Recognizing and accurately labeling the complex cloud patterns requires specialized knowledge. To achieve the highest label accuracy for CloudSEN12+, we have meticulously designed a comprehensive five-step protocol that effectively addresses the unique challenges that cloud labeling poses.

In CloudSEN12+ we have improved the manual labeling of the images. CloudSEN12+ employs a dual-scoring approach to detect potential human errors in semantic segmentation.

CloudSEN12 provides an interactive map that displays regions of interest (ROI) for cloud and shadow detection in satellite images. This map allows users to explore and download geographic data relevant for remote sensing studies and environmental analysis.

All notable changes to the `cloudSEN12` project will be documented in this file. The format is based on Keep a Changelog - https://keepachangelog.com/en/1.0.0/, and this project adheres to Semantic Versioning - https://semver.org/spec/v2.0.0.html.

A brief overview of the tools/code used to build cloudSEN12.