A method includes receiving a satellite image of an area and classifying each pixel in the satellite image as representing water, land or unknown using a model. For each of a plurality of possible water levels, a cost associated with the water level is determined, wherein determining the cost associated with a water level includes determining a number of pixels for which the model classification must change to be consistent with the water level and determining a difference between the water level and a water level determined for the area at a previous time point. The lowest cost water level is selected and used to reclassify at least one pixel.

Calibration of various sensors may be difficult without specialized software to process intrinsic and extrinsic information about the sensors. Certain types of input files, such as image files, may also lack certain information, like depth information, to effectively translate regions of interest between images taken from a different perspective. Landmarks can be used to establish points for associating regions of interest between images taken from a different perspective and provided as an overlay to verify sensor calibration.

An example embodiment of the present invention provides a method of assessing the condition of a pavement site, comprising: (a) acquiring aerial images of the site from above, for example by an unmanned aerial system (UAS); (b) using photogrammetry tools to generate an orthomosaic that represents the airport pavement surface; (c) using image analysis tools and machine learning methods to determine the location and extent of defects in the pavement; (c) producing an image representation of the site and the defects, where the location and extent of defects are discernible from the image; (d) using software application techniques to store and present defect data and other related information for client-side user access.

A method performed by a road identifying system of a vehicle for localization of the vehicle in a multi-level road system. The road identifying system determines with support from a positioning system, a position of the vehicle, identifies, based on the vehicle position with support from at least a first digital map, a multi-level road system in which the vehicle is positioned and obtains image data with support from one or more image capturing devices adapted to capture surroundings of the vehicle. Moreover, the road identifying system determines a road level at which the vehicle is positioned based on feeding the image data through a neural network trained to classify road level based on context of image content and identifies based on the determined road level, with support from the at least first digital map, a road and/or lane of the multi-level road system at which the vehicle is positioned.

Methods and system for identifying and evaluating on-street parking spots of an area are disclosed. The method comprises using at least one of satellite and aerial images and map data to identify vehicles, match them to street sections, compute on-street vehicle lanes, consolidate data from a plurality of images and identify parking lanes, as well as parking spots. The described system comprises a memory component, a processing component and an output component. Furthermore, a computer program executable by a computer and a non-transient computer-readable medium for identifying and evaluating on-street parking spots are described.

A valuation system to identify interior features of a property, identify exterior features related to interior features using aerial images, and to generate property valuations based on the identified features is provided. The valuation system identifies, using a computer vision module, interior features based on interior image data (e.g., photos) of a property, and further identifies exterior features associated with any of the interior features based on an aerial photo of the property. For example, trees and buildings (e.g., exterior features) adjacent to a window (e.g., an interior feature) can be identified by a computer vision module through the combination of interior and exterior image data. In other words, the identification of property features by the computer vision module can be enriched by correlating interior image data (e.g., photos, video walkthroughs) to exterior image data (e.g., satellite photos, aerial photos).

A system may be configured to collect geospatial features (in vector form) such that a software application is operable to edit an object represented by at least one vector. Some embodiments may: generate, via a trained machine learning model, a pixel map based on an aerial or satellite image; convert the pixel map into vector form; and store the vectors. This conversion may include a raster phase and a vector phase. A system may be configured to obtain another image, generate another pixel map based on the other image, convert the other pixel map into vector form, and compare the vectors to identify changes between the images. Some implementations may cause identification, based on a similarity with converted vectors, of a more trustworthy set of vectors for subsequent data source conflation.

Methods and systems in accordance with the present invention provide a continuum measure of visibility from a single image without prior knowledge of the camera system or the cameras in the system. This may be, for example, a score on the weather visibility quality of the image, ranging from good to poor, or a numeric score representing the weather visibility quality of the terrain in the image. This may be done without prior knowledge of the camera system, the camera that took the image, or the environment. It is also done with the single image without using multiple images, or reference images. The system derives a real-time continuum measure of visibility from a single daytime image, with unknown camera quality, system configuration, and environmental conditions.

Automated detection of features and/or parameters within an ocean environment using image data. In an embodiment, captured image data is received from ocean-facing camera(s) that are positioned to capture a region of an ocean environment. Feature(s) are identified within the captured image data, and parameter(s) are measured based on the identified feature(s). Then, when a request for data is received from a user system, the requested data is generated based on the parameter(s) and sent to the user system.

Embodiments of architecture, systems, and methods used to provide map data, sensor data, and asset signature data including location data, depth data, and positional data for a terrestrially mobile entity, location and positional data for pseudo-fixed assets and dynamic assets relative to the terrestrially mobile entity via a combination of aerial sensor data and terrestrial data. Other embodiments may be described and claimed.