An apparatus for visual navigation of a UAV includes a geo-fiducial mat and a plurality of geo-fiducials. The geo-fiducial mat includes a landing pad region that provides a location for aligning with a landing pad of a UAV. The geo-fiducials each includes a two-dimensional (2D) pattern that visually conveys a code. The 2D pattern has a shape from which a visual navigation system of the UAV can visually triangulate a position of the UAV.

A method for obtaining a bid for repairing a roof of a building structure using a portable computing device includes obtaining information about the building structure using aerial photographs and exterior photographs of the building structure to define a work site and selecting at least a portion of the work site to define a scope of repair within the work site. The method also includes obtaining an event report related to the scope of repair and combining the scope of repair and the event report to define warranty parameters. The method also includes communicating the scope of repair and the warranty parameters to a contractor bidding interface. The method also includes receiving the bids from at least one bidding contractor and selecting a desired contractor from the at least one bidding contractor, via the contractor bidding interface and selecting payment terms for paying the desired contractor.

Various aspects described herein relate to a location-based and population-based health metric processes. In one example, a computer-implemented method for generating one or more predicted health metrics for a location includes receiving a request to assess the one or more health metrics associated with the location, and identifying at least one current or future built, social, or natural environment parameter associated with the location. The method may further include calculating one or more predicted health metrics associated with the location based upon at least one of the current or future built, social, or natural environment parameters associated with the location, monetizing the healthcare costs of the predicted health metrics and displaying the one or more predicted health metrics and costs.

Computer vision systems and methods for determining structure features from point cloud data using neural networks are provided. The system obtains point cloud data of a structure or a property parcel having a structure present therein from a database. The system can preprocess the obtained point cloud data to generate another point cloud or 3D representation derived from the point cloud data by spatial cropping and/or transformation, down sampling, up sampling, and filtering. The system can also preprocess point features to generate and/or obtain any new features thereof. Then, the system extracts a structure and/or feature of the structure from the point cloud data utilizing one or more neural networks. The system determines at least one attribute of the extracted structure and/or feature of the structure utilizing the one or more neural networks.

Systems and methods are disclosed for automatically detecting a design element in a design document. One method comprises receiving a design document and generating an enhanced design document based on the received design document. The enhanced design document may be generated by augmenting additional information to the design document using machine learning techniques. In response to receiving a user input, one or more design elements in the enhanced design document may be determined, and additional information associated with the determined one or more design elements may be displayed to the user.

A computing machine receives a real synthetic aperture radar (SAR) image including one or more targets. The real SAR image is one of a plurality of real SAR images in a training set. The computing machine generates, for the real SAR image, a model-based target shadow background (TSB) image using a three-dimensional (3D) model of the target. The computing machine generates, for the real SAR image and using an auto-encoder engine, an auto-encoder-generated TSB image using an artificial neural network (ANN). The computing machine computes, using a discriminator engine, an image difference between the auto-encoder-generated TSB image and the model-based TSB image. The computing machine adjusts weights in the auto-encoder engine based on the computed image difference.

Aspects of the subject disclosure may include, for example, obtaining a first plurality of inputs that identify a plurality of geographical locations and a plurality of infrastructure located at the plurality of geographical locations, classifying each of the plurality of geographical locations in accordance with the first plurality of inputs to obtain a plurality of classes, obtaining a second plurality of inputs that identify costs, revenue, profits, or any combination thereof, associated with the plurality of infrastructure, processing the second plurality of inputs in conjunction with the plurality of classes to identify a first plurality of locations included in the plurality of geographical locations to decommission infrastructure included in the plurality of infrastructure, and presenting the first plurality of locations via a device. Other embodiments are disclosed.

A system and method for automatically documenting a condition of a property and generating a request for return of a security deposit is described. In one embodiment, a method for automatically documenting a condition of a physical space includes determining an initial condition of one or more inspected elements in a physical space. The method also includes obtaining a current condition of the one or more inspected elements in the physical space. The method includes determining changes between the initial condition and the current condition for each of the one or more inspected elements. The method further includes generating a report documenting the changes of the one or more inspected elements in the physical space.

An unmanned aerial vehicle (UAV) may couple to a sensor payload device that includes cameras, radar, lidar, and/or other sensors. The UAV, coupled to the sensor payload device, may fly within the airspace of and/or around a property and capture images and/or sensor measurements of the property. The images and sensor measurements may be certified so that they may be verified as unaltered by viewers. A 3D representation of the property may be generated, and defects in the property may be detected by comparing the 3D representation to media depicting property defects. A report identifying the defects may be generated.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for identifying phases of electrical grid wires are disclosed. A method includes identifying, within an image of a utility pole, a cross-arm supporting multiple wires; identifying a cardinal orientation of the cross-arm based on characteristics of the image; and determining, based on the cardinal orientation of the cross-arm, an electrical phase for each of the wires supported by the cross-arm. Identifying the cardinal orientation of the cross-arm includes: determining an orientation of the cross-arm relative to an axis of a field of view of the image; determining a cardinal orientation of the axis of the field of view; and estimating the cardinal orientation of the cross-arm based on an angular difference between the orientation of the cross-arm relative to the axis of a field of view and the cardinal orientation of the axis of the field of view.