What is the acceptable minimum accuracy in image classification?

The acceptable minimum accuracy in image classification is often set at 85%. This threshold represents a significant balance between reliability and practicality in various applications, such as remote sensing, land cover classification, and object recognition tasks. Achieving this level of accuracy ensures that the classification results are sufficiently trustworthy for decision-making processes, particularly in fields where the implications of misclassification can be substantial, such as environmental monitoring, urban planning, and resource management.

While accuracies lower than this may still be used in certain contexts, they typically indicate a higher risk of errors and may not deliver the necessary confidence for critical applications. Therefore, the standard of 85% is widely recognized as a benchmark that reflects a commitment to both quality and usability in image classification tasks.