Land use classification has been a topic of great interest for many years. The ability to automatically recognize land cover as well as detect changes in remote sensing images is important for a variety of industries. Due in part to the size of satellite images, as well as the inefficiency of current methods, machine learning solutions are often very large. With a growing need for on device analysis, it is becoming more important to perform accurate processing without over utilizing resources. To meet this growing need, this paper presents an efficient algorithm for land use classification. The efficacy of the proposed solution is verified through studies to accurately classify remotely sensed images within the EuroSAT dataset.
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Land Use Classification Efficient Vision Transformer
Semantic Scholar · Environmental Science · 2023
Abstract
Land use classification has been a topic of great interest for many years. The ability to automatically recognize land cover as well as detect changes in remote sensing images is important for a variety of industries. Due in part to the size of satellite images, as well as the inefficiency of current methods, machine learning solutions are often very large. With a growing need for on device analysis, it is becoming more important to perform accurate processing without over utilizing resources. To meet this growing need, this paper presents an efficient algorithm for land use classification. The efficacy of the proposed solution is verified through studies to accurately classify remotely sensed images within the EuroSAT dataset.