Bootstrapped CNNs for Building Segmentation on RGB-D Aerial Imagery

TL;DR

This paper introduces a bootstrapped CNN approach using DenseNet for building segmentation in RGB-D aerial images, achieving high precision with significantly less training data by focusing on informative samples.

Contribution

The novel combination of bootstrapping with DenseNet CNN improves building detection accuracy and reduces training data requirements in aerial imagery segmentation.

Findings

Outperforms non-bootstrapped models with one-sixth data

Achieves 95.10% precision-recall break-even

Uses bootstrapping to select informative training samples

Abstract

Detection of buildings and other objects from aerial images has various applications in urban planning and map making. Automated building detection from aerial imagery is a challenging task, as it is prone to varying lighting conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are robust against some of these variations, although they fail to distinguish easy and difficult examples. We train a detection algorithm from RGB-D images to obtain a segmented mask by using the CNN architecture DenseNet.First, we improve the performance of the model by applying a statistical re-sampling technique called Bootstrapping and demonstrate that more informative examples are retained. Second, the proposed method outperforms the non-bootstrapped version by utilizing only one-sixth of the original training data and it obtains a precision-recall break-even of 95.10% on our aerial imagery dataset.