An imaging system comprises an imaging platform, a camera operatively connected to the imaging platform, and a controller operatively connected to control the imaging platform and the camera. The controller includes machine readable instructions configured to cause the controller to perform a method.

Train a machine learning model, using an image-based knowledge graph of tropical cyclone data, for implementing a surface field modeling architecture that produces images of at least surface wind fields and surface rainfall fields from images of at least tropical cyclone tracks and pressure intensities. Generate model images of a modeled surface wind field and a modeled surface rainfall field by providing images of at least a user-generated tropical cyclone track and pressure intensity to the trained machine learning model.

Systems, methods, and computer readable media for performing task assignment, completion, and management within a crowdsourced surveillance platform. A remote server may identify targets for image capture and may assign capture tasks to users based on travel plans of the user. Users may be assigned task to capture image of target locations lying along a travel path. The remote server may aggregate data related to the captured images and use it to update a map and log changes to the target location over time.

Systems, methods, and computer readable media for performing task assignment, completion, and management within a crowdsourced surveillance platform. A remote server may identify targets for image capture and may assign capture tasks to users based on travel plans of the user. Users may be assigned task to capture image of target locations lying along a travel path. The remote server may aggregate data related to the captured images and use it to update a map and log changes to the target location over time.

In an approach for determining the date of planting of a crop growing in an agricultural field, a processor receives an aerial image of one or more agricultural fields in a pre-determined geographical region. A processor selects a plurality of points from the aerial image. A processor calculates a Vegetation Index of one or more crops growing at the plurality of points selected. A processor compares the Vegetation Index calculated for the one or more crops growing at the plurality of points selected to the Vegetation Index known for a plurality of historical reference signatures. A processor generates an actual signature. A processor cross-correlates the actual signature against the plurality of historical reference signatures to measure a degree of similarity. A processor identifies the one or more crops growing in the one or more agricultural fields in the pre-determined geographical region from the cross-correlation.

An image processing device 100 includes a calculation unit 110 for calculating a solar radiation spectrum in a given area on the ground surface on the basis of an observed image of the area, a solar radiation spectrum component of the area, and the spectrum of a pure substance estimated in the area, and a conversion unit 120 for converting the observed image on the basis of the calculated solar radiation spectrum.

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.

A vessel monitoring service obtains transponder data of a vessel operating within a navigable area. In response to obtaining the transponder data of the vessel, the vessel monitoring service evaluates the transponder data and a plurality of travel segments recorded for a plurality of vessels to identify a travel segment to which the transponder data corresponds. The vessel monitoring service updates the travel segment using the transponder data and determine whether the vessel is engaged in an unauthorized activity. If so, the vessel monitoring service provides an indication that the vessel is engaged in the unauthorized activity.

Apparatus and associated methods relate to ranging object(s) nearby an aircraft using triangulation. A light projector mounted at a projector location on the aircraft projects pulses of polarized light onto the scene external to the aircraft. The projected pulses of polarized light are polarized in a first polarization state. A camera mounted at a camera location on the aircraft has a shutter synchronized to the projector output pulse and receives a portion of the projected pulses of polarized light reflected by the object(s) in the scene and polarized at a second polarization state orthogonal to the first polarization state. Location(s) and/or range(s) of the object(s) is calculated, based on the projector location, the camera location, and pixel location(s) upon which the portion of light is imaged.

Refined searching based on detected object configurations is provided by training a machine learning model to identify non-naturally occurring object configurations, acquiring images of an initial search area based on scanning it using a camera-equipped autonomous aerial vehicle operating in accordance with an initial automated flight plan defining the initial search area, analyzing the acquired images using the trained machine learning model and identifying that an object configuration is a non-naturally occurring object configuration, then based on identifying the non-naturally occurring object configuration, refining the initial automated flight plan to obtain a modified automated flight plan defining a different search area as compared to the initial search area, and initiating autonomous aerial scanning of the different search area in accordance with the modified automated flight plan.