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

Remi Delassus, Romain Giot; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 242-246

Abstract


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 biggest one). The separation of adjacent buildings should be the next enhancement made to the solution.

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[bibtex]
@InProceedings{Delassus_2018_CVPR_Workshops,
author = {Delassus, Remi and Giot, Romain},
title = {CNNs Fusion for Building Detection in Aerial Images for the Building Detection Challenge},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}