  In this paper, we attempt to address the challenging problem of counting
built-structures in the satellite imagery. Building density is a more accurate
estimate of the population density, urban area expansion and its impact on the
environment, than the built-up area segmentation. However, building shape
variances, overlapping boundaries, and variant densities make this a complex
task. To tackle this difficult problem, we propose a deep learning based
regression technique for counting built-structures in satellite imagery. Our
proposed framework intelligently combines features from different regions of
satellite image using attention based re-weighting techniques. Multiple
parallel convolutional networks are designed to capture information at
different granulates. These features are combined into the FusionNet which is
trained to weigh features from different granularity differently, allowing us
to predict a precise building count. To train and evaluate the proposed method,
we put forward a new large-scale and challenging built-structure-count dataset.
Our dataset is constructed by collecting satellite imagery from diverse
geographical areas (planes, urban centers, deserts, etc.,) across the globe
(Asia, Europe, North America, and Africa) and captures the wide density of
built structures. Detailed experimental results and analysis validate the
proposed technique. FusionNet has Mean Absolute Error of 3.65 and R-squared
measure of 88% over the testing data. Finally, we perform the test on the 274:3
? 103 m2 of the unseen region, with the error of 19 buildings off the 656
buildings in that area.
