  Efficient road and building footprint extraction from satellite images are
predominant in many remote sensing applications. However, precise segmentation
map extraction is quite challenging due to the diverse building structures
camouflaged by trees, similar spectral responses between the roads and
buildings, and occlusions by heterogeneous traffic over the roads. Existing
convolutional neural network (CNN)-based methods focus on either enriched
spatial semantics learning for the building extraction or the fine-grained road
topology extraction. The profound semantic information loss due to the
traditional pooling mechanisms in CNN generates fragmented and disconnected
road maps and poorly segmented boundaries for the densely spaced small
buildings in complex surroundings. In this paper, we propose a novel
attention-aware segmentation framework, Multi-Scale Supervised Dilated
Multiple-Path Attention Network (MSSDMPA-Net), equipped with two new modules
Dynamic Attention Map Guided Index Pooling (DAMIP) and Dynamic Attention Map
Guided Spatial and Channel Attention (DAMSCA) to precisely extract the building
footprints and road maps from remotely sensed images. DAMIP mines the salient
features by employing a novel index pooling mechanism to retain important
geometric information. On the other hand, DAMSCA simultaneously extracts the
multi-scale spatial and spectral features. Besides, using dilated convolution
and multi-scale deep supervision in optimizing MSSDMPA-Net helps achieve
stellar performance. Experimental results over multiple benchmark building and
road extraction datasets, ensures MSSDMPA-Net as the state-of-the-art (SOTA)
method for building and road extraction.
