Publications

Journal and Magazine Articles

Deep Neural Network Regression for Normalized Digital Surface Model Generation with Sentinel-2 Imagery Müller, Konstantin; Leppich, Robert; Geiß, Christian; Borst, Vanessa; Pelizari, Patrick Aravena; Kounev, Samuel; in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023). 1–14.
- [ Abstract ]
- [ BibTeX ]
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 normalized digital surface models for extensive areas. This paper 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 pixel-wise regression, our U-Net base models can achieve a mean height error of approximately two meters. Moreover, through our enhancements to the model architecture, we reduce the model error by more than seven percent.

@article{10189905, abstract = {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 normalized digital surface models for extensive areas. This paper 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 pixel-wise regression, our U-Net base models can achieve a mean height error of approximately two meters. Moreover, through our enhancements to the model architecture, we reduce the model error by more than seven percent.}, author = {Müller, Konstantin and Leppich, Robert and Geiß, Christian and Borst, Vanessa and Pelizari, Patrick Aravena and Kounev, Samuel}, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, keywords = {myown}, pages = {1-14}, title = {Deep Neural Network Regression for Normalized Digital Surface Model Generation with Sentinel-2 Imagery}, year = 2023 }

Full Papers

Personal Task Design Preferences of Crowdworkers Hirth, Matthias; Borchert, Kathrin; de Moor, Katrien; Borst, Vanessa; Hoßfeld, Tobias; in 12th International Conference on Quality of Multimedia Experience (QoMEX) (2020). Athlone, Ireland.
- [ BibTeX ]
@inproceedings{info3-inproceedings-2020-5, address = {Athlone, Ireland}, author = {Hirth, Matthias and Borchert, Kathrin and de Moor, Katrien and Borst, Vanessa and Hoßfeld, Tobias}, booktitle = {12th International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {t_full}, month = 5, title = {Personal Task Design Preferences of Crowdworkers}, year = 2020 }

Posters

Segmentierung und Verlaufskontrolle chronischer Wunden durch künstliche Intelligenz mithilfe einer mobilen App (WUNDERKINT Studie) Borst, Vanessa; Dege, Tassilo; Grän, Franziska; Hamrouni, Sandra; Michel, Daniel; Hager, Valerie; Goebeler, Matthias; Leppich, Robert; Kounev, Samuel; Schmieder, Astrid; in Allergologie. JDDG: Journal der Deutschen Dermatologischen Gesellschaft (2023). (Vol. 21) 28–177. John Wiley & Sons Ltd.

Poster Abstract (No. P322, pp. 175-176)
- [ BibTeX ]
- [ URL ]
- [ Download ]
@incollection{borst_ddg23_v2, author = {Borst, Vanessa and Dege, Tassilo and Grän, Franziska and Hamrouni, Sandra and Michel, Daniel and Hager, Valerie and Goebeler, Matthias and Leppich, Robert and Kounev, Samuel and Schmieder, Astrid}, booktitle = {Allergologie. JDDG: Journal der Deutschen Dermatologischen Gesellschaft}, keywords = {myown}, month = {April}, note = {Poster Abstract (No. P322, pp. 175-176)}, number = {S1}, pages = {28-177}, publisher = {John Wiley & Sons Ltd.}, title = {Segmentierung und Verlaufskontrolle chronischer Wunden durch künstliche Intelligenz mithilfe einer mobilen App (WUNDERKINT Studie)}, volume = 21, year = 2023 }

Press Coverage

Main-Post (Press Release im Kontext der Verleihung des "Sonderforschungsförderpreises Medizin 2022" für das interdisziplinäre Forschungsprojekt "Wunderkint"): Großes Festkonzert des Universitätsbundes in der Neubaukirche mit Preisverleihung. November 2022. [ Link ]

Journal and Magazine Articles

Full Papers

Posters

Press Coverage

Hinweis zum Datenschutz

Hinweis zum Datenschutz

Bildnachweise