Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.

Abstract: In one aspect, there is provided a method performed by one or more data processing apparatus, the method including receiving a training dataset having multiple training examples, where each training example includes: (i) an image, and (ii) a segmentation defining a target region of the image that has been classified as including pixels in a target category. The method further includes determining a respective refined segmentation for each training example, including, for each training example, processing the target region of the image defined by the segmentation for the training example using a de-noising neural network to generate a network output that defines the refined segmentation for the training example.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating and executing biological convolutional neural network layers. One of the methods obtaining a network input; and processing the network input using a neural network to generate a network output, wherein the neural network is configured to perform operations comprising: generating a layer input to a convolutional neural network layer based on the network input; and generating a layer output of the convolutional neural network layer based on the layer input, comprising applying a convolutional kernel to the layer input, wherein the convolutional kernel corresponds to a specified neuron in a brain of a biological organism and values of parameters of the convolutional kernel are based on synaptic connectivity between the specified neuron and each of a plurality of other neurons in the brain of the biological organism.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a recurrent neural network that includes a brain emulation subnetwork. One of the methods includes obtaining an input sequence; and processing the input sequence using a recurrent neural network, wherein the recurrent neural network comprises a brain emulation subnetwork having a network architecture that has been determined according to a synaptic connectivity graph, the processing comprising: at a first time step, processing a first input element in the input sequence to generate a hidden state of the recurrent neural network; at each of a plurality of subsequent time steps, updating the hidden state of the recurrent neural network; and at each of one or more of the plurality of time steps, generating an output element for the time step based on the updated hidden state for the time step.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.