University of Minnesota

Anuj Karpatne

Vipin Kumar

Ankush Khandelwal

Xiaowei Jia

A method includes receiving a satellite image of an area and classifying each pixel in the satellite image as representing water, land or unknown using a model. For each of a plurality of possible water levels, a cost associated with the water level is determined, wherein determining the cost associated with a water level includes determining a number of pixels for which the model classification must change to be consistent with the water level and determining a difference between the water level and a water level determined for the area at a previous time point. The lowest cost water level is selected and used to reclassify at least one pixel.

Satellite image classification across multiple resolutions and time using ordering constraint among instances

A method includes classifying low-resolution pixels of a low-resolution satellite image of a geographic area to form an initial classification map and selecting at least one physically-consistent classification map of the low-resolution pixels based on the initial classification map. A water level associated with at least one of the physically-consistent classification maps is then used to identify a set of high-resolution pixels representing a perimeter of water in the geographic area.

A method of identifying land cover includes receiving multi-spectral values for a plurality of locations at a plurality of times. A location is selected and for each time in the plurality of times, a latent representation of the multi-spectral values is determined based on a latent representation of multi-spectral values determined for a previous time and multi-spectral values for the previous time of a plurality of other locations that are near the selected location. The determined latent representation is then used to predict a land cover for the selected location at the time.

Predicting land covers from satellite images using temporal and spatial contexts

A method includes receiving data for an entity for each of a plurality of time points. For each of a plurality of time windows that each comprises a respective plurality of time points, a confidence value is determined. The confidence value provides an indication of the degree to which the time window contains data that is useful in discriminating between classes. The confidence values are used to determine a probability of a class and the probability of the class is used to set a predicted class for the entity.

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Minneapolis, MN, United States of America

Anuj Karpatne