US 12,228,695 B2
Detection of buried pipelines and spills
César Barajas-Olalde, Grand Forks, ND (US); Saurabh Ajit Chimote, Grand Forks, ND (US); Jordan Michael Krueger, Grand Forks, ND (US); and Donald C. Adams, III, Grand Forks, ND (US)
Assigned to Energy and Environmental Research Center Foundation, Grand Forks, ND (US)
Filed by Energy and Environmental Research Center Foundation, Grand Forks, ND (US)
Filed on Mar. 30, 2022, as Appl. No. 17/657,205.
Claims priority of provisional application 63/200,853, filed on Mar. 31, 2021.
Prior Publication US 2022/0317328 A1, Oct. 6, 2022
Int. Cl. G01V 3/38 (2006.01); G01V 3/08 (2006.01); G01V 3/16 (2006.01); G01V 11/00 (2006.01); G06T 7/70 (2017.01); G06V 10/774 (2022.01); G06V 20/17 (2022.01)
CPC G01V 3/38 (2013.01) [G01V 3/081 (2013.01); G01V 3/16 (2013.01); G01V 11/00 (2013.01); G06T 7/70 (2017.01); G06V 10/774 (2022.01); G06V 20/17 (2022.01); G06T 2200/24 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/20081 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
training a machine-learning (ML) model to identify locations of buried pipes, the training based on a training set comprising locations of known buried pipes, images of a surface above the known buried pipes, and magnetic measurements taken over the known buried pipes;
programming a drone to fly over a geographical area with a first buried pipe, the programming based on an approximate location of the first buried pipe;
capturing, during a flight of the drone, geophysical data with geophysical equipment in the drone;
capturing, during the flight of the drone, images with a camera in the drone;
utilizing the machine-learning (ML) model to identify the location of the first buried pipe based on the captured geophysical data and the captured images; and
presenting the identified location of the first buried pipe in a map of the geographical area.