Abstract: A computer-based method for automatically evaluating validity and extent of at least one damaged object from image data, comprising: (a) receiving image data comprising one or more images of at least one damaged object; (b) processing said one or more images using an image alteration detection algorithm to detect fraudulent manipulation of said one or more images by classifying at least one attribute of said one or more images; (c) removing any image comprising fraudulent manipulation from said one or more images; (d) processing said one or more images using at least one machine learning algorithm to identify at least one of: a damaged object in any one of said one or more images a damaged area of said damaged object, and an extent of damage of said at least one damaged area; and (e) generating a predictive analysis to repair and/or replace said at least one damaged object.

Abstract: A computer-based method for automatically evaluating validity and extent of at least one damaged object from image data, comprising: (a) receiving image data comprising one or more images of at least one damaged object; (b) processing said one or more images using an image alteration detection algorithm to detect fraudulent manipulation of said one or more images by classifying at least one attribute of said one or more images; (c) removing any image comprising fraudulent manipulation from said one or more images; (d) processing said one or more images using at least one machine learning algorithm to identify at least one of: a damaged object in any one of said one or more images a damaged area of said damaged object, and an extent of damage of said at least one damaged area; and (e) generating a predictive analysis to repair and/or replace said at least one damaged object.

Abstract: The present invention provides a computer-based method for automatically evaluating validity and extent of at least one damaged object from image data, comprising the steps of: (a) receive image data comprising one or more images of at least one damaged object; (b) inspect any one of said one or more images for existing image alteration utilising an image alteration detection algorithm, and remove any image comprising image alterations from said one or more images; (c) identify and classify said at least one damaged object in any one of said one or more images, utilising at least one first machine learning algorithm; (d) detect at least one damaged area of said classified damaged object, utilising at least one second machine learning algorithm; (e) classify, quantitatively and/or qualitatively, an extent of damage of said at least one damaged area, utilising at least one third machine learning algorithm, and characteristic information of said damaged object and/or an undamaged object that is at least equivale

Abstract: The present invention provides a computer-based method for automatically evaluating validity and extent of at least one damaged object from image data, comprising the steps of: (a) receive image data comprising one or more images of at least one damaged object; (b) inspect any one of said one or more images for existing image alteration utilising an image alteration detection algorithm, and remove any image comprising image alterations from said one or more images; (c) identify and classify said at least one damaged object in any one of said one or more images, utilising at least one first machine learning algorithm; (d) detect at least one damaged area of said classified damaged object, utilising at least one second machine learning algorithm; (e) classify, quantitatively and/or qualitatively, an extent of damage of said at least one damaged area, utilising at least one third machine learning algorithm, and characteristic information of said damaged object and/or an undamaged object that is at least equivale