  In city, information about urban objects such as water supply, railway lines,
power lines, buildings, roads, etc., is necessary for city planning. In
particular, information about the spread of these objects, locations and
capacity is needed for the policymakers to make impactful decisions. This
thesis aims to segment the building and roads from the aerial image captured by
the satellites and UAVs. Many different architectures have been proposed for
the semantic segmentation task and UNet being one of them. In this thesis, we
propose a novel architecture based on Google's newly proposed EfficientNetV2 as
an encoder for feature extraction with UNet decoder for constructing the
segmentation map. Using this approach we achieved a benchmark score for the
Massachusetts Building and Road dataset with an mIOU of 0.8365 and 0.9153
respectively.
