatial relationships, navigation, travel planning, and real-world map
interactions. Unlike prior benchmarks that focus on simple location
queries, MapEval requires models to handle long-context reasoning, API
interactions and visual map analysis, making it the most comprehensive
evaluation framework for geospatial AI. On evaluation of 30 foundation
models, including Claude-3.5-Sonnet, GPT-4o, Gemini-1.5-Pro, none
surpasses 67% accuracy, with open-source models performing significantly
worse and all models lagging over 20% behind human performance. These
results expose critical gaps in spatial inference, as models struggle
with distances, directions, route planning, and place-specific
reasoning, highlighting the need for better geospatial AI to bridge the
gap between foundation models and real-world navigation.
</p>
<h2 style="text-align: center">MapEval Overview</h2>
We introduce MapEval, a novel benchmark designed to evaluate the
geo-spatial reasoning capabilities of foundation models and AI agents in
complex map-based scenarios. MapEval addresses a critical gap in existing
benchmarks by evaluating models' ability to process heterogeneous
geo-spatial contexts, perform compositional reasoning, and interact with
real-world map tools. It features three task types— MapEval-API,
MapEval-Visual, and MapEval-Textual, that require models to collect world
information via map tools, a deep visual understanding, and reason over
diverse geo-spatial data (e.g., named entities, coordinates, operational
hours, distances, routes, user reviews/ratings, map images), all of which
remain challenging for state-of-the-art foundation models. Comprising 700
unique multiple-choice questions across 180 cities and 54 countries,
MapEval reflects real-world user interactions with map services while
pushing state-of-the-art models to understand spatial relationships, map
infographics, travel planning, POI search, and navigation. MapEval ensures
geographic diversity, realistic query patterns, and evaluation across
multiple modalities. By integrating long contexts, visual complexity, API
interactions, and questions requiring commonsense reasoning or recognition
of insufficient information (i.e., unanswerability), it offers a rigorous
framework for advancing geo-spatial AI capabilities.
<br />
<br />
With MapEval, we evaluated 30 prominent foundation models, where
Claude-3.5-Sonnet, GPT-4o, and Gemini-1.5-Pro showed competitive
performance overall. However, significant gaps emerged in MapEval-API,
with Claude-3.5-Sonnet agents outperforming GPT-4o and Gemini-1.5-Pro by
16% and 21%, respectively, and even larger disparities compared to
open-source models. Our detailed analyses revealed further insights into
model strengths and weaknesses. Despite these advances, all models still
fall short of human performance by over 20%, especially in handling
complex map images and rigorous reasoning, underscoring MapEval's role in
advancing geo-spatial understanding.
<h2 style="text-align: center; margin-top: 2rem" id="dataset">
Data Statistics
</h2>
<div class="responsive-flex">
<!-- <img src="images/key_stats.png" alt="Overview" class="half-width" > -->
<!-- <table class="half-width" style="font-size: small;">
<thead>
<td colspan="2">
<hr>
</td>
<tr>
<th>Statistics</th>
<th>Number</th>
</tr>
<tr>
<td colspan="2">
<hr>
</td>
</tr>
</thead>
<tbody>
<tr>
<td>Total unique question instances</td>
<td>700</td>
</tr>
<tr>
<td>- Questions with api or textual-context</td>
<td>300 (42.86%)</td>
</tr>
<tr>
<td>- Questions with visual-context</td>
<td>400 (57.14%)</td>
</tr>
<tr>
<td>Total unique countries</td>
<td>54</td>
</tr>
<tr>
<td>Total unique cities</td>
<td>180</td>
</tr>
<tr>
<td colspan="2">
<hr>
</td>
</tr>
<tr>
<td>Maximum textual-context length</td>
<td>1500</td>
</tr>
<tr>
<td>Maximum question length</td>
<td>107</td>
</tr>
<tr>
<td>Maximum questions from a country</td>
<td>132</td>
</tr>
<tr>
<td>Maximum questions from a city</td>
<td>44</td>
</tr>
<tr>
<td colspan="2">
<hr>
</td>
</tr>
<tr>
<td>Average textual-context length</td>
<td>435.63</td>
</tr>
<tr>
<td>Average question length</td>
<td>21.41</td>
</tr>
<tr>
<td colspan="2">
<hr>
</td>
</tr>
<tr>
<td>Unique number of textual-context</td>
<td>215</td>
</tr>
<tr>
<td>Unique number of visual-context</td>
<td>270</td>
</tr>
<tr>
<td colspan="2">
<hr>
</td>
</tr>
<tr>
<td>Min, Max, Avg Choices</td>
<td>2, 7, 4.004</td>
</tr>
<tr>
<td colspan="2">
<hr>
</td>
</tr>
<tr>
<td>Max zoom of visual-context</td>
<td>21.0</td>
</tr>
<tr>
<td>Min zoom of visual-context</td>
<td>8.0</td>
</tr>
<tr>
<td>Average zoom of visual-context</td>
<td>15.26</td>
</tr>
<tr>
<td colspan="2">
<hr>
</td>
</tr>
</tbody>
</table> -->
<div class="half-width">
<img src="images/key-stats.png" alt="Overview" style="width: 92%" />
</div>
<div class="half-width">
<img
src="images/sunburst_chart.svg"
alt="Overview"
style="
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align-items: center;
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<tspan class="line" dy="0em" x="0" y="0">Place Info</tspan>
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<tspan class="line" dy="0em" x="0" y="0">Counting</tspan>
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<tspan class="line" dy="0em" x="0" y="0">Unanswerable</tspan>
<tspan class="line" dy="1.3em" x="0" y="0">2.86%</tspan>
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<tspan class="line" dy="0em" x="0" y="0">Nearby</tspan>
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<tspan class="line" dy="0em" x="0" y="0">Trip</tspan>
<tspan class="line" dy="1.3em" x="0" y="0">9.57%</tspan>
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<tspan class="line" dy="0em" x="0" y="0">Routing</tspan>
<tspan class="line" dy="1.3em" x="0" y="0">9.43%</tspan>
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<tspan class="line" dy="0em" x="0" y="0">Place Info</tspan>
<tspan class="line" dy="1.3em" x="0" y="0">9.14%</tspan>
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<p style="text-align: center">
Distribution of tasks in <b>MapEval</b>
</p>
</div>
<!-- <div>
<script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
<script src="plot.js">
</script>
<div id="831fada6-f182-4dbd-8933-69c694372809" class="plotly-graph-div" style="height:100%; width:100%;"></div>
<script src="b.js">
</script>
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</div>
<h2 style="text-align: center; margin-top: 2rem" id="results">Results</h2>
<img
src="images/mapeval-textual.png"
alt="Overview"
style="width: 100%"
/>
<p style="text-align: center">Table 1: MapEval-Textual performances</p>
<img
src="images/mapeval-api.png"
alt="Overview"
style="width: 100%; margin-top: 2rem"
/>
<p style="text-align: center">Table 2: MapEval-API performances</p>
<img
src="images/mapeval-visual.png"
alt="Overview"
style="width: 100%; margin-top: 2rem"
/>
<p style="text-align: center">Table 3: MapEval-Visual performances</p>
<!-- <p>
In simple program synthesis tasks like HumanEval and MBPP we got highest scale of performance (Pass@1) scores. The current state-of-the-art method, Reflexion perform reasonably well, this approach does not generalize across varying datasets depicting a wide variety of problems. Self-reflection techniques enhance GPT-4's performance on HumanEval but result in a 3% decrease on the MBPP dataset. Consequently, even in HumanEval, with GPT-4, our Pass@1 surpasses Reflexion by ~3%. The significance of MapCoder shines through clearly when evaluated in competitive problem-solving contexts. Across datasets such as APPS, xCodeEval, and CodeContests, MapCoder demonstrates substantial enhancements over Direct prompting methods, with improvements of 41.3%, 52.6%, and 132.8% for ChatGPT, and 73.7%, 41.2%, and 135.1% for GPT4, respectively. Notably, the most challenging datasets are APPS and CodeContest, where MapCoder's performance stands out prominently. Importantly, on CodeContest our Pass@1 results match the Pass@5 scores of the concurrent state-of-the-art model AlphaCodium: 28.5% vs. their 29%. Furthermore, our Pass@5 in CodeContest is 35.2% results demonstrate an additional improvement of 12.8% over AlphaCodium.
</p> -->
<h2 style="text-align: center;" >Cite Us</h2>
<pre
style="white-space: pre; word-wrap: normal; padding: 1.25em 1.5em; overflow-x: auto; background-color: #eee; color: #4a4a4a; font-size: .875em; border-radius: 3px; margin: 0 3px;">
<code>@inproceedings{
dihan2025mapeval,
title={MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models},
author={Mahir Labib Dihan and MD Tanvir Hassan and MD TANVIR PARVEZ and Md Hasebul Hasan and Md Almash Alam and Muhammad Aamir Cheema and Mohammed Eunus Ali and Md Rizwan Parvez},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=hS2Ed5XYRq}
}</code></pre>
<!-- <pre style="white-space: pre-wrap; word-wrap: break-word;">
@article{dihan2024mapeval,
title={MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models},
author={Dihan, Mahir Labib and Hassan, Md Tanvir and Parvez, Md Tanvir and Hasan, Md Hasebul and Alam, Md Almash and Cheema, Muhammad Aamir and Ali, Mohammed Eunus and Parvez, Md Rizwan},
journal={arXiv preprint arXiv:2501.00316},
year={2024}
}
</pre> -->
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