MAP-Net: Multi Attending Path Neural Network for Building Footprint
Extraction from Remote Sensed Imagery
release_zdzgutvrjjbyhdnqsrplipfojm
by
Qing Zhu, Cheng Liao, Han Hu, Xiaoming Mei, Haifeng Li
2019
Abstract
Accurately and efficiently extracting building footprints from a wide range
of remote sensed imagery remains a challenge due to their complex structure,
variety of scales and diverse appearances. Existing convolutional neural
network (CNN)-based building extraction methods are complained that they cannot
detect the tiny buildings because the spatial information of CNN feature maps
are lost during repeated pooling operations of the CNN, and the large buildings
still have inaccurate segmentation edges. Moreover, features extracted by a CNN
are always partial which restricted by the size of the respective field, and
large-scale buildings with low texture are always discontinuous and holey when
extracted. This paper proposes a novel multi attending path neural network
(MAP-Net) for accurately extracting multiscale building footprints and precise
boundaries. MAP-Net learns spatial localization-preserved multiscale features
through a multi-parallel path in which each stage is gradually generated to
extract high-level semantic features with fixed resolution. Then, an attention
module adaptively squeezes channel-wise features from each path for
optimization, and a pyramid spatial pooling module captures global dependency
for refining discontinuous building footprints. Experimental results show that
MAP-Net outperforms state-of-the-art (SOTA) algorithms in boundary localization
accuracy as well as continuity of large buildings. Specifically, our method
achieved 0.68\%, 1.74\%, 1.46\% precision, and 1.50\%, 1.53\%, 0.82\% IoU score
improvement without increasing computational complexity compared with the
latest HRNetv2 on the Urban 3D, Deep Globe and WHU datasets, respectively. The
TensorFlow implementation is available at https://github.com/lehaifeng/MAPNet.
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