Systems and methods are described to systems and methods for training a machine learning model to categorize each pixel of an input overhead image using received overhead images, and using a trained machine learning model to determine, for each pixel of input overhead images, to which land use or land cover mapping category each pixel of each overhead image belongs. The provided systems and methods may generate a map of a geographic area associated with the plurality of overhead images based on the plurality of overhead images and on the determined categories.

A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.

The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. A communication environment analysis method according to the present invention comprises the steps of: receiving satellite information and image information of a certain area; identifying area information of an object that is not contained in the image information, on the basis of the satellite information; determining characteristic information of the object; and analyzing a communication environment of the certain area on the basis of the characteristic information, wherein the object is one that causes signal attenuation due to at least one of signal scattering and signal absorption.

Methods for characterizing soil stiffness of an area. One example method includes receiving, with an electronic processor, a parameter corresponding to a soil type of the area; receiving, with the electronic processor, a plurality of thermal images of the area; determining, with the electronic processor, an apparent thermal inertia of the area based on the plurality of thermal images; determining, with the electronic processor, a soil gradation of the area based on the parameter; determining, with a machine learning algorithm executed by the electronic processor, an approximate soil stiffness of the area based on the apparent thermal inertia; and outputting, to a display communicatively coupled to the electronic processor, a representation of the approximate soil stiffness.

In accordance with various aspects of the present disclosure, methods and systems for fire detection are disclosed. In some embodiments, a method for fire detection includes: acquiring data related to a monitored area, wherein the data comprises image data related to the monitored area; determining, based on the acquired data, whether a first mode or a second mode is to be executed for fire detection, wherein the first mode comprises a first smoke detection, and the second mode comprises a first flame detection; and executing, by a hardware processor, at least one of the first mode or the second mode based on a result of the determination.

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.

Provided are an image processing apparatus, an image processing method, and an image processing program capable of achieving high accuracy in an index representing vegetation. An image processing apparatus (1) includes a normal map generation unit (12) and a reflection characteristic model generation unit (18). The normal map generation unit (12) obtains a normal vector characteristic based on a polarized image acquired. The reflection characteristic model generation unit (18) estimates a reflection characteristic model based on the normal vector characteristic obtained by the normal map generation unit (12).

Provided are an image fusion classification method and device. The method includes that: a three-dimensional weight matrix of a hyperspectral image is obtained by use of a Support Vector Machine (SVM) classifier (101); superpixel segmentation is performed on the hyperspectral image to obtain K superpixel images, K being a positive integer (102); the three-dimensional weight matrix is regularized by use of a superpixel-image-based segmentation method to obtain a regular matrix (103); and a class that a sample belongs to is determined according to the regular matrix (104).

Disclosed herein are systems, media, and methods for identifying and assessing topographical features such as vegetation density and surface water using satellite data, comprising: a digital processing device; a database of digital maps, each indicative of grazing area(s); and a computer program to create an application comprising: a software module receiving a first set of satellite data indicative of a first topographical feature of the land; a software module calculating a normalized index array of the first topographic feature for the time period using the first set of satellite data; a software module generating a custom map indicative of density of the first topographical feature; a software module combining the custom map with one of the digital maps to generate a combined map indicative of the density of the first topographical feature within the grazing areas; and a software module allowing a user to visualize or print the combined map.