Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training

by   Qingyu Li, et al.

Accurate and reliable building footprint maps are vital to urban planning and monitoring, and most existing approaches fall back on convolutional neural networks (CNNs) for building footprint generation. However, one limitation of these methods is that they require strong supervisory information from massive annotated samples for network learning. State-of-the-art semi-supervised semantic segmentation networks with consistency training can help to deal with this issue by leveraging a large amount of unlabeled data, which encourages the consistency of model output on data perturbation. Considering that rich information is also encoded in feature maps, we propose to integrate the consistency of both features and outputs in the end-to-end network training of unlabeled samples, enabling to impose additional constraints. Prior semi-supervised semantic segmentation networks have established the cluster assumption, in which the decision boundary should lie in the vicinity of low sample density. In this work, we observe that for building footprint generation, the low-density regions are more apparent at the intermediate feature representations within the encoder than the encoder's input or output. Therefore, we propose an instruction to assign the perturbation to the intermediate feature representations within the encoder, which considers the spatial resolution of input remote sensing imagery and the mean size of individual buildings in the study area. The proposed method is evaluated on three datasets with different resolutions: Planet dataset (3 m/pixel), Massachusetts dataset (1 m/pixel), and Inria dataset (0.3 m/pixel). Experimental results show that the proposed approach can well extract more complete building structures and alleviate omission errors.


page 6

page 7

page 8

page 10

page 11

page 12

page 13

page 15


Semi-Supervised Semantic Segmentation with Cross-Consistency Training

In this paper, we present a novel cross-consistency based semi-supervise...

SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks

Most urban applications necessitate building footprints in the form of c...

Building Footprint Generation by IntegratingConvolution Neural Network with Feature PairwiseConditional Random Field (FPCRF)

Building footprint maps are vital to many remote sensing applications, s...

Consistency regularization and CutMix for semi-supervised semantic segmentation

Consistency regularization describes a class of approaches that have yie...

Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students

The popular methods for semi-supervised semantic segmentation mostly ado...

Quantization in Relative Gradient Angle Domain For Building Polygon Estimation

Building footprint extraction in remote sensing data benefits many impor...

Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows

The detection of manufacturing errors is crucial in fabrication processe...

Please sign up or login with your details

Forgot password? Click here to reset