Described are approaches for monitoring construction of a structure. In an embodiment, sensor data (e.g., imaging data, LIDAR, infrared, etc.) of a construction site is obtained. The sensor data is analyzed and objects related to the construction site are identified. The objects are mapped to corresponding objects of a builder’s design plans of the construction site, and the location of components are checked for accuracy. When a discrepancy above a threshold is detected, a report indicating such errors is generated and appropriate entities are provided the report.

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.

Provided are a damage determination information system, a server device, a terminal apparatus, and a program that can realize at least one of the improvement of the convincing feeling for a damage determination result, the reduction of an investigation cost, or the decrease of fluctuation of the damage determination results by a plurality of investigators. A provisional damage determination result is acquired, and a server device extracts a plurality of disaster images related to the provisional damage determination result from a disaster image database (15) and displays the extracted disaster image on a display device of a terminal apparatus. Input of an instruction for settling the damage determination result is received from the terminal apparatus that displays the disaster image, and the settled damage determination result is registered in a damage investigation result database (17).

An electronic device is provided. The electronic device includes a camera module, a communication circuit, a memory, and a processor, wherein the processor may execute a first application using the camera module, obtain a first image through the camera module while the first application operates, recognize an object of a specified type in the first image, obtain location information of where the first image is obtained, determine a first virtual point corresponding to the location information on a three-dimensional (3D) virtual map, obtain a second image corresponding to the first image at the first virtual point, and update data of the 3D virtual map on the object based on a comparison between the first image and the second image.

A system determines spatial locations of pixels of an image. The system includes a processor configured to: receive location data from devices located within a hotspot; generate a density map for the hotspot including density pixels associated with spatial locations defined by the location data, each density pixel having a value indicating an amount of location data received from an associated spatial location; match the density pixels of the density map to at least a portion of the pixels of the image; and determine spatial locations of the at least a portion of the pixels of the image based on the spatial locations of the matching density pixels of the density map. In some embodiments, the image and density map are converted to edge maps, and a convolution is applied to the edge maps to match the density map to the pixels of the image.

A method for measuring an antenna downtilt angle based on a multi-scale deep semantic segmentation network is disclosed, including: collecting base station antenna data by using an unmanned aerial vehicle, and labeling an acquired antenna image with a labeling tool to make a data set; calling the data set for training and debugging a model; recognizing and detecting a target antenna, performing semantic segmentation on an output image, finally obtaining a target image finally segmented, and calculating a downtilt angle of the target image. The method is highly applicable, cost-effective, and safe.

Disclosed herein is a method for an image analysis server to detect a change to a structure by using a drone. The method for an image analysis server to detect a change to a structure by using a drone includes: receiving images of a specific inspection target structure taken at different time points by a drone; detecting the difference between an image taken at a first time point and an image taken at a second time point based on the received images; and detecting a change to the inspection target structure via the detected difference, and generating a risk signal and then transmitting it to an administrator terminal.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a driving scenario machine learning network and providing a simulated driving environment. One of the operations is performed by receiving video data that includes multiple video frames depicting an aerial view of vehicles moving about an area. The video data is processed and driving scenario data is generated which includes information about the dynamic objects identified in the video. A machine learning network is trained using the generated driving scenario data. A 3-dimensional simulated environment is provided which is configured to allow an autonomous vehicle to interact with one or more of the dynamic objects.

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.

A trusted image recognition system and method are disclosed. A target vehicle includes a target vehicle controller configured to generate a first cryptographic key having a first sequence of bits, and a mechanical key display device configured to display a mechanical representation of the first sequence of bits. A host vehicle includes an image sensor configured to capture an image of the mechanical representation of the first sequence of bits, and a host vehicle controller configured to: recognize the mechanical representation of the first sequence of bits in the image; retrieve the first sequence of bits of the first cryptographic key from the image; generate a second cryptographic key having a second sequence of bits; compare the first sequence of bits to the second sequence of bits; and, responsive to the first sequence of bits matching the second sequence of bits, identify the target vehicle as an authenticated target vehicle.