  Climate change is expected to intensify and increase extreme events in the
weather cycle. Since this has a significant impact on various sectors of our
life, recent works are concerned with identifying and predicting such extreme
events from Earth observations. With respect to wildfire danger forecasting,
previous deep learning approaches duplicate static variables along the time
dimension and neglect the intrinsic differences between static and dynamic
variables. Furthermore, most existing multi-branch architectures lose the
interconnections between the branches during the feature learning stage. To
address these issues, this paper proposes a 2D/3D two-branch convolutional
neural network (CNN) with a Location-aware Adaptive Normalization layer (LOAN).
Using LOAN as a building block, we can modulate the dynamic features
conditional on their geographical locations. Thus, our approach considers
feature properties as a unified yet compound 2D/3D model. Besides, we propose
using the sinusoidal-based encoding of the day of the year to provide the model
with explicit temporal information about the target day within the year. Our
experimental results show a better performance of our approach than other
baselines on the challenging FireCube dataset. The results show that
location-aware adaptive feature normalization is a promising technique to learn
the relation between dynamic variables and their geographic locations, which is
highly relevant for areas where remote sensing data builds the basis for
analysis. The source code is available at https://github.com/HakamShams/LOAN.
