Our Work
Results of Collaborative Research
This page presents the key results of the project, including developed methods, tools, publications, and other achievements. They are presented clearly and accessibly for further use and enhancement.
Peer-Reviewed Scientific Articles in Journals
- Potočnik Buhvald, A., Oštir, K., Skudnik, M. (2026). Interannual variability of European beech spring phenology across elevation gradients derived from Sentinel-2 time series. Ecological Informatics, 97, https://doi.org/10.1016/j.ecoinf.2026.103934
Scientific Monograph
- Stančič, L., Oštir, K., Kokalj, Ž. (2026). Sub-pixel mapping for change detection in fluvial environments. Ljubljana: Založba ZRC, 2026. Prostor, kraj, čas, 23. https://doi.org/10.3986/9789610510949
Published Conference Papers
- Gerčer, M., Grabrijan, T., Tekavec, J., Lisec, A., Oštir, K. (2026). Near Real-Time Flood Mapping from Sentinel Data Using Machine Learning Techniques. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B3-2026, 797–803, https://doi.org/10.5194/isprs-archives-XLIX-B3-2026-797-2026.
- Gorup, G., Bohak, C. (2026). Visualization of temporal changes in environmental point cloud scans. In: EnvirVis 2026 : Workshop on Visualisation in Environmental Sciences : Nottingham, UK, June 8– 12, 2026. Graz: Eurographics Association, cop. 2026. Pp. 1-7, https://doi.org/10.2312/envir.20261004
- Gorup, G., Bohak, C. (2026). Volume change analysis in environmental point clouds. In: WSCG 2026 : 34. International Conference on Computer Graphics, Visualization and Computer Vision, May 26 – 28, 2026, Plzen, Czech Republic. http://wscg.zcu.cz/wscg2026/Papers/2026_E73-full.PDF
- Stojanović, L., Lisec, A., Oštir, K., Fetai, B. (2026). Detection of Cropland Abandonment through Multi-Temporal Landsat Data and Spatially Independent Machine Learning Validation. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B3-2026, 679–688, https://doi.org/10.5194/isprs-archives-XLIX-B3-2026-679-2026
- Wolf, F., Rolih, B., Čehovin Zajc, L. (2026). Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 27815-27826. https://openaccess.thecvf.com/content/CVPR2026/html/Wolf_Brewing_Stronger_Features_Dual-Teacher_Distillation_for_Multispectral_Earth_Observation_CVPR_2026_paper.html, https://github.com/wolfilip/DEO-FM
Published Conference Abstracts
- Jemec Auflič, M., Maček, M., Smolar, J., Kure, K., Peternel, T., Grčman, H., Turniški, R., Zupan., M., Žvokelj, L., Pulko, B. (2026). Framework for early detection and characterisation of hydraulically induced shallow landslides. In: EGU General Assembly 2026 : Vienna, Austria & Online, 3–8 May 2026. EGU – European Geosciences Union, https://doi.org/10.5194/egusphere-egu26-9556
