Name
Abstract | ||
|---|---|---|
We have created a deep-learning-based river network extraction model, called DeepRiver, that learns the characteristics of rivers from synthetic data and generalizes them to natural data. To train this model, we created a very large database of exemplary synthetic local channel segments, including channel intersections. Our model uses a special loss function that automatically shifts the focus to ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LGRS.2018.2811754 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Rivers,Adaptation models,Remote sensing,Indexes,Training,Oceans,Computational modeling | Computer vision,Pattern recognition,Very large database,Communication channel,Synthetic data,Extractor,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
15 | 6 | 1545-598X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Furkan Isikdogan | 1 | 5 | 1.57 |
| Alan C. Bovik | 2 | 5062 | 349.55 |
| Paola Passalacqua | 3 | 10 | 2.73 |