Abstract: A process for Earth observation and analysis by pre-processing remote sensing images which may be from sources including MODIS, Proba-V, Landsat and/or Sentinel or any other space-borne or airborne sensor. Filtering the images by applying a temporal signal processing filter to a time series of remote sensing images and extracting descriptive statistics from image pixels of the remote sensing images to create input X parameters for use in a machine learning process. Applying the machine learning process to create a model which determines how the input X-parameter values map to the range of possible Y-parameter values in a way that improves RAM allocation and parallelizing in the software and processors during the machine learning process. Applying the output from machine learning to a potentially new Area of Interest to determine or predict Y-values for the known X-values using data scoring. Generating calibrated output images corresponding to the specific regions defining the Areas of Interest.

Abstract: A process for Earth observation and analysis by pre-processing remote sensing images which may be from sources including MODIS, Proba-V, Landsat and/or Sentinel or any other space-borne or airborne sensor. Filtering the images by applying a temporal signal processing filter to a time series of remote sensing images and extracting descriptive statistics from image pixels of the remote sensing images to create input X parameters for use in a machine learning process. Applying the machine learning process to create a model which determines how the input X-parameter values map to the range of possible Y-parameter values in a way that improves RAM allocation and parallelizing in the software and processors during the machine learning process. Applying the output from machine learning to a potentially new Area of Interest to determine or predict Y-values for the known X-values using data scoring. Generating calibrated output images corresponding to the specific regions defining the Areas of Interest.

Abstract: Aspects of the present disclosure involve systems, methods, computer program products, and the like, for tracking, assessing and predicting human behavioral disorders in real time through a mobile device. In general, the mobile health platform involves tracking a geographical location of a user of the system through the mobile device, receiving environmental and user-provided information through the mobile device or from another source, and processing the received information. In one embodiment, the processing of the received information provides for a prediction of a future human behavior and such a prediction may be provided to the user's mobile device. For example, the information may indicate that a user of the mobile device is at risk for a particular human behavior and, as a result, a warning of the risk of the human behavior is transmitted to the user's mobile device.