Abstract: A process for locating real estate parcels for a user comprises accessing a library of parceled real estate image data to identify objects and features in a plurality of parcels identified by the user as having a feature of interest. A predictive model is constructed and applied to a geographic region selected by the user to generate a customized output of real estate parcels predicted to have the feature of interest, or, in some implementations, not to have the feature of interest.

Abstract: Systems and methods for roof condition assessment from digital images using machine learning are disclosed, including receiving an image of a structure having roof characteristic(s), first pixel values depicting the structure, second pixel values outside of the structure depicting a background surrounding the structure, and first geolocation data; generating a synthetic shape image of the structure from the image using machine learning, including pixel values forming a synthetic outline shape, and having second geolocation data; mapping the synthetic shape onto the image, based on the first and second geolocation data, and changing the second pixel values so as to not depict the background; assessing roof characteristic(s) based on the first pixel values with a second machine learning algorithm resulting in a plurality of probabilities, each for a respective roof condition classification category, and determining a composite probability based upon the plurality of probabilities so as to classify the roof char

Abstract: 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 an outline of a structure at a first instance of time to pixels within an image depicting the structure, the image captured at a second instance of time; assess a degree of alignment between the outline and the pixels within the image depicting the structure, using a machine learning model to generate an alignment confidence score; determine an existence of a change in extent of the structure based upon the alignment confidence score indicating that the outline and the pixels within the image are not aligned; identify a shape of the change in extent of the structure; and store the shape of the change in extent of the structure.

Abstract: A process for locating real estate parcels for a user comprises accessing a library of parceled real estate image data to identify objects and features in a plurality of parcels identified by the user as having a feature of interest. A predictive model is constructed and applied to a geographic region selected by the user to generate a customized output of real estate parcels predicted to have the feature of interest.

Abstract: Systems and methods for roof condition assessment from digital images using machine learning are disclosed, including receiving an image of a structure having roof characteristic(s), first pixel values depicting the structure, second pixel values outside of the structure depicting a background surrounding the structure, and first geolocation data; generating a synthetic shape image of the structure from the image using machine learning, including pixel values forming a synthetic outline shape, and having second geolocation data; mapping the synthetic shape onto the image, based on the first and second geolocation data, and changing the second pixel values so as to not depict the background; assessing roof characteristic(s) based on the first pixel values with a second machine learning algorithm resulting in a plurality of probabilities, each for a respective roof condition classification category, and determining a composite probability based upon the plurality of probabilities so as to classify the roof char

Abstract: Apparatuses, systems, methods, and medium are disclosed for precise geospatial structure geometry extraction from multi-view imagery, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: receive an image of a structure having an outline, the image having pixels with first pixel values depicting the structure and second pixel values outside of the structure depicting a background of a geographic area surrounding the structure, and image metadata including first geolocation data; and generate a synthetic shape image of the structure from the image using a machine learning algorithm, the synthetic shape image including pixels having pixel values forming a synthetic shape of the outline, the synthetic shape image having second geolocation data derived from the first geolocation data.

Abstract: Methods and systems for automatic estimation of object characteristics from a digital image are disclosed, including a method comprising sub-dividing into two or more segments a digital image comprising pixels and depicting an object of interest, wherein each segment comprises two or more pixels; assessing content depicted in one or more of the segments for a predetermined object characteristic using machine learning techniques comprising General Image Classification of the one or more segments using a convolutional neural network, wherein the General Image Classification comprises analyzing the segment as a whole and outputting a general classification for the segment as a whole as related to the one or more predetermined object characteristic; and determining a level of confidence of one or more of the segments having the one or more predetermined object characteristic based on the General Image Classification assessment.

Abstract: 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.

Abstract: Apparatuses, systems, methods, and medium are disclosed for precise geospatial structure geometry extraction from multi-view imagery, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: receive an image of a structure having an outline, the image having pixels with first pixel values depicting the structure and second pixel values outside of the structure depicting a background of a geographic area surrounding the structure, and image metadata including first geolocation data; and generate a synthetic shape image of the structure from the image using a machine learning algorithm, the synthetic shape image including pixels having pixel values forming a synthetic shape of the outline, the synthetic shape image having second geolocation data derived from the first geolocation data.

Abstract: A process for locating real estate parcels for a user comprises accessing a library of parceled real estate image data to identify objects and features in a plurality of parcels identified by the user as having a feature of interest. A predictive model is constructed and applied to a geographic region selected by the user to generate a customized output of real estate parcels predicted to have the feature of interest.

Abstract: Methods and systems for automated structure modeling form digital imagery are disclosed, including a method comprising receiving target digital images depicting a target structure; automatically identifying target elements of the target structure in the target digital images using convolutional neural network semantic segmentation; automatically generating a heat map model depicting a likelihood of a location of the target elements of the target structure; automatically generating a two-dimensional model or a three-dimensional model of the target structure based on the heat map model without further utilizing the target digital images; and extracting information regarding the target elements from the two-dimensional or the three-dimensional model of the target structure.

Abstract: Methods and systems for predicting well site production are disclosed, including a computer system comprising one or more processor and a non-transitory computer memory storing processor readable instructions that when executed by the one or more processor cause the one or more processor to receive image data of a geographic region around and including a well site; receive well site location data of a location of the well site; analyze well site data to determine well pad location data of a location of a well pad including an area of observation extending beyond and around a well site; determine pixel data of the well pad within the image data for a particular time from the well pad location data; and analyze the pixel data of the well pad for a particular time to determine a volume of flared gas based on the pixel data.

Abstract: Methods and systems for automated faux-manual image-marking of a digital image are disclosed, including a method comprising obtaining results of an automated analysis of one or more digital image indicative of determinations of structure abnormalities of one or more portions of a structure depicted in the one or more digital image; applying automatically, on the one or more digital image, with one or more computer processors, standardized markings indicative of the location in the image of the structure abnormalities of the structure depicted in the image; and generating, automatically with the one or more computer processors, one or more faux-manual markings by modifying one or more of the standardized markings, utilizing one or more image-manipulation algorithm, wherein the faux-manual markings mimic an appearance of manual markings on the structure in the real world.

Abstract: 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.

Abstract: A process for locating real estate parcels for a user comprises accessing a library of parceled real estate image data to identify objects and features in a plurality of parcels identified by the user as having a feature of interest. A predictive model is constructed and applied to a geographic region selected by the user to generate a customized output of real estate parcels predicted to have the feature of interest.

Abstract: 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.

Abstract: 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, a structure shape of a structure at a first instance of time to pixels within an aerial image depicting the structure captured at a second instance of time; assess a degree of alignment between the structure shape and the pixels, so as to classify similarities between the structure depicted within the pixels and the structure shape 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 structure shape and the pixels within the aerial image are aligned.

Abstract: A device includes an image data receiving component, a vegetation index generation component, a crop data receiving component, a masking component and a multivariate regression component. The image data receiving component receives image data of a geographic region. The vegetation index generation component generates an array of vegetation indices based on the received image data, and includes a plurality of vegetation index generating components, each operable to generate a respective individual vegetation index based on the received image data. The crop data receiving component receives crop data associated with the geographic region. The masking component generates a masked vegetation index based on the array of vegetation indices and the received crop data. The multivariate regression component generates a crop parameter based on the masked vegetation index.

Abstract: Methods and systems for automatic estimation of object characteristics from a digital image are disclosed, including a method comprising sub-dividing into two or more segments a digital image comprising pixels and depicting an object of interest, wherein each segment comprises two or more pixels; assessing content depicted in one or more of the segments for a predetermined object characteristic using machine learning techniques comprising General Image Classification of the one or more segments using a convolutional neural network, wherein the General Image Classification comprises analyzing the segment as a whole and outputting a general classification for the segment as a whole as related to the one or more predetermined object characteristic; and determining a level of confidence of one or more of the segments having the one or more predetermined object characteristic based on the General Image Classification assessment.

Abstract: Methods and systems for predicting well site production are disclosed, including a computer system comprising one or more processor and a non-transitory computer memory storing processor readable instructions that when executed by the one or more processor cause the one or more processor to receive image data of a geographic region around and including a well site; receive well site location data of a location of the well site; analyze well site data to determine well pad location data of a location of a well pad including an area of observation extending beyond and around a well site; determine pixel data of the well pad within the image data for a particular time from the well pad location data; and analyze the pixel data of the well pad for a particular time to determine a volume of flared gas based on the pixel data.