Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system includes an aerial image database containing sensor data representing an aerial image of the earth surface, the sensor data comprising a feature vector for each pixel in the aerial image. A processor applies a plurality of classifiers to each feature vector to produce a plurality of classifier scores for each feature vector. The processor then determines a plurality of cluster probabilities for each feature vector, each cluster probability for a feature vector indicating a probability of the feature vector given a respective cluster of feature vectors. The processor uses the cluster probabilities for the feature vectors to form a respective weight for each of the plurality of classifiers. The processor combines the weights and the classifier scores to form an ensemble score for each pixel, the ensemble score indicating which of two possible land cover types is present on a portion of the earth surface represented by the pixel.

Abstract: A method of characterizing relationships among spatio-temporal events and a system to characterize the relationships are described. The method includes receiving information specifying the spatio-temporal events and associated categories from one or more sources. The method also includes building, using a processor, a directed acyclic graph (DAG) indicating a relationship among the categories for each of two or more space lag (SL) and time lag (TL) sets. Each of the two or more SL and TL sets defines a spatio-temporal boundary such that only the spatio-temporal events and the associated categories with (SL,TL)-neighborhoods inside the respective spatio-temporal boundary are considered in building the respective DAG. The respective (SL,TL)-neighborhood of each of the spatio-temporal events is a polygonal shape defined by the respective SL and the respective TL and the respective (SL,TL)-neighborhood of each of the categories is a union of the (SL,TL)-neighborhoods of the associated spatio-temporal events.