  Building extraction from aerial images has several applications in problems
such as urban planning, change detection, and disaster management. With the
increasing availability of data, Convolutional Neural Networks (CNNs) for
semantic segmentation of remote sensing imagery has improved significantly in
recent years. However, convolutions operate in local neighborhoods and fail to
capture non-local features that are essential in semantic understanding of
aerial images. In this work, we propose to improve building segmentation of
different sizes by capturing long-range dependencies using contextual pyramid
attention (CPA). The pathways process the input at multiple scales efficiently
and combine them in a weighted manner, similar to an ensemble model. The
proposed method obtains state-of-the-art performance on the Inria Aerial Image
Labelling Dataset with minimal computation costs. Our method improves 1.8
points over current state-of-the-art methods and 12.6 points higher than
existing baselines on the Intersection over Union (IoU) metric without any
post-processing. Code and models will be made publicly available.
