Abstract: Computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken.

Abstract: Computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. Training data can then be selected from these labelled samples using a method based on simulated annealing, which iteratively searches through subsets of the available patches to identify a solution that is as close to the most optimal solution as possible.

Abstract: Computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken.

Abstract: Computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. Training data can then be selected from these labelled samples using a method based on simulated annealing, which iteratively searches through subsets of the available patches to identify a solution that is as close to the most optimal solution as possible.