Abstract: A method, computer system, and a computer program for weather prediction are provided. The method may include receiving a first weather event associated with a first location. The present invention may further include inputting the first weather event into a machine learning model generated via mapping historical weather data into a latent space and via identifying, in the latent space, climate teleconnections amongst historical weather events at various locations. The method may further include in response to the inputting, receiving by the computing device a weather prediction for a second location, the weather prediction being based on a predicted climate teleconnection between the first location and the second location with respect to the first weather event, wherein the teleconnections machine learning model maps the first weather event into latent code for the latent space in order to generate the weather prediction for the second location.

Abstract: A method, computer system, and a computer program for generating weather simulations based on significant events is provided. The present invention may include receiving a plurality of environmental data associated with a geographic area. The present invention may then include retrieving one or more data feeds including a plurality of events. The present invention may further include associating the plurality of events with a subset of the plurality of environmental data. The present invention may further include generating a knowledge graph for the geographic area based on the association.

Abstract: A computer completes a data image analysis task. The computer receives a machine learning (ML) model trained for use with image data content characterized by first context. The computer receives an evaluation image dataset having evaluation image data content characterized by a second context. The computer receives a request to complete an image data analysis task for the evaluation image dataset using the ML model. The computer compares the contexts to determine whether the contexts are similar and whether the evaluation image dataset is compatible with the ML model. If the evaluation dataset is incompatible with the ML model, the computer uses the generative model to generate a ML model compatible synthetic image dataset based on the evaluation dataset. The computer applies the ML model to the synthetic image dataset to provide an answer for the data image analysis task; the computer delivers the answer to a user interface.

Abstract: A processor may receive weather event data. The processor may determine, utilizing an artificial intelligence model mapping weather events having weather impacts with a higher likelihood of occurrence proximate to each other in the latent space, a weather impact associated with a weather event. In some embodiments, the artificial intelligence model may be trained using historical weather event data and historical weather impact data associated with the historical weather event data. The processor may output the weather impact associated with the weather event to a user.

Abstract: A computer implemented method of predictive weather occurrences includes generating, by a computer processor, a training model through artificial intelligence. The training model is based on climate data processed by a variational autoencoder. A geographic location is selected for climate study. Historical weather measurements associated with the selected geographic location are retrieved from a knowledge climate database. The retrieved historical weather measurements are processed using the training model. The training model receives threshold parameters defining extremeness of weather. Extremeness is based on a weather intensity data point being farther from a norm than closer to the norm. Synthetic weather data is generated for the selected location, wherein the synthetic weather data predicts weather events satisfying the extremeness threshold parameters.

Abstract: A computer completes a data image analysis task. The computer receives a machine learning (ML) model trained for use with image data content characterized by first context. The computer receives an evaluation image dataset having evaluation image data content characterized by a second context. The computer receives a request to complete an image data analysis task for the evaluation image dataset using the ML model. The computer compares the contexts to determine whether the contexts are similar and whether the evaluation image dataset is compatible with the ML model. If the evaluation dataset is incompatible with the ML model, the computer uses the generative model to generate a ML model compatible synthetic image dataset based on the evaluation dataset. The computer applies the ML model to the synthetic image dataset to provide an answer for the data image analysis task; the computer delivers the answer to a user interface.

Abstract: Data associated with a region, acquired by a robot may be passed to a previously trained classifier. The classifier outputs a classification label L, and a confidence score C. Responsive to determining that the confidence score C is below a threshold T, the acquired data can be added to a training data set associated with the classifier, and the classifier retrained using the training data set which include at least information from the acquired data. Responsive to determining that the confidence score C is below the threshold T, at least one candidate region having characteristic similarity to the region can be identified. Responsive to determining that the confidence score C is not below the threshold T, at least one candidate region having a different characteristic from the region can be identified. The robot may be caused to acquire data associated with the candidate region.

Abstract: Data associated with a region, acquired by a robot may be passed to a previously trained classifier. The classifier outputs a classification label L, and a confidence score C. Responsive to determining that the confidence score C is below a threshold T, the acquired data can be added to a training data set associated with the classifier, and the classifier retrained using the training data set which include at least information from the acquired data. Responsive to determining that the confidence score C is below the threshold T, at least one candidate region having characteristic similarity to the region can be identified. Responsive to determining that the confidence score C is not below the threshold T, at least one candidate region having a different characteristic from the region can be identified. The robot may be caused to acquire data associated with the candidate region.

Abstract: Methods that support the analysis of digital images through the distributed and integrated processing of raster and vector digital data in a computer cluster environment, the set of methods including a particular strategy for distributing the processing of spatial context-aware operations over distributed datasets, as well as specific methods for the structuring of operations aimed at calculating spectral and topological properties of image objects, and for the resolution of spatial conflicts among objects.

Abstract: In a precision agriculture application, using an imagery task constraint corresponding to an image capture task, an autonomous vehicle (AV) is selected to perform the task. The AV is caused to perform the task according to the imagery task constraint, causing the AV to autonomously record image data of an area in a field of view of the AV. Image data responsive to the task is received from the AV. From analysis of the image data using a processor and a memory, a material distribution task and a corresponding distribution task constraint are generated. Using the distribution task constraint, a second AV to perform the material application task is selected. The second AV is caused to perform the material distribution task according to the distribution task constraint, causing the autonomous vehicle to autonomously trigger dispersal of a material in an area in a field of view of the autonomous vehicle.

Abstract: The present approach relates to providing image quality feedback to personnel (e.g., a technician) acquiring non-invasive images in real-time or near real-time. By way of example, the proposed approach may automatically assess the quality of images in real-time by evaluating the images for the presence or absence of non-conformities using processor-implemented, rule-based algorithms running partly or completely in parallel to one another. The proposed approach improves the image analysis pipeline by efficiently providing notification of and/or discarding low-quality or unsuitable images or exams after they are taken, such as in within seconds or minutes.

Abstract: The present approach relates to providing image quality feedback to personnel (e.g., a technician) acquiring non-invasive images in real-time or near real-time. By way of example, the proposed approach may automatically assess the quality of images in real-time by evaluating the images for the presence or absence of non-conformities using processor-implemented, rule-based algorithms running partly or completely in parallel to one another. The proposed approach improves the image analysis pipeline by efficiently providing notification of and/or discarding low-quality or unsuitable images or exams after they are taken, such as in within seconds or minutes.

Abstract: Embodiments described herein provide an image analytics platform that follows a microservice-based architecture. The platform provides a set of algorithms implemented as microservices to design knowledge-based models. The image analytics platform is deployed in the cloud for management and storage of images. In order to facilitate the design, composition and integration of the algorithms, a web application facilitates design of the knowledge models by the use of directed graphs. The web application allows physicians to design specific knowledge models and share with others; developers can easily add and test new image processing algorithms; and physicians can design and test different algorithms and evaluate selected results in a more efficient way.