# Multi-label Cloud Segmentation Using a Deep Network

## TL;DR

This paper introduces a deep learning approach using U-Net for multi-label cloud segmentation in sky images, significantly outperforming existing binary segmentation methods.

## Contribution

It presents a novel application of U-Net for multi-label sky/cloud segmentation, advancing beyond binary segmentation techniques.

## Key findings

- Outperforms recent methods by a large margin
- Effective multi-label segmentation of sky and clouds
- Demonstrates the potential of deep networks for cloud detection

## Abstract

Different empirical models have been developed for cloud detection. There is a growing interest in using the ground-based sky/cloud images for this purpose. Several methods exist that perform binary segmentation of clouds. In this paper, we propose to use a deep learning architecture (U-Net) to perform multi-label sky/cloud image segmentation. The proposed approach outperforms recent literature by a large margin.