A computer-implemented method for determining field boundaries and crop forecasts in each field is provided. The method includes deriving vegetation indices for each geo-spatial pixel of each image of multi-spectral imagery at a plurality of points in time, constructing minimum bounding boxes for each image according to the vegetation indices, and generating, based on a neural network analysis of each image and the minimum bounding boxes, a geo-spatial plot of crops including a predicted plot of future crop usage for an area including each field in the multi-spectral imagery.

A computer-implemented method for tracking includes obtaining an infrared image and a visible image from an imaging device supported by a carrier of an unmanned aerial vehicle (UAV), obtaining a combined image based on the infrared image and the visible image, identifying a target in the combined image, and generating control signals for tracking the identified target using the imaging device.

A method for shadow and cloud masking for remote sensing images of an agricultural field using multi-layer perceptrons includes electronically receiving an observed image, performing using at least one processor an image segmentation of the observed image to divide the observed image into a plurality of image segments or superpixels, extracting features for each of the image segments using the at least one processor, and determining by a cloud mask generation module executing on the at least one processor a classification for each of the image segments using the features extracted for each of the image segments, wherein the cloud mask generation module applies a classification model including an ensemble of multilayer perceptrons to generate a cloud mask for the observed image such that each pixel within the observed image has a corresponding classification.

Aspects of the invention include includes detecting, using a first machine learning model, a first well pad at a first location based at least in part on a first set of data comprising spectral data describing a gas emission from the first location. Detecting an environmental event within a threshold distance of the well pad. Determining a probability of damage to the first well pad from the environmental event.

In an approach for identifying a crop using satellite observation data and crop rotation data, a processor receives an aerial image of one or more agricultural fields in a pre-determined geographical region from an optical satellite. A processor selects a plurality of points from the aerial image. A processor clips the aerial image into one or more smaller images to generate one or more observed signatures. A processor determines there is a change in a spatial distribution of one or more crops. A processor generates an actual signature from the one or more observed signatures. A processor cross-correlates the actual signature against a plurality of historical reference signatures to determine a degree of similarity. A processor ranks one or more results of the cross-correlation. A processor identifies the one or more crops. A processor calculates a first estimate of an amount of the one or more crops.

Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a Neural Network, making an initial determination of cloud or ground by using the Neural Network, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.

A method and system of diagnosing a medical condition of a target area of a patient using a mobile device are provided. One or more magnetic field images of a target area of a patient are received. One or more hyperspectral images of the target area of the patient are received. For each of the one or more magnetic field images and the one or more hyperspectral images, a three-dimensional (3D) position of the mobile device is tracked with respect to the target are of the patient. A 3D image of the target area is generated based on the received one or more magnetic field images, one or more hyperspectral images, and the corresponding tracked 3D position of the phone with respect to each image. A medical condition of the target area is diagnosed or monitored based on the generated 3D image.

The evaluation information generating system of the present invention evaluates a ground area owned by a user, using satellite data observed by an artificial satellite, and includes a user information acquisition unit that acquires user information that is personal information of the user, a ground area information acquisition unit that acquires ground area information including a position of the ground area, a satellite data acquisition unit that acquires the satellite data, a situation detection unit that detects a situation of the ground area based on the satellite data, and an evaluation data generating unit that generates evaluation data of the user or the ground area based on the user information, ground area information, and situation of the ground area.

A satellite image sequence cloud region repairing method and apparatus. The method comprises: respectively identifying and expanding a cloud region and a shadow region under the cloud from multiple multispectral images of a target region, and generating multiple single-band images (S11); according to the similarities between the single-band images, calculating a reference image and a repair sequence that are required to repair the multispectral images (S12); determining repair regions of the multispectral images according to cloud mask images corresponding to the single-band images (S13); according to the repair sequence, replacing the pixel values of the repair regions of the multispectral images with the pixel values of a non-cloud region of the reference image to obtain replacement repair images (S14); recording the number value of each reference image corresponding to each multispectral image in the repair regions to obtain replacement reference mask images (S15); and performing cloud repairing on each replacement repair image and each replacement reference mask image in the target region by means of Poisson fusion (S16). The method improves the efficiency and accuracy of satellite image sequence cloud region repairing.

The present invention discloses a method and an apparatus for extracting mountain landscape buildings based on high-resolution remote sensing images. The method comprises: segmenting a remote sensing image, and extracting non-vegetation areas from the remote sensing image by using NDVI; segmenting the non-vegetation areas, and extracting building areas by using NDBI; segmenting the building areas again, and calculating a normalized difference build shadow index NSBI of each patch; calculating NSBI separator of each patch in the non-vegetation areas and setting a separator threshold, and extracting landscape building areas based on the threshold. In the present invention, by introducing a near infrared band in the remote sensing image spectrum, in which there is a significant difference between shadows and non-shadows, the influence of large shadow areas in mountainous shady areas in the remote sensing image on the result of extraction is reduced.