	US 11,836,223 B2
	Systems and methods for automated detection of building footprints
Li Chen, Fremont, CA (US); Purvi Goel, Fremont, CA (US); Ilknur Kaynar Kabul, Mountain View, CA (US); and David Dongzhe Yang, Brighton, MA (US)
Assigned to Meta Platforms, Inc., Menlo Park, CA (US)
Filed by Meta Platforms, Inc., Menlo Park, CA (US)
Filed on Jun. 17, 2021, as Appl. No. 17/350,594.
Claims priority of provisional application 63/113,805, filed on Nov. 13, 2020.
Prior Publication US 2022/0156526 A1, May 19, 2022
Int. Cl. G06K 9/62 (2022.01); G06K 9/32 (2006.01); G06K 9/40 (2006.01); G06K 9/00 (2022.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06F 18/21 (2023.01); G06V 10/24 (2022.01); G06V 10/30 (2022.01); G06V 20/10 (2022.01); G06F 18/2113 (2023.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01)

CPC G06F 18/2185 (2023.01) [G06F 18/2113 (2023.01); G06F 18/2148 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 10/243 (2022.01); G06V 10/30 (2022.01); G06V 20/176 (2022.01)]

20 Claims

1. A computer-implemented method comprising:

collecting a set of labels that label polygons within a training set of images as architectural structures;

creating a set of noisy labels with a predetermined degree of noise by distorting boundaries of a number of the polygons within the training set of images;

simultaneously training two neural networks by applying a co-teaching method to learn from the set of noisy labels;

extracting a preferential list of training data based on the two trained neural networks;

training a machine learning model with the preferential list of training data; and

identifying at least one building footprint in a target image using the trained machine learning model.