Semantic Segmentation-Based Building Extraction in Urban Area Using Memory-Efficient Residual Dilated Convolutional Network.

Autor:	Ramalingam, Avudaiammal, George, Sam Varghese, Srivastava, Vandita, Alagala, Swarnalatha, Manickam, J. Martin Leo
Předmět:	CITIES & towns REMOTE-sensing images CONVOLUTIONAL neural networks TAX assessment DEEP learning
Zdroj:	Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Sep2024, Vol. 49 Issue 9, p12147-12166, 20p
Abstrakt:	The satellite images have been employed in building extraction to aid urban planning, tax assessment, disaster management, etc. The number of buildings and building types is huge in urban areas, which puts more burden on human experts to extract buildings in satellite images. Hence, building extraction from satellite images using deep learning (DL) has become an emerging research domain in recent decades. The performance of the DL model depends on training parameters, the depth of the model, and the memory required to preserve the model. In this work, a Memory-Efficient Residual Dilated Convolutional Network (MRDCN) has been proposed to extract buildings effectively with reduced number of training parameters and with lesser memory consumption. The model is trained using the Massachusetts buildings dataset and implemented using PyTorch in Kaggle platform. The trained model has been tested using both Massachusetts and AIRS Dataset. The simulation results prove that the proposed model uses 31.64% less memory than the existing dilated residual network. It is evident from the results that the MRDCN is able to extract the buildings with better accuracy and an Intersection of Union with minimal memory consumption than the existing standard UNet, SegNet, ResUNet, and Dilated ResUNet models. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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