Abstract: A method and system for generating geographic routes using topographic data that has been transformed into vector tiles to enable large spatial networks to be reduced from large scale to small scale, wherein the networks are reduced based on the lowest cost path(s) across each vector tile (for example, shortest, fastest and/or safest route across the represented geographic area) whilst ensuring connectivity between adjacent vector tiles is maintained. Complex and vast networks can be simplified to the cost of moving across a single vector tile from edge to edge, with each zoom level comprising a plurality of vector tiles with varying levels of detail concerning the paths across each vector tile. Routes between two locations, particularly those covering large distances, can then be quickly and efficiently determined using the processed vector tiles.

Abstract: Computer-implemented methods and systems for identifying topographic features that optimises and subsequently implements a machine learning model to automatically classify and extract topographic features from a set of target imagery are described herein. In particular, the optimised machine learning model creates heat maps from the target imagery corresponding to each class of feature, wherein the intensity of each pixel indicates whether a certain type of feature is present. The resulting heat maps are then processed to transform each pixel, specifically those identifying a topographic feature, into a geospatial vector.

Abstract: A computer-implemented method and system for determining the geographical location of a user based on the characteristics of intersecting features, such as a road intersection. Specifically, the geometry of each intersection in a geographical area is used to derive a unique fingerprint for each individual intersection, the fingerprint comprising information relating to the geometry, the geographical location of the intersection, and other characteristics. These fingerprints can then be stored locally to a device, for example, a mobile phone, a tablet, a wearable computing device, an in-vehicle infotainment (IVI) system and the like. To determine the geographical location of the device, the geometry of a nearby intersection may be analysed by some means and compared to the stored set of unique fingerprints to identify the intersection and its associated location.

Abstract: Aspects described herein provide a computer-implemented method and system for generating topographic map data at different scales. More specifically, the topographic map data is reduced from large scale to small scale, wherein the scale of the vector features is reduced according to their geometry, feature type and attributes. In order to quickly and easily output digital maps at different scales so that a user may quickly zoom in and out of a map, different zoom levels are produced for different scales, each zoom level containing a variable amount of detail according to its scale, with larger scale zoom levels generally containing more detail than small scale zoom levels. Each zoom level is made up of one or more vector tiles representative of a geographic area, wherein the vector tiles may contain one or more vector features that are representative of objects within that geographic area, and other information related to that geographical area.

Abstract: K-NN spatial queries may be performed, and the results of one K-NN query are re-used to perform a plurality of K-NN queries on a set of query points. More specifically, a K-NN query is performed on a pre-defined location, for example, an object point, to find the nearest K data objects for this pre-defined location, the results of this K-NN query being used to perform further K-NN queries on the query points near the pre-defined location. In doing so, the efficiency of large scale K-NN spatial queries is improved by limiting the spatial search range of the K-NN queries to be performed and re-using pre-computed K-NN data.

Abstract: Embodiments described herein provide a computer-implemented method of creating a digest of a document. The document to be processed and analysed may be a physical document, or it may already be in a digital form. In the case of starting from a physical document, the document is first scanned, so as to obtain an image of the document. The digital document is then processed using an algorithm or function to obtain one or more datasets comprising a plurality of position independent values. Each of the datasets may correspond to a different line of text or field of text within the document. The one or more datasets are then encoded, the encoded data being used to generate a digest associated with the document, and wherein the digest comprises a plurality of short hashes corresponding to each dataset. The generated digest can then be used to print a digital signature on the document, which can be used to later verify the authenticity of the document or a copy thereof.

Abstract: Aspects described herein provide a computer implemented radio frequency propagation simulation tool to allow the simulation of radio frequency propagation across a topographic area which has been very finely mapped in three dimensions to include possible obstructions to high frequency radio waves. A computer implemented RF propagation simulation tool may identify any possible obstructions one edge of which may lie in a simulated RF propagation path between two points, and apply an edge based RF diffraction model (a so-called “knife edge diffraction” model) thereto to simulate the RF propagation around the obstruction. In some aspects, a computer implemented RF propagation simulation tool may identify possible obstructions which in their entirety lie within the width of a simulated RF propagation path, and apply a further diffraction model (a so-called “shield diffraction” model) thereto to simulate the RF propagation around the obstruction.

Abstract: A computer-implemented method and system for generating partition polygons across a geographic area, wherein a concave hull of the reference network, usually a network of linear features such as roads, and the spatial data objects to be processed is first generated, the resulting concave hull thereby providing the boundary geometry for the final partition scheme. By generating a concave hull from the reference network and the spatial data objects, it is possible to derive a boundary that is based on the spatial position of the reference network and the spatial data objects at the periphery of the geographic area that they cover, that is, the boundary itself connects at least a portion of the end points of the reference network and the spatial data objects.

Abstract: A method and system that enables a data owner to write data in an encrypted manner to an immutable ledger, and yet still be able to grant read access to specific data elements, as they were written at particular moments in time, to a requesting party. Examples therefore provide a process for encryption of data onto an immutable ledger in a time indexed manner, together with a process by which a third party can request access to the data stored in the immutable ledger from the data owner, and the data owner can provide them with certain decryption keys that allow the third party to read the data directly from the immutable ledger, again based on time-indexed queries. The data the third party can read is restricted to specific elements only of the data written, and further restricted to within a time range or to a specific point in time.

Abstract: Computer-implemented methods and systems are described herein for optimising aerial observation positions for capturing aerial images of a geographical area, in which both the field of view of the camera used to capture the images and the surface terrain is taken into consideration to ensure that all of the geographical area is fully and completely imaged. Aspects may be used for improving the aerial imagery used for geospatial surveying. The pixels of a digital surface model representative of a geographic area are analysed to determine whether they will be visible to a camera when it is located at a number of different observation points located above the same target geographic area. For each observation point at which an image is to be captured, each pixel is analysed to determine whether or not they are within the field of view of the camera when positioned at that given observation point.

Abstract: Aspects described herein provide a computer-implemented method and system of generating circular routes that start and end at the same geographical position, the route passing through a number of intermediate points along the way. The circular routes are generated in dependence on one or more route criterion, which may be pre-defined or input by the user. The route criterion may relate to a number of different route properties, for example, the length of time it takes to complete the route, the distance or area covered by the route, the direction the route takes, the type of terrain experienced during the route, whether the route passes through certain points more than once and/or whether the route passes any points of interest.

Abstract: The present disclosure provides a computer implemented method and system for compensating for cloud cover to allow the build-up of composite, cloud free images of a particular geographic area. The disclosure takes satellite or aerial image data as an input and temporally stacks the images for a particular geographic area, with the most recent image at the top. The time for the composite image to be produced (which may be the present time, or may be a time from the past) is defined as a temporal projection plane. The most recent image prior to or temporally proximate to this plane is analysed to identify any areas obscured by clouds. Cloud free areas of the most recent image are projected onto the temporal projection plane. For the identified obscured areas, older, cloud free images in the stack are projected onto the temporal projection plane to fill the obscured areas. This forms a cloud free composite image at the defined temporal projection plane.

Abstract: Aspects described herein relate to methods and systems for obtaining geographic position data for features that are captured through a device using a relative coordinate system, such as an optical sensor on a vehicle travelling along a route. The relative positions of the features are transformed into geographic positions by first establishing a spatial relationship between the relative coordinate system and a geographic coordinate system. Once the spatial relationship is known, it can be used to match a number of the features captured in the relative coordinate system to features having a known position within the geographic coordinate system, preferably, features that are known to have an accurately measured geographic position. The matched features can then be used as tie points between the relative coordinate system and the geographic coordinate system, and then used to transform unmatched features in the relative coordinate system to positions in the geographic coordinate system.

Abstract: Aspects described herein provide a computer-implemented method and system for grouping topographic data based on the context of said data without the operator needing to make any assumptions. For each vector feature, its context, that is, information about the adjacent features, is incorporated in to the associated attribution data. In doing this, the system is able to characterise all of the vector features in a geographical area based on its context, from which patterns emerge. These patterns indicate features that have similar contexts, enabling the system to group the vector features according to their contexts based on their characteristics and attributes. Conversely, features that are anomalous within the region, that is, they do not fit the pattern of the surrounding features, are also identified. This is particularly important for identifying and resolving errors in the underlying topographic data.

Abstract: Aspects described herein provide a computer-implemented method and system for generating topographic map data at different scales. More specifically, the topographic map data is reduced from large scale to small scale, wherein the scale of the vector features is reduced according to their geometry, feature type and attributes. In order to quickly and easily output digital maps at different scales so that a user may quickly zoom in and out of a map, different zoom levels are produced for different scales, each zoom level containing a variable amount of detail according to its scale, with larger scale zoom levels generally containing more detail than small scale zoom levels. Each zoom level is made up of one or more vector tiles representative of a geographic area, wherein the vector tiles may contain one or more vector features that are representative of objects within that geographic area, and other information related to that geographical area.

Abstract: Land cover (LC) and land use (LU) have commonly been classified separately from remotely sensed imagery, without considering the intrinsically hierarchical and nested relationships between them. A novel joint deep learning framework is proposed and demonstrated for LC and LU classification. The proposed Joint Deep Learning (JDL) model incorporates a multilayer perceptron (MLP) and convolutional neutral network (CNN), and is implemented via a Markov process involving iterative updating. In the JDL, LU classification conducted by the CNN is made conditional upon the LC probabilities predicted by the MLP. In turn, those LU probabilities together with the original imagery are re-used as inputs to the MLP to strengthen the spatial and spectral feature representation. This process of updating the MLP and CNN forms a joint distribution, where both LC and LU are classified simultaneously through iteration.

Abstract: A computer-implemented method and system for determining the geographical location of a user based on the characteristics of intersecting features, such as a road intersection. Specifically, the geometry of each intersection in a geographical area is used to derive a unique fingerprint for each individual intersection, the fingerprint comprising information relating to the geometry, the geographical location of the intersection, and other characteristics. These fingerprints can then be stored locally to a device, for example, a mobile phone, a tablet, a wearable computing device, an in-vehicle infotainment (IVI) system and the like. To determine the geographical location of the device, the geometry of a nearby intersection may be analysed by some means and compared to the stored set of unique fingerprints to identify the intersection and its associated location.

Abstract: Aspects described herein are concerned with automatically validating spatial features of a spatial dataset. One solution is built upon a database server which performs a decomposition of the features' geometries into line segments, after which, sets of line segments are compared to detect errors in the dataset. The decomposition of features geometries into line segments results in a reduction of processing time with a simultaneous increase of precision. The method not only generates new intermediate database structures for detection of topological errors but is also complemented by a clearance process that can lead to the automatic repair of topological errors. The clearance process identifies topological errors and provides modifications to the previously identified line segments such that a modified set of line segments overcomes the conditions that led to the error state. The changes to the data set can be applied automatically after displaying them as an overlay for a predefined amount of time.

Abstract: An object-based convolutional neural network (OCNN) method and system for urban land use classification from VFSR imagery are described. In the OCNN, segmented objects consisting of linearly shaped objects (LS-objects) and other general objects (G-objects), are utilized as functional units. The G-objects are precisely identified and labelled through a single large input window (128×128) CNN with a deep (eight-layer) network to perform a contextual object-based classification. Whereas the LS-objects are each distinguished accurately using a range of small input window (48×48) CNNs with less deep (six-layer) networks along the objects' lengths through majority voting. The locations of the input image patches for both CNN networks are determined by considering both object geometry and its spatial anisotropy, such as to accurately classify the objects into urban land use classes.

Abstract: A system and method of authentication is provided that uses the spatiotemporal context of a user which has been measured using third party monitoring systems. Spatiotemporal data obtained from third party systems such as CCTV networks, microphone networks, UAV networks, CAV networks, ATM networks or the like is used to determine a unique spatiotemporal fingerprint for an individual. That is, the unique identifier is not derived from a device carried by the individual. This fingerprint can then be used to verify a user and detect abnormal and unexpected behaviour.