# ACLNet: An Attention and Clustering-based Cloud Segmentation Network

arXiv: https://arxiv.org/abs/2207.06277 · HTML: https://mlsanity.com/paper/2207.06277/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation](https://mlsanity.com/paper/1904.07979/) (2019)
- [Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras](https://mlsanity.com/paper/1606.03669/) (2016)
- [Systematic study of color spaces and components for the segmentation of sky/cloud images](https://mlsanity.com/paper/1701.04520/) (2017)
- [Nighttime sky/cloud image segmentation](https://mlsanity.com/paper/1705.10583/) (2017)

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

- [Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey](https://mlsanity.com/paper/2211.14709/) (2022)
- [Short-Term Solar Irradiance Forecasting Under Data Transmission Constraints](https://mlsanity.com/paper/2403.12873/) (2024)

## Abstract (arXiv)

We propose a novel deep learning model named ACLNet, for cloud segmentation from ground images. ACLNet uses both deep neural network and machine learning (ML) algorithm to extract complementary features. Specifically, it uses EfficientNet-B0 as the backbone, "`a trous spatial pyramid pooling" (ASPP) to learn at multiple receptive fields, and "global attention module" (GAM) to extract finegrained details from the image. ACLNet also uses k-means clustering to extract cloud boundaries more precisely. ACLNet is effective for both daytime and nighttime images. It provides lower error rate, higher recall and higher F1-score than state-of-art cloud segmentation models. The source-code of ACLNet is available here: https://github.com/ckmvigil/ACLNet.