In an embodiment of the present invention, imagery of a geographic area is received from an image capture system. The imagery includes an obscurity impeding visibility. A geographic region within the geographic area is determined corresponding to a location of the obscurity. The image capture system is requested to capture new imagery of the geographic region at a time determined based on information including weather patterns. The new imagery of the geographic region is combined with the imagery of the geographic area to produce resulting imagery resolving the obscurity.

Implementations relate to crop yield prediction at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that capture a first geographic area and are acquired over a first predetermined time interval while the first geographic area includes a particular crop. A first plurality of other data points may also be obtained that influence a ground truth crop yield of the first geographic area after the first predetermined time interval. The first plurality of other data points may be grouped into temporal chunks corresponding temporally with respective images of the first temporal sequence. The first temporal sequence and the temporal chunks of the first plurality of other data points may be applied, e.g., iteratively, as input across a machine learning model to estimate a crop yield of the first geographic area at the end of the first predetermined time interval.

Systems and methods are disclosed herein for generating and/or providing a model, such as may be utilized for designing and/or presenting a proposal for a project for a structure or site. The disclosed embodiments can include identifying installation surface faces, installation surface face benefit metrics, obstructions, obstacles affecting placement of elements of the project, and for providing an educational experience, comprising general information and at least one structure-specific installation proposal. A selected plan can be implemented from within a proposal platform of the disclosed systems and methods.

An image processing apparatus comprising: a storage device storing weather data representing a state of an atmospheric air in at least one of a plurality of areas existing within a predetermined range; and a control device computing a color of a cloud about at least one area by using the weather data.

A method of removing raindrops from video images is provided. The method includes the steps of: training a raindrop image recognition model using a plurality raindrop training images labeled in a plurality of rainy-scene images; recognizing a plurality of raindrop images from a plurality of scene images in a video sequence using the raindrop image recognition model; and in response to a specific raindrop image in a current scene image satisfying a predetermined condition, replacing the specific raindrop image in the current scene image with an image region corresponding to the specific raindrop image in a specific scene image prior to the current scene image to generate an output scene image.

A method for image dehazing based on adaptively improved linear global atmospheric light of a dark channel. A haze image in haze weather is first obtained, a variation angle of atmospheric light of the image is obtain through calculating a slope of a connection line between a center and a center of gravity of a binary image of the image, a linear atmospheric light map that varies regularly along a variation direction of the atmospheric light is obtains, a dehazed image is solved through an atmospheric scattering model, and then a processed haze image taken in the haze weather is output.

A ground weather center may transmit information requests that carry at least one meteorological specific triggering command. An airborne system may translate the triggering command into detectable meteorological conditions and may arm the trigger(s) for specific weather sensors accordingly and downlink information upon the airborne system detects the triggering conditions. By using such a triggering command, the airborne system may be able transmit the same amount of valuable information with less bandwidth by reducing the number of redundant downlinked packets.

A weather forecasting system has a data processing system that receives weather data from one or more sources and processes such data in conjunction with a weather forecasting algorithm in order to forecast weather for one or more geographic regions. In this regard, the weather data is input into a machine learning algorithm, which applies learned weights and relationships to the inputs in order to calculate at least one score indicating a probability that precipitation or other weather event will occur in the future within a certain time period (e.g., within the next 1 hour or some other unit of time) in one or more geographic regions. For each such geographic region, the weather forecasting logic may also predict the extent to which rain or other precipitation, lightning, or other weather event will occur during the time period.

An information processing system is equipped with at least one vehicle and a server that can communicate with the at least one vehicle. The vehicle acquires an image obtained by imaging a landscape outside the vehicle, a humidity outside the vehicle, and an imaging position of the image. The vehicle or the server generates floating substance information on an atmospheric floating substance around the vehicle, based on the image and the humidity. The server stores at least one pair of the floating substance information and the imaging position corresponding thereto, and provides information to a client through the use of the at least one pair of the stored floating substance information and the stored imaging position corresponding thereto.

Satellite images from vast historical archives are analyzed to predict severe storms. We extract and summarize important visual storm evidence from satellite image sequences in a way similar to how meteorologists interpret these images. The method extracts and fits local cloud motions from image sequences to model the storm-related cloud patches. Image data of an entire year are adopted to train the model. The historical storm reports since the year 2000 are used as the ground-truth and statistical priors in the modeling process. Experiments demonstrate the usefulness and potential of the algorithm for producing improved storm forecasts. A preferred method applies cloud motion estimation in image sequences. This aspect of the invention is important because it extracts and models certain patterns of cloud motion, in addition to capturing the cloud displacement.