Monitoring of Urban Changes With Multimodal Sentinel 1 and 2 Data in Mariupol, Ukraine, in 2022/23

Abstract

The ability to constantly monitor urban changes is of significant socio-economic interest, such as 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 operating on multimodal 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 multimodal input, our method was more dependent on optical multispectral than synthetic aperture radar observations but resilient to loss of observations.

Keywords

• Deep neural network (DNN)
• multimodal
• remote sensing
• transfer learning
• urban change monitoring