# Dense Fusion Classmate Network for Land Cover Classification

Source: [Dense Fusion Classmate Network for Land Cover Classification](<https://www.sota2.com/research/paper/68fa66ab1e209131cea118fd>)

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

Data updated: 2025-10-25T21:55:21.281Z

Authors: Chao Tian, Cong Li, Jianping Shi

Published: 2019-11-19T09:25:59.000Z

## Abstract

Recently, FCNs based methods have made great progress in semantic segmentation. Different with ordinary scenes, satellite image owns specific characteristics, which elements always extend to large scope and no regular or clear boundaries. Therefore, effective mid-level structure information extremely missing, precise pixel-level classification becomes tough issues. In this paper, a Dense Fusion Classmate Network (DFCNet) is proposed to adopt in land cover 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/1911.08169>)

[PDF](<https://arxiv.org/pdf/1911.08169.pdf>)