Evolutionary Architecture Search for Chlorophyll-$a$ Prediction in Lakes using Sentinel-2
Kursat KomurcuLinas Petkevicius
Oct 2026
Machine Learning
Abstract
Small tabular datasets with expert-designed spectral features are the norm in
operational Earth observation, and the networks applied to them are typically
hand-designed. We revisit one such published model -- a Sentinel-2 algal
bloom classifier -- and ask what architecture search adds, holding the task,
the features and the lake-level train/test split of the original study fixed.
Searching an extended multilayer-perceptron space with regularized evolution,
and selecting on inner-cross-validation AUC only, we find networks that
improve held-out AUC from 0.790 to 0.820 and accuracy from 0.733 to 0.748
while using 409 trainable parameters, 26 times fewer than the strongest
hand-designed reference. The search converges on a consistent recipe -- a
single narrow layer, RMS normalisation, $\tanh$ activation, step-decayed
RMSprop and weight averaging -- that a practitioner would be unlikely to
reach by default. At 1.6\,kB the resulting model is small enough to serve as
an onboard screening trigger, which is the setting that motivates the work.
Code: https://github.com/VU-AIML/automl4eo-bloom-nas.
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