# CNNs Fusion for Building Detection in Aerial Images for the Building Detection Challenge

arXiv: https://arxiv.org/abs/1809.10976 · HTML: https://mlsanity.com/paper/1809.10976/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery](https://mlsanity.com/paper/2104.01263/) (2021)
- [Sci-Net: Scale Invariant Model for Buildings Segmentation from Aerial Imagery](https://mlsanity.com/paper/2111.06812/) (2021)

## Abstract (arXiv)

This paper presents our contribution to the DeepGlobe Building Detection Challenge. We enhanced the SpaceNet Challenge winning solution by proposing a new fusion strategy based on a deep combiner using segmentation both results of different CNN and input data to segment. Segmentation results for all cities have been significantly improved (between 1% improvement over the baseline for the smallest one to more than 7% for the largest one). The separation of adjacent buildings should be the next enhancement made to the solution.