Cleanlab Studio is a no-code platform that uses novel Data-Centric AI methods to improve the quality of image/text/tabular datasets and machine learning models, automatically detecting issues such as labeling errors, outliers, duplicates, and mislabeled examples in satellite imagery data. The RESISC45 dataset, with over 1,400 Google Scholar citations, is used as an example, where Cleanlab Studio detected 281 label issues, 363 outliers, and 20 near duplicates, revealing the potential for automated data correction to improve the reliability of ML models and analytics in critical situations like disaster response. By using Cleanlab Studio, users can automate the process of identifying and addressing these issues, leading to more accurate scientific research, policies, and financial outcomes, while also enabling the development of more complex and powerful machine learning models.