  This paper proposes CANC, a Co-teaching Active Noise Cancellation method,
applied in spatial computing to address deep learning trained with extreme
noisy labels. Deep learning algorithms have been successful in spatial
computing for land or building footprint recognition. However a lot of noise
exists in ground truth labels due to how labels are collected in spatial
computing and satellite imagery. Existing methods to deal with extreme label
noise conduct clean sample selection and do not utilize the remaining samples.
Such techniques can be wasteful due to the cost of data retrieval. Our proposed
CANC algorithm not only conserves high-cost training samples but also provides
active label correction to better improve robust deep learning with extreme
noisy labels. We demonstrate the effectiveness of CANC for building footprint
recognition for spatial computing.
