# LULC classification by semantic segmentation of satellite images using FastFCN

arXiv: https://arxiv.org/abs/2011.06825 · HTML: https://mlsanity.com/paper/2011.06825/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [DeepTriNet: A Tri-Level Attention Based DeepLabv3+ Architecture for Semantic Segmentation of Satellite Images](https://mlsanity.com/paper/2310.06848/) (2023)
- [Segmentation of arbitrary features in very high resolution remote sensing imagery](https://mlsanity.com/paper/2412.16046/) (2024)

## Abstract (arXiv)

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.