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Smart city article details

Title	Semantic Segmentation Of Remote Sensing Images Of Urban Regions Using Deep Learning Methods
ID_Doc	48263
Authors	Vetsa P.; Buddharaju A.; Dasari V.; Bogila Y.; Sundaresan A.A.; Paul M.B.
Year	2025
Published	AIP Conference Proceedings, 3291, 1
DOI	http://dx.doi.org/10.1063/5.0269084
Abstract	As our world is attempting to step into a better innovative future, the need for smart cities is demanding. Key components of a smart city include environmental surveillance, advanced navigation, planning of urban areas, and the management of traffic, among others. The process of semantic segmentation of remote sensing images is essential for the evolution and expansion of smart cities, providing a highly effective method for accurately mapping geographical regions. In the pursuit of accurately detecting and segmenting specific regions on land, the collection of relevant data is as important as it sounds. Satellite imagery emerges as a paramount source, providing detectable insights for the detection and segmentation of areas. By harnessing the rich details in satellite images, precise information can be drawn. This approach allows us for a good analysis of the urban area. Given that the U-NET is currently a leading approach for accurate detection and segmentation, there is a growing trend to enhance its MIOU and overall performance by incorporating transformers into the model. This integration attempts to increase the model's precision and aid in its general improvement. © 2025 Author(s).
Author Keywords	Environmental Monitoring; Remote Sensing; Satellite Imagery; Semantic Segmentation; Traffic Management; Urban Planning

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