A method for efficient image registration between two images in the presence of inlier-poor domains includes receiving a set of candidate correspondences between the two images. An approximate homography between the two images is generated based upon a first correspondence in the correspondences. The set of candidate correspondences is filtered to identify inlier correspondences based upon the approximate homography. A candidate homography is computed based upon the inlier correspondences. The candidate homography can be selected as a final homography between the two images based upon a support of the candidate homography against the set of candidate correspondences. An image registration is performed between the two images based upon the candidate homography being selected as the final homography.

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.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a synthesized signal. In some implementations, a computer-implemented system obtains generator input data including at least an input signal having one or more first characteristics, processes the generator input data to generate output data including a synthesized signal having one or more second characteristics using a generator neural network, and outputs the synthesized signal to a device. The generator neural network is trained, based on a plurality of training examples, with a discriminator neural network. The discriminator neural network is configured to process discriminator input data that combines a discriminator input signal having the one or more second characteristics with at least a portion of generator input data to generate a prediction of whether the discriminator input signal is a real signal provided in one of the plurality of training examples or a synthesized signal outputted by the generator neural network.

Process and system of analysis and management for agricultural production, which includes the stages of loading historical data, measuring and surveying present data, predicting meteorological and hydrological events, generating an agricultural handing protocol, and transmitting the agricultural handling protocol.

Process and system of analysis and management for agricultural production, which includes the stages of loading historical data, measuring and surveying present data, predicting meteorological and hydrological events, generating an agricultural handing protocol, and transmitting the agricultural handling protocol.

The subject matter discloses a method of asset change detection using images, the method comprising steps executed by processing circuitry, the steps comprising: receiving at least one image of an asset captured by an image capturing device; receiving at least one attribute of a task of detecting a change in the asset using the received at least one image, at least one of the at least one attribute being one of the group consisting of: an attribute measured by a sensor, an attribute extracted from a website, an attribute retrieved from a database, an attribute input by a user, and an attribute encoded in computer code; selecting a reference image among a plurality of reference images of the asset according to at least one criterion based on the received at least one attribute of the task of detecting the change in the asset; computing an asset-difference pixel map, using the selected reference image and the image captured by the image capturing device; and detecting the change in the asset, using the computed asset-difference map.

A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.

A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.

An unmanned aerial vehicle manages storage of data and transfer between other connected devices. The unmanned aerial vehicle captures sensor data from sensors on the unmanned aerial vehicle. The unmanned aerial vehicle transfers the captured sensor data from the unmanned aerial vehicle to a remote controller via a wireless interface. The captured data may be transferred via a TCP link, a UDP link, or a combination thereof. If a loss of link is detected, the captured sensor data is stored to a buffer and a battery level of the unmanned aerial vehicle and a flight status of the unmanned aerial vehicle is monitored. The stored sensor data is transferred from the buffer to a non-volatile storage responsive to the battery level dropping below a predefined threshold or detecting that the unmanned aerial vehicle is stationary and a shutdown may be imminent.

An unmanned aerial vehicle manages storage of data and transfer between other connected devices. The unmanned aerial vehicle captures sensor data from sensors on the unmanned aerial vehicle. The unmanned aerial vehicle transfers the captured sensor data from the unmanned aerial vehicle to a remote controller via a wireless interface. The captured data may be transferred via a TCP link, a UDP link, or a combination thereof. If a loss of link is detected, the captured sensor data is stored to a buffer and a battery level of the unmanned aerial vehicle and a flight status of the unmanned aerial vehicle is monitored. The stored sensor data is transferred from the buffer to a non-volatile storage responsive to the battery level dropping below a predefined threshold or detecting that the unmanned aerial vehicle is stationary and a shutdown may be imminent.