A method for determining a change in an asset and/or a characterization of an asset is provided. In an embodiment, the method can include receiving first data characterizing a target site including one or more assets. The method can also include generating a three-dimensional model of the target site based on the first data. The method can further include registering the first data with the three-dimensional model. The method can also include generating at least one three-dimensional projection onto at least one asset of the one or more assets included in the first data. The method can further include determining second data characterizing the at least one asset based on the at least on three-dimensional projection and providing the second data. In some embodiments, the method can be performed by systems or stored as instructions on computer readable mediums described herein.

A system for modeling a three-dimensional structure utilizing two-dimensional segments comprising a memory and a processor in communication with the memory. The processor extracts a plurality of two-dimensional segments corresponding to the three-dimensional structure from a plurality of images indicative of different views of the three-dimensional structure. The processor determines a plurality of three-dimensional candidate segments based on the extracted plurality of two-dimensional segments and adds the plurality of three-dimensional candidate segments to a three-dimensional segment cloud. The processor transforms the three-dimensional segment cloud into a wireframe indicative of the three-dimensional structure by performing a wireframe extraction process on the three-dimensional segment cloud.

A system and method for automatically (without human intervention) identifying a material in an image that comprises a building material for buildings in the image. Building side polygons which may be used to identify building sides in off-nadir imagery are generated. Off-nadir, multispectral images, building footprint data and elevation data for a geographic area are taken as input. Building heights for buildings in the geographic area are determined by clipping the elevation data using the building footprint data and then calculating building heights. A candidate set of polygons representing visible side faces of each building in the images is created from the known building heights, and based on the viewpoint direction, using vector analysis. After culling occluded polygons and polygons too small for analysis, the polygons are associated with a building footprint. Building materials for each building having visible polygons can then be identified.

Systems and methods are provided for impervious surface mapping of a target geographic area. The impervious surface mapping utilizes four-band imagery data and light detection and ranging (LIDAR) data collected from the target geographic area. The identified impervious surfaces can be attributed to parcels within the target geographic area for purposes of generating bills for storm water runoff to parcel owners.

A system, apparatus, computer program product, and method use a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the FFH and/or FFE of the building on the parcel, it is possible to use that information while performing a flood risk assessment to a property without requiring a personal inspection of the parcel by a human.

A method implemented with instructions executed by a processor includes receiving a digital image of an interior space. At least one detected object is identified within the digital image. Dimensions of the detected object are determined. Image segmentation is applied to the digital image to produce a segmented image. Edges are detected in the segmented image to produce a combined output image. Geometric transformation, field of view and depth correction are applied to the combined output image to correct for image distortion to produce a geometrically transformed digital image. Dimensions are applied to the geometrically transformed digital image at least partially based on the dimensions of the detected object to produce a dimensionalized floorplan.

The present invention belongs to the technical field of 3D reconstruction in the field of computer vision, and provides a dataset generation method for self-supervised learning scene point cloud completion based on panoramas. Pairs of incomplete point cloud and target point cloud with RGB information and normal information can be generated by taking RGB panoramas, depth panoramas and normal panoramas in the same view as input for constructing a self-supervised learning dataset for training of the scene point cloud completion network. The key points of the present invention are occlusion prediction and equirectangular projection based on view conversion, and processing of the stripe problem and point-to-point occlusion problem during conversion. The method of the present invention includes simplification of the collection mode of the point cloud data in a real scene; occlusion prediction idea of view conversion; and design of view selection strategy.

An Artificial Intelligence (AI) roof deterioration analysis system that tracks changes in roofs over time by evaluating a series of features in high-resolution images.

The technologies described herein are generally directed to the autonomous onsite remediation of adverse conditions for network infrastructure in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include detecting a condition of a component of network equipment at a site that has a likelihood of indicating a defined adverse event that has at least a threshold likelihood of occurring. The method can further include, in response to detecting the condition, facilitating generating a graphical image of the component. Further, the method can include, based on information determined from the graphical image, remediating the condition.

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.