  Accurate urban maps provide essential information to support sustainable
urban development. Recent urban mapping methods use multi-modal deep neural
networks to fuse Synthetic Aperture Radar (SAR) and optical data. However,
multi-modal networks may rely on just one modality due to the greedy nature of
learning. In turn, the imbalanced utilization of modalities can negatively
affect the generalization ability of a network. In this paper, we investigate
the utilization of SAR and optical data for urban mapping. To that end, a
dual-branch network architecture using intermediate fusion modules to share
information between the uni-modal branches is utilized. A cut-off mechanism in
the fusion modules enables the stopping of information flow between the
branches, which is used to estimate the network's dependence on SAR and optical
data. While our experiments on the SEN12 Global Urban Mapping dataset show that
good performance can be achieved with conventional SAR-optical data fusion (F1
score = 0.682 $\pm$ 0.014), we also observed a clear under-utilization of
optical data. Therefore, future work is required to investigate whether a more
balanced utilization of SAR and optical data can lead to performance
improvements.
