Floods are one of the most destructive disasters worldwide and although they largely happen in rural, ruther than in urban areas, it is in the urban areas that substantial destruction of infrastructures is observed. Thus, cost effective methods to monitor flood damage and extent are required. In this paper, we investigate the implementation of U-Net on satellite and drone image dataset such as xBD and EDDA for building damage assessment in Mozambique. The recently published dataset EDDA was created by the National Institute for Disaster Management (INGD) and comprises drone imagery of Beira, in Mozambique. Using them, we obtained a dice score of 0.76 on building localization (BL) and mean intersection over the union (mIoU) of 0.54 on damage classification (DC). These are promising results considering that many datasets lack detailed information on African buildings. We also use some pre-trained models models such as ResNet for BL and DC.