Abstract: A technique is directed to methods and systems for calculating building heights from mono imagery. In some implementations, a building height calculation system performs orthorectification of an image of buildings against a digital terrain model to remove effects of terrain distortion from the image. The building height calculation system can execute an edge detection algorithm on the image to identify the edges of the building in the image. The edges can provide a rooftop vector of the building. The building height calculation system can execute, using image data at input, a machine learning algorithm to determine the footprint vector of the building in the image. The building height is calculated based on a camera angle, a distance from the camera to the building, and a pixel offset from the footprint vector to the rooftop vector.

Abstract: Systems and methods of enhancing the resolution of digital terrain models (DTM) for location-based applications and analyses. The DTM enhancement process takes the signature of the input image (e.g., via the input image and a noise surface file with similar characteristics as the sensor used to capture the input image) and applies it to the DTM without including large features such as buildings. The disclosed methods include utilize a process similar to that used for enhancing a DSM based on mapping the changing intensity from the image file to changes in elevation in the DSM using a regression over a local neighborhood of pixels. Further, the disclosed methods do not rely on information about the sensors and are extendable to be able to utilize any types of images. Additionally, the disclosed embodiments are sensor agnostic and can be applied on any type of image collected by any type of sensor.

Abstract: Systems and methods of enhancing the resolution of digital terrain models (DTM) for location-based applications and analyses. The DTM enhancement process takes the signature of the input image (e.g., via the input image and a noise surface file with similar characteristics as the sensor used to capture the input image) and applies it to the DTM without including large features such as buildings. The disclosed methods include utilize a process similar to that used for enhancing a DSM based on mapping the changing intensity from the image file to changes in elevation in the DSM using a regression over a local neighborhood of pixels. Further, the disclosed methods do not rely on information about the sensors and are extendable to be able to utilize any types of images. Additionally, the disclosed embodiments are sensor agnostic and can be applied on any type of image collected by any type of sensor.

Abstract: Systems and methods of enhancing the resolution of digital terrain models (DTM) for location-based applications and analyses. The DTM enhancement process takes the signature of the input image (e.g., via the input image and a noise surface file with similar characteristics as the sensor used to capture the input image) and applies it to the DTM without including large features such as buildings. The disclosed methods include utilize a process similar to that used for enhancing a DSM based on mapping the changing intensity from the image file to changes in elevation in the DSM using a regression over a local neighborhood of pixels. Further, the disclosed methods do not rely on information about the sensors and are extendable to be able to utilize any types of images. Additionally, the disclosed embodiments are sensor agnostic and can be applied on any type of image collected by any type of sensor.

Abstract: Systems and methods of enhancing the resolution of digital terrain models (DTM) for location-based applications and analyses. The DTM enhancement process takes the signature of the input image (e.g., via the input image and a noise surface file with similar characteristics as the sensor used to capture the input image) and applies it to the DTM without including large features such as buildings. The disclosed methods include utilize a process similar to that used for enhancing a DSM based on mapping the changing intensity from the image file to changes in elevation in the DSM using a regression over a local neighborhood of pixels. Further, the disclosed methods do not rely on information about the sensors and are extendable to be able to utilize any types of images. Additionally, the disclosed embodiments are sensor agnostic and can be applied on any type of image collected by any type of sensor.

Abstract: Systems and methods of enhancing the resolution or restoring details associated with high resolution images into a filtered digital surface model (DSM) for location-based applications and analyses. The disclosed methods include mapping the changing gray scale values (intensity) from the images to changes in elevation in the DSM using a regression over a local neighborhood of pixels. Further, the disclosed methods do not rely on information about the sensor illumination geometry, and are extendable to be able to utilize any types of images. Additionally, the disclosed embodiments are sensor agnostic. That is, the disclosed methods can be applied on any type of images collected by any type of sensor.

Abstract: Systems and methods of enhancing the resolution or restoring details associated with high resolution images into a filtered digital surface model (DSM) for location-based applications and analyses. The disclosed methods include mapping the changing gray scale values (intensity) from the images to changes in elevation in the DSM using a regression over a local neighborhood of pixels. Further, the disclosed methods do not rely on information about the sensor illumination geometry, and are extendable to be able to utilize any types of images. Additionally, the disclosed embodiments are sensor agnostic. That is, the disclosed methods can be applied on any type of images collected by any type of sensor.

Abstract: Systems and methods of enhancing the resolution or restoring details associated with high resolution images into a filtered digital surface model (DSM) for location-based applications and analyses. The disclosed methods include mapping the changing gray scale values (intensity) from the images to changes in elevation in the DSM using a regression over a local neighborhood of pixels. Further, the disclosed methods do not rely on information about the sensor illumination geometry, and are extendable to be able to utilize any types of images. Additionally, the disclosed embodiments are sensor agnostic. That is, the disclosed methods can be applied on any type of images collected by any type of sensor.

Abstract: A system and method for generating a corrected digital elevation model is configured to remove systematic errors from digital elevation models. A residual dataset that is the difference between a first digital elevation model and a second digital elevation model is obtained and at least one error pattern related to a systematic error is identified. An error model representing at least one error pattern is generated and then removed from the first digital elevation model to create the corrected digital elevation model.