Land Use Land Cover Classification from Satellite Imagery using mUnet: A Modified Unet Architecture

Land-use-land-cover classification(LULC) is used to automate the process of providing labels, describing the physical land type to represent how a land area is being used. Many sectors such as telecom, utility, hydrology etc need land use and land cover information from remote sensing images. This information provides an insight into the type of geographical distribution of a region with providing low level features such as amount of vegetation, building area, and geometry etc as well as higher level concepts such as land use classes. This information is particularly useful for resource-starved rapidly developing cities for urban planning and resource management. LULC also provides historical changes in land-use patterns over a period of time. In this paper, we analyze patterns of land use in urban and rural neighborhoods using high resolution satellite imagery, utilizing a state of the art deep convolutional neural network. The proposed LULC network, termed as mUnet is based on an encoder-decoder convolutional architecture for pixel-level semantic segmentation. We test our approach on 3 band, FCC satellite imagery covering 225 km(2) area of Karachi. Experimental results show the superiority of our proposed network architecture vis-a-vis other state of the art networks.