# LULC classification by semantic segmentation of satellite images using FastFCN

Source: [LULC classification by semantic segmentation of satellite images using FastFCN](<https://www.sota2.com/research/paper/68fa6be01e209131cea30cf0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:59:05.045Z

Authors: Md. Saif Hassan Onim, Aiman Rafeed Ehtesham, Amreen Anbar, A. K. M. Nazrul Islam, A. K. M. Mahbubur Rahman

Published: 2020-11-13T09:33:03.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.06825>)

[PDF](<https://arxiv.org/pdf/2011.06825.pdf>)