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.
@article{arxiv.2011.06825,
title = {LULC classification by semantic segmentation of satellite images using FastFCN},
author = {Md. Saif Hassan Onim and Aiman Rafeed Ehtesham and Amreen Anbar and A. K. M. Nazrul Islam and A. K. M. Mahbubur Rahman},
journal= {arXiv preprint arXiv:2011.06825},
year = {2022}
}