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

Contextual Pyramid Attention Network for Building Segmentation in Aerial Imagery