A method of determining a roadway map includes receiving an image from above a roadway. The method further includes generating a skeletonized map based on the received image, wherein the skeletonized map comprises a plurality of roads. The method includes identifying intersections based on joining of multiple roads of the plurality of roads in the skeletonized map. The method includes partitioning the skeletonized map based on the identified intersections, wherein partitioning the skeletonized map defines a roadway data set and an intersection data set. The method includes analyzing the roadway data set to determine a number of lanes in each roadway of the plurality of roads. The method further includes analyzing the intersection data set to lane connections in the identified intersections. The method further includes merging results of the analyzed road data set and the analyzed intersection data set to generate the roadway map.

There is provided a digital map product comprising data informative of a location of a zone in a surface area including a subsurface utility infrastructure (SUI), the digital map product being derivative of a method comprising: receiving a digital image of the surface area; identifying a plurality of surface features; for each of the plurality of surface features: calculating an indication of utility location (IUL) from the respective surface feature and location, wherein the IUL is one of: a location of a point of the SUI, a location of a zone of the SUI, a location of a zone from which the SUI is absent, thereby giving rise of a plurality of IULs; and defining a location of a zone including the SUI in accordance with, at least, the plurality of IULs wherein at least one surface feature of the plurality of surface features is a public works surface marking.

A system and method for determining a viewpoint of a traffic camera includes obtaining images of a real road captured by the traffic camera, segmenting a road surface from the captured images to generate a mask of the real road, generating a 3D model of a simulated road corresponding to the real road, from geographical data of the real road, adding a simulated camera corresponding to the traffic camera to a location in the 3D model that is corresponding to a location of the traffic camera in the real road, generating a plurality of simulated images of the simulated road using the 3D model, each corresponding to a set of viewpoint parameters of the simulated traffic camera, selecting the simulated image that provides the best fit between the simulated image and the mask, and generating mapping between pixel locations in the captured images and locations on the real road.

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.

Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align, with an image classifier model, an outline of a structure at a first instance of time to pixels within an image depicting the structure captured at a second instance of time; assess a degree of alignment between the outline and the pixels depicting the structure, so as to classify similarities between the structure depicted within the pixels of the image and the outline using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the outline and the pixels within the image are aligned.

A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.

The present disclosure discloses a remote sensing-based extraction method for a type of a river channel, including: obtaining multi-source remote sensing data of a target area including a river channel; preprocessing the multi-source remote sensing data, and obtaining corresponding reflectance data; according to the reflectance data, analyzing a water index and a vegetation index of the target area; and according to the water index and the vegetation index, constructing first preset conditions for determining whether there is water in the river channel and second preset conditions for determining whether the river channel is a non-dry river channel to determine the type of the river channel. Types of river channels can be divided into four types: non-dry river channels, seasonal dry river channels, temporary water channels, and dry river channels.

Computer-based method and system for urban planning of a facility are described herein. The method and system facilitate a user, without any hard knowledge of geographic information system (GIS) to perform quick geo-spatial analysis with a fully automated, single step, and single input process including automatically determining one or more criteria corresponding to the facility based on the facility type defined by the user, automatically performing data analysis on a plurality of data-sets, automatically determining and presenting at least one suitable site within the geographic area for the facility based on the data analysis.

The present disclosure provides a method, apparatus, medium and device for extracting a river drying-up region and frequency, and belongs to the technical field of remote sensing. The method for extracting a river drying-up region and frequency can be applied to efficiently acquiring river drying-up information for a long time within the large spatial region. With the inverse normalized difference water index (iNDWI) and the maximum value composite (MVC) method for the remote sensing images, the present disclosure omits the troublesome step of separately extracting a river range for each image in the conventional method. In addition, the present disclosure quickly obtains the drying-up frequency by counting the total number of available images and the number of non-water images at the same pixel position, and yields a greater efficiency for extracting the river drying-up region and frequency.

A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.