Publikacje

@book{ysak2024,
title = {Deep Learning Residential Building Segmentation for Evaluation of Suburban Areas Development},
author = {Agnieszka {\L}ysak and Marcin Luckner},
url = {http://dx.doi.org/10.1007/978-3-031-63783-4_9},
doi = {10.1007/978-3-031-63783-4_9},
issn = {16113349},
year = {2024},
date = {2024-01-01},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {14838 LNCS},
pages = {103--117},
publisher = {Springer Nature Switzerland},
abstract = {Deep neural network models are commonly used in computer vision problems, e.g., image segmentation. Convolutional neural networks have been state-of-the-art methods in image processing, but new architectures, such as Transformer-based approaches, have started outperforming previous techniques in many applications. However, those techniques are still not commonly used in urban analyses, mostly performed manually. This paper presents a framework for the residential building semantic segmentation architecture as a tool for automatic urban phenomena monitoring. The method could improve urban decision-making processes with automatic city analysis, which is predisposed to be faster and even more accurate than those made by human researchers. The study compares the application of new deep network architectures with state-of-the-art solutions. The analysed problem is urban functional zone segmentation for the urban sprawl evaluation using targeted land cover map construction. The proposed method monitors the expansion of the city, which, uncontrolled, can cause adverse effects. The method was tested on photos from three residential districts. The first district has been manually segmented by functional zones and used for model training and evaluation. The other two districts have been used for automated segmentation by models' inference to test the robustness of the methodology. The test resulted in 98.2% accuracy.},
keywords = {Computer vision, Deep learning, SegFormer, Semantic segmentation, Transformers},
pubstate = {published},
tppubtype = {book}
}