Abstract: A method, a system, and a computer program product for performing on-demand feature extraction from a raw image of an object for analysis. A query is executed to retrieve an image of an object. Using one or more parameters of the query, a raw image of the object is compressed to generate a compressed image of the object. One or more features associated with the object are extracted from the compressed image of the object. Based on the compressed image of the object, the image of the object is generated using the extracted one or more features of the object.

Abstract: The present disclosure involves systems, software, and computer implemented methods for predicting wildfires on the basis of biophysical indicators and spatiotemporal properties. A method includes receiving a request for a wildfire prediction for at least one geographical area. At least one biophysical indicator is identified. Each biophysical indicator provides biophysical data for the at least one geographical area. The at least one biophysical indicator is provided to a long short term memory (LSTM) network. The LSTM network includes a convolutional neural network (CNN) for each of multiple LSTM units. Each LSTM unit and each CNN are associated with a historical time period in a time series. The LSTM is used to generate at least one prediction for wildfire risk for the at least one geographical area for an upcoming time period. The at least one prediction is provided responsive to the request.

Abstract: Systems, software, and computer implemented methods can be used to predict wildfires based on biophysical and spatiotemporal data. A method includes receiving a request for a wildfire prediction for at least one geographical area. At least one biophysical indicator is identified. Each biophysical indicator provides biophysical data for the at least one geographical area. The at least one biophysical indicator is provided to a convolutional neural network (CNN). The CNN is trained using ground truth data that includes historical information about wildfires for at least one ground truth geographical area. The CNN is used to generate at least one prediction for wildfire risk for the at least one geographical area. The at least one prediction is provided responsive to the request.

Abstract: A method, a system, and a computer program product for performing on-demand feature extraction from a raw image of an object for analysis. A query is executed to retrieve an image of an object. Using one or more parameters of the query, a raw image of the object is compressed to generate a compressed image of the object. One or more features associated with the object are extracted from the compressed image of the object. Based on the compressed image of the object, the image of the object is generated using the extracted one or more features of the object.

Abstract: Technologies are provided for implementing temporal and spatio-temporal spiking neural networks (SNNs) using neuromorphic hardware devices. Temporal synapse circuits, with associated weight values, can be used to control spike times of connected neuron circuits. The controlled spike times of multiple neuron circuits can be used to temporally encode information in a neural network in neuromorphic hardware. Neuron circuits in a state space detection layer can be organized into multiple subsets. Neuron circuits in different subsets can be connected to output neuron circuits in an output layer by separate temporal synapse circuits. Spiking signals sent from the neuron circuits in the state space detection layer via separate temporal synapse circuits can cause associated output neuron circuits to generate output spiking signals at different times. The various spike times of the output neuron circuits can be aggregated to produce an output signal for the network.

Abstract: The present disclosure involves systems, software, and computer implemented methods for predicting wildfires on the basis of biophysical indicators and spatiotemporal properties. A method includes receiving a request for a wildfire prediction for at least one geographical area. At least one biophysical indicator is identified. Each biophysical indicator provides biophysical data for the at least one geographical area. The at least one biophysical indicator is provided to a convolutional neural network (CNN). The CNN is trained using ground truth data that includes historical information about wildfires for at least one ground truth geographical area. The CNN is used to generate at least one prediction for wildfire risk for the at least one geographical area. The at least one prediction is provided responsive to the request.

Abstract: The present disclosure involves systems, software, and computer implemented methods for predicting wildfires on the basis of biophysical indicators and spatiotemporal properties. A method includes receiving a request for a wildfire prediction for at least one geographical area. At least one biophysical indicator is identified. Each biophysical indicator provides biophysical data for the at least one geographical area. The at least one biophysical indicator is provided to a long short term memory (LSTM) network. The LSTM network includes a convolutional neural network (CNN) for each of multiple LSTM units. Each LSTM unit and each CNN are associated with a historical time period in a time series. The LSTM is used to generate at least one prediction for wildfire risk for the at least one geographical area for an upcoming time period. The at least one prediction is provided responsive to the request.