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Smart city article details
| Title | Deep Siamese Network For Annual Change Detection In Beijing Using Landsat Satellite Data |
|---|---|
| ID_Doc | 18080 |
| Authors | Bao H.; Zerres V.H.D.; Lehnert L.W. |
| Year | 2024 |
| Published | International Journal of Applied Earth Observation and Geoinformation, 130 |
| DOI | http://dx.doi.org/10.1016/j.jag.2024.103897 |
| Abstract | The detection of spatiotemporal changes in land use/land cover (LULC-SC) plays a paramount role in the analysis of smart cities, it can describe complex urban distribution, functions, and patterns. Chinas capital city has been developing rapidly in the past two decades, however, there are only few long-term studies on an annual scale of LULC-SC. To fill this research gap, we propose a remote sensing parallel framework for the detection of LULC-SC based on the combination of the Deep Siamese Network and long time series, which focuses on the spatial semantic information at the object level. A Landsat time series from 2002 to 2022 serves as input satellite data. First, we focus on building graph constructions at the object level and then use an autonomously constructed deeper-feature graph convolutional network (DF-GCN) to mine deeper features, spatial semantic, and relationships at the object level. Finally, the Siamese Network recognizes the changes in the spatial semantic tensors of long time series of Landsat images and quickly maps LULC-SC. The results prove that the proposed spatiotemporal change detection framework is effective in LULC-SC in Beijing. Compared with other networks, the optimal accuracy of semantic mining based on DF-GCN can reach about 90%. Over the past two decades, the LULC-SC of Beijing has changed in a complex way, with urbanization occurring primarily through the replacement of farmland. Consequently, the proposed framework can generate accurate LULC change maps at high temporal frequencies, which can contribute to a better comprehension of sustainable urban development and planning. © 2024 The Author(s) |
| Author Keywords | Deep Learning; DF-GCN; Object-level semantic information; Remote sensing; Spatio-temporal change |
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