Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying wildfire in satellite imagery. In some implementations, a server obtains a satellite image of a geographic region and a date corresponding to when the satellite image was generated. The server determines a number of pixels in the satellite image that are indicated as on fire. The server obtains satellite imagery of the geographic region from before the date. The server generates a statistical distribution from the satellite imagery. The server determines a likelihood that the satellite image illustrates fire based on a comparison of the determined number of pixels in the satellite image that are indicated as on fire to the generated statistical distribution. The server can compare the determined likelihood to a threshold. In response to comparing the determined likelihood to the threshold, the server provides an indication that the satellite image illustrates fire.

Abstract: Implementations are described herein for automatically generating multimodal geospatial workflows for accomplishing geospatial tasks. In various implementations, a natural language request may be processed based on generative model(s) such as LLM(s) to generate workflow output tokens that identify high-level actions for completing a geospatial task conveyed in the natural language request. First data indicative of the high-level actions may be processed using one or more of the generative models to generate dataset output tokens that identify responsive dataset(s) that likely contain data responsive to the geospatial task. Second data indicative of both the high-level actions and the responsive dataset(s) may be processed based on one or more of the generative models to generate data manipulation output tokens that identify data manipulation instructions for assembling data from the responsive dataset(s) into a response that fulfills the geospatial task.

Abstract: Methods, systems, and apparatus for using one or more machine learning (ML) models to mitigate effects of climate change by evaluating impact of wildfire mitigation actions (WMAs) for selective deployment of WMAs.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying wildfire in satellite imagery. In some implementations, a server obtains a satellite image of a geographic region and a date corresponding to when the satellite image was generated. The server determines a number of pixels in the satellite image that are indicated as on fire. The server obtains satellite imagery of the geographic region from before the date. The server generates a statistical distribution from the satellite imagery. The server determines a likelihood that the satellite image illustrates fire based on a comparison of the determined number of pixels in the satellite image that are indicated as on fire to the generated statistical distribution. The server can compare the determined likelihood to a threshold. In response to comparing the determined likelihood to the threshold, the server provides an indication that the satellite image illustrates fire.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying wildfire in satellite imagery. In some implementations, a server obtains a satellite image of a geographic region and a date corresponding to when the satellite image was generated. The server determines a number of pixels in the satellite image that are indicated as on fire. The server obtains satellite imagery of the geographic region from before the date. The server generates a statistical distribution from the satellite imagery. The server determines a likelihood that the satellite image illustrates fire based on a comparison of the determined number of pixels in the satellite image that are indicated as on fire to the generated statistical distribution. The server can compare the determined likelihood to a threshold. In response to comparing the determined likelihood to the threshold, the server provides an indication that the satellite image illustrates fire.

Abstract: Methods, systems, and apparatus for obtaining input features representative of a region of space, processing an input comprising the input features through the ML model to generate a prediction describing predicted features of the region of space, obtaining result features describing the region of space, determining a value of at least one evaluation metric that relates the predicted features and the result features, that at least one evaluation metric including one of a distance score, a pyramiding density error, and min-max intersection over union (IOU) score, and training the ML model responsive to the at least one evaluation metric. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

Abstract: Methods, systems, and apparatus for receiving, by a wildfire modeling system, region of interest (ROI) data representative of pixels that represent a geographical ROI, generating, by the wildfire modeling system, transition probabilities for each pixel in the ROI data, determining, by the wildfire modeling system, chained probabilities along each path in a set of paths within the ROI, adjusting, by the wildfire modeling system, chained probabilities based on a likelihood of ignition of a starting pixel represented in the ROI data, combining, by the fire modeling system, the adjusted chained probabilities to provide connectivity data that represents respective likelihood of spread of a wildfire from the starting pixel to each other pixel within the ROI, and displaying a connectivity map that graphically represents connectivity data of each pixel within the ROI.

Abstract: Methods, systems, and apparatus for obtaining input features representative of a region of space, processing an input comprising the input features through the ML model to generate a prediction describing predicted features of the region of space, obtaining result features describing the region of space, determining a value of at least one evaluation metric that relates the predicted features and the result features, that at least one evaluation metric including one of a distance score, a pyramiding density error, and min-max intersection over union (IOU) score, and training the ML model responsive to the at least one evaluation metric. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model to perform a machine learning task. In one aspect, a method includes: obtaining a set of training examples; obtaining, for each training example, a respective metadata label that characterizes the training example; and training the machine learning model over a sequence of training stages, including, at each training stage: identifying a selection criterion corresponding to the current training stage that defines a criterion for selecting training examples based on the metadata labels of the training examples; selecting a proper subset of the set training examples as training data for the current training stage in accordance with the selection criterion for the current training stage; and updating the machine learning model by training the machine learning model on the training data for the current training stage.

Abstract: Methods, systems, and apparatus for receiving a request for a risk assessment for a parcel, receiving a set of images for the parcel, the set of images including two or more images, each image having an image scale and an image resolution that is different from other images in the set of images, providing a first-level feature embedding and a second-level feature embedding, the first-level feature embedding being provided by processing a first-level image through a first-level machine learning (ML) model, and the second-level feature embedding being provided by processing a second-level image through a second-level ML model, determining a risk assessment at least partially by processing each of the first-level feature embedding and a second-level feature embedding through a fusion network, and providing a representation of the risk assessment for display.

Abstract: Methods, systems, and apparatus for obtaining a first plurality of data elements, each data element representing a fire-related metric of a geographic region, determining, using at least a subset of the first data elements, one or more values representing one or more derived fire-related metrics, associating the one or more values with the first data elements, obtaining a second plurality of data elements, each data element representing a fire-related metric of the geographic region, and training a machine learning (ML) model using at least a subset of the first plurality of data elements, at least a subset of the second plurality of data elements, and values associated with the subset of the first plurality of data elements to provide a trained ML model.

Abstract: Methods, systems, and apparatus for providing a ML model for inference, the ML model having been trained using a first set of training data to provide predictions associated with an adverse event, after training of the ML model, receiving data from one or more data sources, the data representative of characteristics relevant to predictions associated with the adverse event, providing a second set of training data, determining, by a trigger module, a trigger decision based on a set of signals at least partially determined from the second set of training data, the trigger decision indicating whether the ML model is to be one of updated and retrained based on the second set of training data, and selectively executing one of updating and retraining of the ML model using at least a portion of the second set of training data in response to the trigger decision.