# CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation

## TL;DR

CloudSegNet is a novel deep learning model that unifies daytime and nighttime cloud image segmentation, achieving state-of-the-art accuracy in a single framework for sky/cloud images.

## Contribution

It introduces the first integrated deep network for nychthemeron cloud segmentation, combining daytime and nighttime images in one lightweight architecture.

## Key findings

- Achieves state-of-the-art segmentation accuracy on public datasets.
- Successfully unifies daytime and nighttime cloud analysis.
- Demonstrates robustness across different lighting conditions.

## Abstract

We analyze clouds in the earth's atmosphere using ground-based sky cameras. An accurate segmentation of clouds in the captured sky/cloud image is difficult, owing to the fuzzy boundaries of clouds. Several techniques have been proposed that use color as the discriminatory feature for cloud detection. In the existing literature, however, analysis of daytime and nighttime images is considered separately, mainly because of differences in image characteristics and applications. In this paper, we propose a light-weight deep-learning architecture called CloudSegNet. It is the first that integrates daytime and nighttime (also known as nychthemeron) image segmentation in a single framework, and achieves state-of-the-art results on public databases.