Name
Title | ||
|---|---|---|
Classifying land-use patterns by integrating time-series electricity data and high-spatial resolution remote sensing imagery |
Abstract | ||
|---|---|---|
•A deep learning model (TR-CNN) for land-use classification at fine scale is proposed.•TR-CNN can fuse multi-source features from HSR and electricity data.•Electricity data is first proved to reflect socioeconomic features of land use.•TR-CNN obtained 0.934 accuracy, which can accurately identify land-use types.•TR-CNN can sense land-use patterns from both “top-down” and “bottom-up” recognition. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.jag.2021.102664 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | DocType | Volume |
Urban land-use classification,Time-series electricity data,High-spatial resolution images,Feature fusion,Deep learning,TR-CNN | Journal | 106 |
ISSN | Citations | PageRank |
1569-8432 | 2 | 0.40 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao Yao | 1 | 2 | 0.40 |
| Xiaoqin Yan | 2 | 2 | 0.40 |
| Peng Luo | 3 | 2 | 0.40 |
| Yuyun Liang | 4 | 2 | 0.40 |
| Shuliang Ren | 5 | 2 | 1.42 |
| Ying Hu | 6 | 2 | 0.40 |
| Jian Han | 7 | 2 | 0.40 |
| Qingfeng Guan | 8 | 16 | 8.64 |