  This paper analyses how well a Fast Fully Convolutional Network (FastFCN)
semantically segments satellite images and thus classifies Land Use/Land
Cover(LULC) classes. Fast-FCN was used on Gaofen-2 Image Dataset (GID-2) to
segment them in five different classes: BuiltUp, Meadow, Farmland, Water and
Forest. The results showed better accuracy (0.93), precision (0.99), recall
(0.98) and mean Intersection over Union (mIoU)(0.97) than other approaches like
using FCN-8 or eCognition, a readily available software. We presented a
comparison between the results. We propose FastFCN to be both faster and more
accurate automated method than other existing methods for LULC classification.
