Abstract: Disclosed are methods for training a first machine learning model, which is a sequential generative model, and a second machine learning model, which is a sequence-to-sequence model, with a training data set including a plurality of specific routes and with a plurality of generic routes mapped from the specific routes. A method for synthesizing mobility traces includes: generating a plurality of synthetic generic routes, each synthetic generic route including an ordered sequence of synthetic generic positions, and generating a plurality of synthetic specific routes using the plurality of synthetic generic routes. Each synthetic specific route corresponds to a synthetic generic route and includes a corresponding ordered sequence of synthetic specific positions. The synthetic specific positions have a finer granularity than the synthetic generic positions.

Abstract: A method is directed to identifying spatially extensive image features that are not pixelwise contiguous in a two-dimensional digital image to be examined having pixels arranged in grid form in the form of a rectangular grid. Each pixel of the two-dimensional digital image is respectively assigned two coordinate values corresponding to their position in the rectangular grid of the digital image, and the respective coordinate values of adjoining pixels respectively differ by one pixel dimension. The individual pixels are run through in the order of a regular scan.

Abstract: A method is directed to identifying spatially extensive image features that are not pixelwise contiguous in a two-dimensional digital image to be examined having pixels arranged in grid form in the form of a rectangular grid. Each pixel of the two-dimensional digital image is respectively assigned two coordinate values corresponding to their position in the rectangular grid of the digital image, and the respective coordinate values of adjoining pixels respectively differ by one pixel dimension. The individual pixels are run through in the order of a regular scan.