The present disclosure is directed to a software tool that engages in an image matching technique. In one implementation, the software tool (i) accesses a set of two-dimensional drawings representative of a construction project, (ii) determines multiple candidate pixel regions for each of the two-dimensional drawings, (iii) compares the candidate pixel regions to identify a set of landmark pixel regions that appear in the set of two-dimensional drawings at a threshold rate, (iv) compare a first subset of landmark pixel regions from a first two-dimensional drawing with a second subset of landmark pixel regions from a second two-dimensional drawing to identify matching landmark pixel regions, (v) project the first and second two-dimensional drawings onto a projection space such that a maximum number of the matching landmark pixel regions align, and (vi) determine an extent of similarity between the projected first and second two-dimensional drawings in the projection space.

In one aspect, a request to generate an automated tour based on a set of panoramic images is received. Each particular panoramic image is associated with geographic location information and linking information linking the particular panoramic image with one or more other panoramic images in the set. A starting panoramic image and a second panoramic image are determined based at least in part on the starting panoramic image and the linking information associated with the starting and second panoramic images. A first transition between the starting panoramic image and the second panoramic image is also determined based at least in part on the linking information for these panoramic images. Additional panoramic images as well as a second transition for between the additional panoramic images are also determined. The determined panoramic images and transitions are added to the tour according to an order of the tour.

The systems may include dividing a digital map provided by a mapping system into a matrix having a plurality of cells; assigning a cell of the plurality of cells to encompass a geographic region of the digital map; calculating a number of sites of interest in the cell; creating a marker comprising a first count number representing the number of sites of interest in the cell; and sharing the marker with a browser for display on the digital map.

The systems may include dividing a digital map provided by a mapping system into a matrix having a plurality of cells; assigning a cell of the plurality of cells to encompass a geographic region of the digital map; calculating a number of sites of interest in the cell; creating a marker comprising a first count number representing the number of sites of interest in the cell; and sharing the marker with a browser for display on the digital map.

A method for digitizing image-based data includes receiving an image file including one or more target objects, generating an intermediate image by removing noise from the image file using a denoising machine learning model, identifying the one or more target objects included in the intermediate image using an object segmentation machine learning model, discretizing the one or more target objects that were identified using the trained object segmentation machine learning model, and storing the one or more target objects that were discretized in a data file, visualizing the one or more target objects, or both.

Systems and methods for automating information extraction from piping and instrumentation diagrams is provided. Traditional systems and methods do not provide for end-to-end and automated data extraction from the piping and instrumentation diagrams. The method disclosed provides for automatic generation of end-to-end information from piping and instrumentation diagrams by detecting, via one or more hardware processors, a plurality of components from one or more piping and instrumentation diagrams by implementing one or more image processing and deep learning techniques; associating, via an association module, each of the detected plurality of components by implementing a Euclidean Distance technique; and generating, based upon each of the associated plurality of components, a plurality of tree-shaped data structures by implementing a structuring technique, wherein each of the plurality of tree-shaped data structures capture a process flow of pipeline schematics corresponding to the one or more piping and instrumentation diagrams.

A method includes retrieving an unstructured document and defining an area of interest of the unstructured document that visually represents geological formation information. The method also includes extracting a set of vectorized polygons from the area of interest. Additionally, the method includes assigning properties from the unstructured document to each of the vectorized polygons in the set of vectorized polygons. Further, the method includes assigning a coordinate reference frame to the set of vectorized polygons and generating a user-interactive document from the set of vectorized polygons.

A method includes retrieving an unstructured document and defining an area of interest of the unstructured document that visually represents geological formation information. The method also includes extracting a set of vectorized polygons from the area of interest. Additionally, the method includes assigning properties from the unstructured document to each of the vectorized polygons in the set of vectorized polygons. Further, the method includes assigning a coordinate reference frame to the set of vectorized polygons and generating a user-interactive document from the set of vectorized polygons.

Automated data analytics techniques for non-tabular data sets may include methods and systems for (1) automatically developing models that perform tasks in the domains of computer vision, audio processing, speech processing, text processing, or natural language processing; (2) automatically developing models that analyze heterogeneous data sets containing image data and non-image data, and/or heterogeneous data sets containing tabular data and non-tabular data; (3) determining the importance of an image feature with respect to a modeling task, (4) explaining the value of a modeling target based at least in part on an image feature, and (5) detecting drift in image data. In some cases, multi-stage models may be developed, wherein a pre-trained feature extraction model extracts low-, mid-, high-, and/or highest-level features of non-tabular data, and a data analytics models uses those features (or features derived therefrom) to perform a data analytics task.

Automated data analytics techniques for non-tabular data sets may include methods and systems for (1) automatically developing models that perform tasks in the domains of computer vision, audio processing, speech processing, text processing, or natural language processing; (2) automatically developing models that analyze heterogeneous data sets containing image data and non-image data, and/or heterogeneous data sets containing tabular data and non-tabular data; (3) determining the importance of an image feature with respect to a modeling task, (4) explaining the value of a modeling target based at least in part on an image feature, and (5) detecting drift in image data. In some cases, multi-stage models may be developed, wherein a pre-trained feature extraction model extracts low-, mid-, high-, and/or highest-level features of non-tabular data, and a data analytics models uses those features (or features derived therefrom) to perform a data analytics task.