# Feature Pyramid Network for Multi-Class Land Segmentation

arXiv: https://arxiv.org/abs/1806.03510 · HTML: https://mlsanity.com/paper/1806.03510/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2018.

## Built on by (most cited first, 24 of 24)

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- [CLAIRE: A Dual Encoder Network with RIFT Loss and Phi-3 Small Language Model Based Interpretability for Cross-Modality Synthetic Aperture Radar and Optical Land Cover Segmentation](https://mlsanity.com/paper/2509.11952/) (2025)
- [LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection](https://mlsanity.com/paper/2604.04086/) (2026)

## Abstract (arXiv)

Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation results, leaderboard score and our own experience this network shows reliable results for the DEEPGLOBE - CVPR 2018 land cover classification sub-challenge. Moreover, this network moderately uses memory that allows using GTX 1080 or 1080 TI video cards to perform whole training and makes pretty fast predictions.