  The ability to constantly monitor urban changes is of significant
socio-economic interest, like detecting trends in urban expansion or tracking
the vitality of urban areas. Especially in present conflict zones or disaster
areas, such insights provide valuable information to keep track of the current
situation. However, they are often subject to limited data availability in
space and time. We built on our previous work, which used a transferred Deep
Neural Network (DNN) operating on multi-modal Sentinel 1 and 2 data. In the
current study, we have demonstrated and discussed its applicability in
monitoring the present conflict zone of Mariupol, Ukraine, with high-temporal
resolution Sentinel time series for the years 2022/23. A transfer to that
conflict zone was challenging due to the limited availability of recent Very
High Resolution (VHR) data. The current work had two objectives. First,
transfer learning with older and publicly available VHR data was shown to be
sufficient. That guaranteed the availability of more and less expensive data as
time constraints were relaxed. Second, in an ablation study, we analyzed the
effects of loss of observations to demonstrate the resiliency of our method.
That was of particular interest due to the malfunctioning of Sentinel 1B
shortly before the selected conflict. Our study demonstrated that urban change
monitoring is possible for present conflict zones after transferring with older
VHR data. It also indicated that, despite the multi-modal input, our method was
more dependent on optical multispectral than Synthetic Aperture Radar (SAR)
observations but resilient to loss of observations.
