Influence of Augmentation of UAV Collected Data on Deep Learning Based Facade Segmentation Task

Open Webpage

Patryk Balak, Agnieszka Lysak, Kamil Choromanski, Marcin Luckner

Published: 2025, Last Modified: 01 Apr 2026ISD 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Data augmentation is crucial for image segmentation, especially in transfer learning with limited data, however it can be costly. This study examines the cost-benefit of augmentation in facade segmentation using unmanned aerial vehicles (UAV) data. We analysed how dataset size and offline augmentation impact classification quality and computation using DeepLabV3+ architecture. Expanding the dataset from 5 to 480 thousand tiles improved segmentation efficiency by an average of 3.7%. Beyond a certain point, further dataset expansion yields minimal gains, in our case, just 1%, on average, after segmentation accuracy plateaued at around 76%. These findings help avoid the computational and time costs of ineffective data augmentation.