Abstract: Systems and methods of automating the generation of a correction of an estimate of an elevation of a digital elevation model (DEM) of the bare earth under forest canopy. The disclosed embodiments facilitate generation of a more accurate DEM in areas of canopy coverage (where the input X-band DSM cannot see the ground) to estimate both the canopy height and the associated DEM. In some embodiments, the result of computationally correcting an estimate of an original DEM is a modified DEM. The method of correcting an estimate of an original DEM utilizes a pair of P-band radar images, an original DEM overlapping the same scene as the P-band radar images, at least one common, uniquely-identifiable point in the P-band radar images, and a definition of a geographical area surrounding the common, uniquely identifiable point over which the elevation correction is applicable.

Abstract: Systems and methods of automating the generation of an estimate of the first floor elevation of a building, using a Digital Terrain Model (DTM) and an image of the front of the building. First, machine learning (ML) algorithms are used to estimate the height of the first floor of the building above ground from the image. The estimated height is added to a DTM height of the geographical area corresponding to the location of the building to provide an estimate of the FFE. The disclosed embodiments can be applied in estimating the first floor height of any kind of building. For example, single-family homes, townhomes, high rise buildings, stores, malls, shops, movie theaters, or any other building. Additionally, the disclosed embodiments are camera-agnostic and can be applied on any type of image collected by any type of camera.

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: Implementations described and claimed herein provide systems and methods for flood hazard zone modeling. In one implementation, one or more relevant stream segments are identified from an input stream network. One or more cross profiles are defined for one or more selected points on the relevant stream segments, with the selected points located within a flood hazard area. A high resolution flood level elevation map is generated by interpolating flood level elevation from the selected points. A flood depth map is generated having a flood depth value for each of the selected points computed as a difference between the high resolution flood level elevation map and a terrain elevation. One or more flood zones are defined in the flood depth map. The flood zones have a positive flood depth for a return period.

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: Implementations described and claimed herein provide systems and methods for flood hazard zone modeling. In one implementation, one or more relevant stream segments are identified from an input stream network. One or more cross profiles are defined for one or more selected points on the relevant stream segments, with the selected points located within a flood hazard area. A high resolution flood level elevation map is generated by interpolating flood level elevation from the selected points. A flood depth map is generated having a flood depth value for each of the selected points computed as a difference between the high resolution flood level elevation map and a terrain elevation. One or more flood zones are defined in the flood depth map. The flood zones have a positive flood depth for a return period.

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.