  With a number of marine populations in rapid decline, collecting and
analyzing data about marine populations has become increasingly important to
develop effective conservation policies for a wide range of marine animals,
including whales. Modern computer vision algorithms allow us to detect whales
in images in a wide range of domains, further speeding up and enhancing the
monitoring process. However, these algorithms heavily rely on large training
datasets, which are challenging and time-consuming to collect particularly in
marine or aquatic environments. Recent advances in AI however have made it
possible to synthetically create datasets for training machine learning
algorithms, thus enabling new solutions that were not possible before. In this
work, we present a solution - SeaDroneSim2 benchmark suite, which addresses
this challenge by generating aerial, and satellite synthetic image datasets to
improve the detection of whales and reduce the effort required for training
data collection. We show that we can achieve a 15% performance boost on whale
detection compared to using the real data alone for training, by augmenting a
10% real data. We open source both the code of the simulation platform
SeaDroneSim2 and the dataset generated through it.
