Ground truth refers to the accurate, real-world information used in various fields like machine learning, computer vision, and remote sensing to validate or train model predictions or algorithms. It is considered the ultimate standard or benchmark against which the results of a system or model are compared to measure its performance or accuracy.
Ground Truth in practice
In the context of machine learning or computer vision, ground truth is often represented by annotated data. For instance, in an image-recognition task, each image in the training dataset is labeled with the correct answer, or "ground truth", e.g., "cat", "dog", etc. The model is then trained on these images and uses the ground truth to learn how to identify aspects of the data correctly.
Similarly, in geospatial studies or remote sensing, ground truth data may be obtained by physically visiting the location or utilizing highly accurate GPS devices to confirm the information collected remotely.
Moreover, in autonomous vehicles testing, the "ground truth" is the actual location of the car at a specific time, which is typically collected using highly accurate GPS. The car's perception and prediction systems are then compared against this ground truth data.
The use of ground truth allows scientists and researchers to assess the effectiveness and accuracy of their models or techniques by comparing predicted results against real-world, accurate data.
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