# End-to-end Cloud Segmentation in High-Resolution Multispectral Satellite Imagery Using Deep Learning

## TL;DR

This paper introduces a new high-resolution satellite cloud dataset and an end-to-end deep learning segmentation method that significantly improves accuracy in cloud detection tasks.

## Contribution

It provides the CloudPeru2 dataset and a CNN-based segmentation approach using Deeplab v3+ for automated cloud detection in satellite imagery.

## Key findings

- Achieved 96.62% accuracy in cloud segmentation
- Outperformed existing methods in precision and specificity
- Provided a valuable dataset for future research

## Abstract

Segmenting clouds in high-resolution satellite images is an arduous and challenging task due to the many types of geographies and clouds a satellite can capture. Therefore, it needs to be automated and optimized, specially for those who regularly process great amounts of satellite images, such as governmental institutions. In that sense, the contribution of this work is twofold: We present the CloudPeru2 dataset, consisting of 22,400 images of 512x512 pixels and their respective hand-drawn cloud masks, as well as the proposal of an end-to-end segmentation method for clouds using a Convolutional Neural Network (CNN) based on the Deeplab v3+ architecture. The results over the test set achieved an accuracy of 96.62%, precision of 96.46%, specificity of 98.53%, and sensitivity of 96.72% which is superior to the compared methods.