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<!DOCTYPE html>
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<meta name="description"
content="AutoMergeNet: AutoML-based M-Source Satellite Data Fusion Evaluated with Atmospheric Case Studies.">
<meta name="keywords" content="IEEE JSTARS, methane plume detection,carbon monoxide,atmospheric plumes,AutoML, Neural Architecture Search, Data fusion, GeoAI, ML4EO">
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<title>IEEE JSTARS AutoMergeNet</title>
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Representation bias in methane plume detection
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<h1 class="title is-1 publication-title">AutoMergeNet: AutoML-based M-Source Satellite Data Fusion Evaluated with Atmospheric Case Studies.</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://juliawasala.nl">Julia Wąsala</a><sup>1,2</sup>,</span>
<span class="author-block">
Joannes D. Maasakkers<sup>2</sup>,</span>
<span class="author-block">
Berend J. Schuit<sup>2,5</sup>,
</span>
<span class="author-block">
Gijs Leguijt<sup>2,6</sup>,
</span>
<span class="author-block">
Ilse Aben<sup>2</sup>,
</span>
<span class="author-block">
Rochelle Schneider<sup>3</sup>,
</span>
<span class="author-block">
Holger Hoos<sup>4</sup>,
</span>
<span class="author-block">
Mitra Baratchi<sup>1</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Leiden Institute of Advanced Computer Science
(LIACS),</span>
<span class="author-block"><sup>2</sup>SRON Space Research Organisation Netherlands,</span>
<span class="author-block"><sup>3</sup>Phi-lab, ESA-ESRIN,</span>
<span class="author-block"><sup>4</sup>Chair of AI Methodology, RWTH Aachen</span>
<span class="author-block"><sup>5</sup>GHGSat Inc.</span>
<span class="author-block"><sup>5</sup>Department of
Climate, Air and Sustainability at the Netherlands Organisation for Applied
Scientific Research, TNO</span>
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<span>Methane Data</span>
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<h2 class="subtitle has-text-centered">
Accepted at IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
Accurate detection of anomalous phenomena in satellite data often requires data layers containing complementary
information (e.g., data from different sensors, auxiliary features such as land cover maps, and metadata regarding data
quality).
However, existing highly specialised approaches to fuse multiple data layers cannot be transferred to other related
problems, as they rely on expert-selected features and manual pipeline design.
In this work, we propose AutoMergeNet,
a framework for satellite image data fusion
based on Neural Architecture Search (NAS).
AutoMergeNet generates neural networks that fuse any number of raster data layers.
Consequently, it can address different classification problems based on satellite images without manual pipeline design.
We designed the search space of AutoMergeNet by identifying relevant design choices from the image classification and
data fusion literature.
AutoMergeNet automatically transforms image classification networks into multi-branch networks by optimising critical
architectural and training hyperparameters.
Since the high dimensionality of multimodal image data poses a challenge for data fusion problems with limited labels,
we use an auxiliary unimodal classifier combined with AutoMergeNet.
We evaluate AutoMergeNet on a methane plume detection dataset from the literature and our newly created carbon monoxide
plume detection dataset.
AutoMergeNet performs strongly and consistently on these two multimodal classification problems, outperforming six
baseline methods selected from state-of-the-art image classification approaches.
Finally, we demonstrate the usability of our framework with a realistic methane plume detection use case, which shows
that AutoMergeNet can be used as a highly specialised, state-of-the-art approach.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@ARTICLE{wasala_2025,
author={W{\k a}sala, Julia and Maasakkers, Joannes D. and Schuit, Berend J. and Leguijt, Gijs and Aben, Ilse and
Schneider, Rochelle and Hoos, Holger and Baratchi, Mitra},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title={{{AutoMergeNet}}: {{AutoML}}-based{{ M-Source Satellite Data Fusion Evaluated}} with {{Atmospheric Case
Studies}}},
year={2025},
volume={},
number={},
doi={10.1109/JSTARS.2025.3621068}}</code></pre>
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