004 Datenverarbeitung; Informatik
Refine
Has Fulltext
- yes (203)
Year of publication
Document Type
- Journal article (78)
- Doctoral Thesis (75)
- Working Paper (37)
- Conference Proceeding (8)
- Master Thesis (3)
- Report (2)
Language
- English (183)
- German (19)
- Multiple languages (1)
Keywords
- Datennetz (14)
- Leistungsbewertung (13)
- virtual reality (12)
- Robotik (8)
- Mobiler Roboter (7)
- Autonomer Roboter (6)
- Komplexitätstheorie (5)
- Optimierung (5)
- P4 (5)
- Simulation (5)
Institute
- Institut für Informatik (203) (remove)
Schriftenreihe
Sonstige beteiligte Institutionen
In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7%.