ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-X-4-W1-2022-9-2023 BUILDING DETECTION FROM AERIAL IMAGERY USING INCEPTION RESNET UNET AND UNET ARCHITECTURES Aghayari S. 1 Hadavand A. 1 Mohamadnezhad Niazi S. 1 Omidalizarandi M. 2 Photogrammetry and Computer Vision Department, Ideh Pardazan Tosseah co, Tehran, Iran Geodetic Institute, Leibniz University Hannover, Germany 13 01 2023 X-4/W1-2022 9 17 Copyright: © 2023 S. Aghayari et al. 2023 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/X-4-W1-2022/9/2023/isprs-annals-X-4-W1-2022-9-2023.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/X-4-W1-2022/9/2023/isprs-annals-X-4-W1-2022-9-2023.pdf

Buildings are one of the key components in change detection, urban planning, and monitoring. The automatic extraction of the building from high-resolution aerial imagery is still challenging due to the variations in their shapes, structures, textures, and colours. Recently, the convolutional neural networks (CNN) show a significant improvement in object detection and extraction that surpasses other methods. To extract building, in this paper two segmentation architectures, the UNet and the Inception ResNet UNet are implemented and then tested on the Inria aerial image datasets. The Inception ResNet UNet utilizes the Inception architecture and residual blocks. This makes the model wide and deep, though there are a few differences between numbers of UNet and Inception ResNet UNet parameters. The analyses show that UNet has a high rate of metrics in the training progress. However, on the unseen dataset, Inception ResNet UNet extracts buildings more accurately (97.95% accuracy and 0.96 in the dice metric) in comparison with UNet (94.30% accuracy and 0.55 in the dice metric).