In variants, the method for occlusion correction can include: determining a measurement depicting an occluded object of interest (OOI), optionally infilling the occluded portion of the object of interest within the measurement, and determining an attribute of the object of interest based on the infilled measurement.

Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align, with an image classifier model, an outline of a structure at a first instance of time to pixels within an image depicting the structure captured at a second instance of time; assess a degree of alignment between the outline and the pixels depicting the structure, so as to classify similarities between the structure depicted within the pixels of the image and the outline using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the outline and the pixels within the image are aligned.

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.

Systems and methods are disclosed for creating a mosaic image of two or more geo-referenced source images, the geo-referenced source images having the same orientation, based on a ground confidence map created by analyzing pixels of one or more of the geo-referenced source images, the ground confidence map having values and data indicative of particular geographic locations represented by the values, at least one of the values indicative of a statistical probability that the particular geographic locations represented by the values represents the ground; and using routes for steering mosaic cut lines based at least in part on the values indicative of the statistical probability that the particular geographic locations represented by the values represents the ground of the ground confidence map, such that the routes have an increased statistical probability of cutting through pixels representative of the ground versus routes not based on the ground confidence map.

An information processing apparatus includes at least one processor configured to execute, composition processing of combining a first image and another image, and image processing on a second image generated through the composition processing, using metadata of the first image and metadata of the second image.

Systems and methods are provided for pitch determination. An example method includes obtaining an image depicting a structure, the image being captured via a user device positioned proximate to the structure. The image is segmented to identify, at least, a roof facet of the structure. An eave vector and a rake vector which are associated with the roof facet are determined. A normal vector of the roof facet is calculated based on the eave vector and the rake vector, and compared to a vector indicating a vertical direction such as gravity. The angle made out by the normal and a gravity vector may be utilized to calculate the pitch of the roof facet.

A method of determining one or more shading conditions associated with a structure is provided. A method may include determining an azimuth of a reference roof edge relative to an orientation of a first image of a structure. The method may further include determining a relative azimuth of the reference roof edge from a lower hemisphere of a second, different image captured proximate the structure. In addition, the method may include determining one or more shading conditions associated with the structure based on the azimuth of the reference roof edge and the relative azimuth of the reference roof edge.

The present invention proposes a method for identifying the spatial-temporal distribution of the vehicle loads on a bridge based on the DenseNet. The method includes five steps: firstly, mounting a plurality of cameras in different positions of a bridge, acquiring images of the bridge from different directions, and outputting video images with time tags; secondly, acquiring multichannel characteristics of vehicles on the bridge by using DenseNet, including color characteristics, shape characteristics and position characteristics; thirdly, analyzing the data and characteristics of the vehicles from different cameras at a same moment to obtain vehicle distribution on the bridge at any time; fourthly, continuously monitoring the vehicle distribution in a time period to obtain a vehicle load situation on any section of the bridge; and finally, integrating the time and space distribution of the vehicles to obtain spatial-temporal distribution of the bridge.

Apparatuses, systems, methods, and medium are disclosed for precise geospatial structure geometry extraction from multi-view imagery, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: receive an image of a structure having an outline, the image having pixels with first pixel values depicting the structure and second pixel values outside of the structure depicting a background of a geographic area surrounding the structure, and image metadata including first geolocation data; and generate a synthetic shape image of the structure from the image using a machine learning algorithm, the synthetic shape image including pixels having pixel values forming a synthetic shape of the outline, the synthetic shape image having second geolocation data derived from the first geolocation data.

A computer vision-based system may assist in wire installation. The system may use computer vision techniques to assess the quality of service wire installations. The system may generate text descriptions of the image of service installation work, assessing the quality of the work to facilitate searching and summarization.