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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
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<title>Simulated Aerial Imagery Dataset</title>
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<h1 class="title is-1 publication-title">Error Correction through Multimodal Interpretable Meta Conditions:
Simulated Aerial Imagery Dataset
</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=en&user=JGtztIAAAAAJ" target="_blank">Noel
Ngu</a><sup>*</sup>,
<a href="https://scholar.google.com/citations?hl=en&user=hfU1wQwAAAAJ" target="_blank">Aditya
Taparia</a><sup>*</sup>,
<a href="https://scholar.google.com/citations?hl=en&user=z1lLFmoAAAAJ" target="_blank">Gerardo I.
Simari</a><sup>*</sup>,
<a href="https://scholar.google.com/citations?hl=en&user=Vj4muV8AAAAJ" target="_blank">Mario
Leiva</a><sup>*</sup>,
<a href="https://scholar.google.com/citations?hl=en&user=mmo0bDIAAAAJ" target="_blank">Ransalu
Senanayake</a><sup>*</sup>,
<a href="https://scholar.google.com/citations?user=OUAMn6oAAAAJ&hl=en" target="_blank">Paulo
Shakarian</a><sup>*</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">Arizona State University<br></span>
<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
</div>
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<div class="publication-links">
<!-- Arxiv PDF link -->
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<!-- Your image here -->
<img src="static/images/sample_train_images/sample_dust.png" alt="MY ALT TEXT" style="width: 75%;" />
<h2 class="subtitle has-text-centered">
The dust dataset contains images where dust is the prominent weather condition
<br /> alongside their bounding box annotations.
</h2>
</div>
<div class="item" style="display: flex; flex-direction:column; align-items: center;">
<!-- Your image here -->
<img src="static/images/sample_train_images/sample_fog.png" alt="MY ALT TEXT" style="width: 75%;" />
<h2 class="subtitle has-text-centered">
The fog dataset contains images where fog is the prominent weather condition
<br /> alongside their bounding box annotations.
</h2>
</div>
<div class="item" style="display: flex; flex-direction:column; align-items: center;">
<!-- Your image here -->
<img src="static/images/sample_train_images/sample_maple_leaf.png" alt="MY ALT TEXT"
style="width: 75%;" />
<h2 class="subtitle has-text-centered">
The maple_leaf dataset contains images where maple leaves is the prominent weather condition
<br /> alongside their bounding box annotations.
</h2>
</div>
<div class="item" style="display: flex; flex-direction:column; align-items: center;">
<!-- Your image here -->
<img src="static/images/sample_train_images/sample_rain.png" alt="MY ALT TEXT" style="width: 75%;" />
<h2 class="subtitle has-text-centered">
The rain dataset contains images where rain is the prominent weather condition
<br /> alongside their bounding box annotations.
</h2>
</div>
<div class="item" style="display: flex; flex-direction:column; align-items: center;">
<!-- Your image here -->
<img src="static/images/sample_train_images/sample_snow.png" alt="MY ALT TEXT" style="width: 75%;" />
<h2 class="subtitle has-text-centered">
The snow dataset contains images where snow is the prominent weather condition
<br /> alongside their bounding box annotations.
</h2>
</div>
</div>
</div>
</div>
</section>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<br />
<br />
<h2 class="subtitle has-text-centered">
Arizona State University is releasing a new aerial imagery dataset aimed at advancing object detection and
creating new solutions for handling diverse data distributions in aerial imagery.
The dataset is generated using the AirSim simulator and contains images taken under different weather
conditions. Object-detection models fine-tuned on each dataset distribution are also provided as well to
facilitate
research in this area.
</h2>
</div>
</div>
</section>
<!-- End teaser video -->
<!-- Paper abstract -->
<section class="section hero is-light">
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<h2 class="title is-3">Introduction</h2>
<div class="content has-text-justified">
<p>
This dataset contains images from the AirSim simulator in the City environment. The images are taken at
random positions within the environment under various weather conditions
such as fog, rain, snow, dust, and maple leaves. The dataset is aimed at advancing object detection by
addressing diverse data distributions.
<br />
The idea of this dataset is that models trained on different distributions of data (which in this case is
different distributions of weather) can be
combined using an ensemble approach to effectively handle a separate distribution to enable better
generalization and performance on diverse datasets.
</p>
</div>
</div>
</div>
</div>
</section>
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<h2 class="title is-3">Training Sets</h2>
<div class="content has-text-justified">
<p>
It contains images from the AirSim simulator in the CityEnviron environment. The images are taken at
random positions within the environment under weather conditions
such as fog, rain, snow, dust, and maple leaves.
</p>
The <a href="https://github.com/lab-v2/aerial-imagery-dataset">GitHub repository</a>
contains datasets with different distributions:
<ul>
<li>dust: This dataset contains images where dust is the prominent weather condition.</li>
<li>fog: This dataset contains images where fog is the prominent weather condition.</li>
<li>maple_leaf: This dataset contains images where maple leaves are the prominent weather condition.</li>
<li>rain: This dataset contains images where rain is the prominent weather condition.</li>
<li>snow: This dataset contains images where the parameter for snow is set to a high value.</li>
</ul>
The bar charts below display the average intensity of each weather parameter in each training set.
<img src="static/images/sample_train_images/train_set_distribution.png" alt="MY ALT TEXT"
style="width: 100%;" />
</div>
</div>
</div>
</div>
</section>
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Test Set</h2>
<div class="content has-text-justified">
<p>
The test set contains aerial imagery captured under mixed weather conditions using AirSim's drone vehicle.
While the training sets each contain images with a particular prominent weather condition, the test set
contains a variety of mixed weather conditions to emulate real-life scenarios and to test the
generalization of models trained on the training sets.
</p>
The bar chart below display the average intensity of each weather parameter in the test set.
<p class="has-text-centered">
<img src="static/images/sample_test_images/test_set_distribution.png" alt="MY ALT TEXT"
style="width: 40%;" />
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section hero is-normal">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Implementation</h2>
<div class="content has-text-justified">
<p>
The dataset contains various images captured at random positions within
the City environment in Airsim. The following is a table containing the various parameters that were set
in the AirSim simulator to produce the datasets with different distributions.
<br />
<img src="static/images/table.png" alt="MY ALT TEXT" style="width: 100%;" />
<br />
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Data format</h2>
<div class="content has-text-justified">
<p>
Each dataset adheres to the COCO format and includes three key folders: annotations, train, and val.
<br />
<ul>
<li>annotations: This folder contains the annotations for the train and validation datasets in the COCO
format
named custom_train.json and custom_val.json respectively.</li>
<li>train: This folder contains the images for the training set.</li>
<li>val: This folder contains the images for the validation dataset.</li>
</ul>
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section hero is-normal">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Models</h2>
<div class="content has-text-justified">
<p>Various models were trained on each distribution using Facebook's DeTR model. The models were trained
with consistent hyperparameters, which include:</p>
<ul>
<li>Number of epochs: 500</li>
<li>Learning rate: 0.00005</li>
<li>Weight decay: 0.0001</li>
<li>Max gradient norm: 0.01</li>
</ul>
<p>This setup ensures that each model is optimized for its specific distribution while following a
standardized training process.</p>
<p>The model weights for models trained on each training set are available for general use.</p>
</div>
<figure>
<img src="static/images/model_comparisons.png" alt="MY ALT TEXT" style="width: 100%;" />
<figcaption>
Each model has different strengths, one can see that the model on the left trained on maple leaves is able
to
distinguish between maple leaves and pedestrians whereas the one on the left trained on snow has some
trouble.
</figcaption>
</figure>
</div>
</div>
</div>
</section>
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<h2 class="title">BibTeX</h2>
<pre><code>
@misc{shakarian2025error,
author = {Paulo Shakarian and Ransalu Senanayake and Gerardo I. Simari and Mario Leiva and Aditya Taparia and Noel Ngu},
title = {Error Correction through Multimodal Interpretable Meta Conditions: Simulated Aerial Imagery Dataset},
year = {2025},
url = {https://neurosymbolic.asu.edu/metacognition/}
}
</code></pre>
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