  With the rapid rise of neural architecture search, the ability to understand
its complexity from the perspective of a search algorithm is desirable.
Recently, Traor\'e et al. have proposed the framework of Fitness Landscape
Footprint to help describe and compare neural architecture search problems. It
attempts at describing why a search strategy might be successful, struggle or
fail on a target task. Our study leverages this methodology in the context of
searching across sensors, including sensor data fusion. In particular, we apply
the Fitness Landscape Footprint to the real-world image classification problem
of So2Sat LCZ42, in order to identify the most beneficial sensor to our neural
network hyper-parameter optimization problem. From the perspective of
distributions of fitness, our findings indicate a similar behaviour of the
search space for all sensors: the longer the training time, the larger the
overall fitness, and more flatness in the landscapes (less ruggedness and
deviation). Regarding sensors, the better the fitness they enable (Sentinel-2),
the better the search trajectories (smoother, higher persistence). Results also
indicate very similar search behaviour for sensors that can be decently fitted
by the search space (Sentinel-2 and fusion).
