# Deep Built-Structure Counting in Satellite Imagery Using Attention Based Re-Weighting

## TL;DR

This paper introduces a deep learning framework with attention-based re-weighting for accurately counting built-structures in satellite images, addressing challenges like shape variance and overlapping boundaries.

## Contribution

It proposes a novel attention-based fusion network and a large-scale dataset for improved built-structure counting in diverse satellite imagery.

## Key findings

- Achieved a Mean Absolute Error of 3.65 in building count prediction.
- Demonstrated high correlation with an R-squared of 88%.
- Validated on unseen regions with an error of 19 buildings out of 656.

## Abstract

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.