A system includes an imaging device configured to capture image data of a monitored area and a computing device having at least one processor. The computing device processor is configured to receive first image data from the imaging device and second image data from a satellite imagery system, where the second image data relates to the monitored area. The processor is also configured to determine a first set of points of interest in the first image data and a second set of points of interest in the second image data, wherein members of each set of points of interest are represented by a respective vector. The processor is further configured to generate a homography matrix based on the two vectors and determine, using the homography matrix, latitude and longitude coordinates of at least one object represented in the first image data but not the second image data.

A road surface inspection apparatus (10) includes an image acquisition unit (110), a damage detection unit (120), and an information output unit (130). The image acquisition unit (110) acquires an image in which a road is captured. The damage detection unit (120) sets a target region in the image in image processing for detecting damage to a road, based on an attribute of the road captured in the image, and performs the image processing on the set target region. The information output unit (130) outputs position determination information allowing determination of a position of a road damage to which is detected by the image processing.

Computer-implemented methods and systems for image analysis of multiband images of geographic regions are described, including a method by one or more computer executing executable instructions stored in one or more non-transitory, tangible, computer readable media, the method comprising: receiving one or more multiband image of a geographic region, the one or more multiband image having pixels; generating a grey level co-occurrence matrix for the pixels in the one or more multiband image; generating a surface index for the one or more multiband image containing information indicative of a surface type represented by one or more of the pixels in the one or more multiband image; and classifying the pixels of the one or more multiband image into one of a group of predefined land cover classes, based on the surface index in combination with the grey level co-occurrence matrix.

A computer-implemented method for selecting aerial images for image processing to identify Energy Infrastructure (EI) features is provided. The method includes performing image processing on aerial images of a portion of global terrain captured at different times to determine differences in terrain content the captured images. Aerial images are selected for further image processing according to identified differences in terrain content. The selected images are imaged processed via an EI feature recognition type to identify EI features within the images.

An electronic device displays an application user interface that includes a representation of a field of view of one or more cameras. The representation of the field of view is updated over time based on changes to current visual data detected by the one or more cameras, and the field of view includes a physical object in a three-dimensional space. While the device is a first distance from the physical object, the device displays a representation of a measurement that corresponds to the physical object. After the device has moved to a second distance from the physical object, the device displays, a second representation of the measurement that includes one or more scale markers along at least a portion of the second representation of the measurement that were not displayed with the first representation of the measurement.

Embodiments of the disclosure provide systems and methods for detecting road markings from a laser intensity image. An exemplary method may include receiving, by a communication interface, the laser intensity image acquired by a sensor. The method may also include segmenting the laser intensity image into a plurality of road segments, and dividing a road segment into a plurality of sub-images. The method may further include generating a road marking image corresponding to each of the sub-images based on a semantic segmentation method using a learning model and generating an overall road marking image for the road segment by piecing together the road marking images corresponding to the sub-images of the road segment.

Technology is provided for identifying concrete and/or asphalt (or other materials) in a multispectral satellite image that has multiple bands including a first set of bands from a first sensor and a second set of bands from a second sensor. The second sensor is at a different position on a focal plane as compared to the first sensor so that a single location depicted in the multispectral image will have been sensed at different times by the first sensor and the second sensor. The system identifies moving vehicles in the multispectral image and subsequently identifies sample pixels in the multispectral image that are near the moving vehicles. These pixels are high confidence samples of roads made of concrete and/or asphalt. Additional pixels are identified in the multispectral image having spectral characteristics that are within a threshold of spectral characteristics of the sample pixels. These additional pixels also depict concrete and/or asphalt.

In various examples, operations include obtaining, from a machine learning model, feature classifications that correspond to features of objects depicted in images of a geographical area in which the images are provided to the machine learning model. The operations may also include annotating the images with three-dimensional representations that are based on the obtained feature classifications. Further, the operations may include generating map data corresponding to the geographical area based on the annotated images.

A system and method for identification of a landing site for aerial vehicles are provided. In one embodiment, the method includes processing image data pertaining to a potential site area. The method further includes identifying existing building infrastructure and defining boundaries of the identified existing building infrastructure in the potential site area, based on the processed image data. The method also includes identifying existing road infrastructure adjacent to the boundaries of the identified existing building infrastructure and defining boundaries of the identified existing road infrastructure, based on the processed image data. The method further includes calculating a feasibility score of the existing building infrastructure by analyzing the identified existing building infrastructure and the identified existing road infrastructure. The method further includes outputting the identified existing building infrastructure as a potential landing site for landing aerial vehicles when the calculated feasibility score is above a predetermined threshold score.

An image recognition method and an unmanned aerial vehicle system are provided. A training image marked with a specified range is received, and a plurality of features are extracted from the training image through a basic model to obtain a feature map. Next, a frame selection is performed on each point on the feature map to obtain a plurality of initial detection frames, and a plurality of candidate regions are selected in the initial detection frames based on the specified range. Thereafter, the obtained candidate regions are classified to obtain a target block, feature data corresponding to the target block is extracted from the feature map, and a parameter of the basic model is adjusted based on the extracted feature data. In the disclosure, a higher-resolution image is achieved, time flexibility is provided, and accuracy of image recognition is thereby improved.