Name
Abstract | ||
|---|---|---|
Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications [1]. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modelling is still largely impeded by scene complexity, incomplete cue extraction and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification [2]. Therefore, intelligent and innovative algorithms are in dire need for high success of automatic building extraction and modelling. This Special Issue focuses on the newly-developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D roof modelling. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/rs12030549 | REMOTE SENSING |
Field | DocType | Volume |
Remote sensing,Geology | Journal | 12 |
Issue | Citations | PageRank |
3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Awrangjeb | 1 | 0 | 0.68 |
| Xiangyun Hu | 2 | 79 | 8.87 |
| Bisheng Yang | 3 | 308 | 33.15 |
| Jiaojiao Tian | 4 | 0 | 1.01 |