Identifying the physical aspect of the earth's surface (Land cover) as well as how we exploit the land (Land use) is a challenging problem in environment monitoring and many other subdomains. This can be done through field surveys or analyzing satellite images(Remote Sensing). While carrying out field surveys is more comprehensive and authoritative, it is an expensive project and mostly takes a long time to update. With recent developments in the Space industry and the increased availability of satellite images (both free and commercial), deep learning and Convolutional Neural Networks has shown a promising result in land use classification. In this project, I used the freely available Sentinel-2 satellite images to classify 9 land use classes and 24000 labeled images ( Figure 2).