A significant amount of research has been carried out in extracting land
surface objects, but intelligent digital surface models for monitoring land surface
objects are still an active research topic due to emerging technologies such as IoT and
Blockchain. These technologies play an important role in quantifying the ecological
and geographical properties of the land surface. About such technology of detecting
buildings, roads, and terrain from satellite images offer a lot of potential for tracking
the migration of large chunks of the population and helps in geographical analysis of
the city. In this paper, we explore a Convolutional Neural Network method for
extracting land surface objects from satellite imaging with the help of U-Net. As a
known fact, the number of disasters occurring every year affects thousands of the
population, so suitable mechanisms must be provided for rescue operations. To provide
these rescue operations, predictions about the geographical location are of primary
importance. Our model produces reasonable accuracy of 60.62% at a very minimal loss
rate.
Keywords: U-Net, Spatial Processing, Image Segmentation, Deep Learning, Computer Vision, Down and Up Sampling, Skip Connection.