Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Stefano D'Aronco"'
Autor:
Riccardo de Lutio, John Y. Park, Kimberly A. Watson, Stefano D'Aronco, Jan D. Wegner, Jan J. Wieringa, Melissa Tulig, Richard L. Pyle, Timothy J. Gallaher, Gillian Brown, Gordon Guymer, Andrew Franks, Dhahara Ranatunga, Yumiko Baba, Serge J. Belongie, Fabián A. Michelangeli, Barbara A. Ambrose, Damon P. Little
Publikováno v:
Frontiers in Plant Science, Vol 12 (2022)
Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all–important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the dom
Externí odkaz:
https://doaj.org/article/ad8c544a72304e2f95ee3fecab90fb13
Autor:
Priyanka Chaudhary, João P. Leitão, Tabea Donauer, Stefano D’Aronco, Nathanaël Perraudin, Guillaume Obozinski, Fernando Perez-Cruz, Konrad Schindler, Jan Dirk Wegner, Stefania Russo
Publikováno v:
Water, Vol 14, Iss 19, p 2980 (2022)
We propose a probabilistic deep learning approach for the prediction of maximum water depth hazard maps at high spatial resolutions, which assigns well-calibrated uncertainty estimates to every predicted water depth. Efficient, accurate, and trustwor
Externí odkaz:
https://doaj.org/article/e10d301f4a2b4123b6d7a8b441019a0b
Autor:
Endrit Shehaj, Luca Miotti, Alain Geiger, Stefano D'Aronco, Jan D. Wegner, Gregor Moeller, Benedikt Soja, Markus Rothacher
Publikováno v:
Acta Astronautica, 204
Scopus-Elsevier
Scopus-Elsevier
Signals used for Earth observation, when travelling through the atmosphere, are sensitive to refractivity; especially high spatio-temporal variations of water vapor are difficult to model and correct. Remaining unmodeled tropospheric delays deteriora
Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach
Publikováno v:
Remote Sensing, Vol 14, Iss 1, p 17 (2021)
Long-term Global Navigation Satellite System (GNSS) height residual time series contain signals that are related to environmental influences. A big part of the residuals can be explained by environmental surface loadings, expressed through physical m
Externí odkaz:
https://doaj.org/article/e6d74f657070412bac0aa06feb43a62d
Publikováno v:
Remote Sensing, Vol 13, Iss 21, p 4302 (2021)
Typhoon Goni crossed several provinces in the Philippines where agriculture has high socioeconomic importance, including the top-3 provinces in terms of planted coconut trees. We have used a computational model to infer coconut tree density from sate
Externí odkaz:
https://doaj.org/article/fdabab2cefcd4a1ebe5230cb356e3938
Autor:
Nicholus Mboga, Stefano D’Aronco, Tais Grippa, Charlotte Pelletier, Stefanos Georganos, Sabine Vanhuysse, Eléonore Wolff, Benoît Smets, Olivier Dewitte, Moritz Lennert, Jan Dirk Wegner
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 10, Iss 8, p 523 (2021)
Multitemporal environmental and urban studies are essential to guide policy making to ultimately improve human wellbeing in the Global South. Land-cover products derived from historical aerial orthomosaics acquired decades ago can provide important e
Externí odkaz:
https://doaj.org/article/85681d387c66413c9526869838b32321
Autor:
Jan Dirk Wegner, Konrad Schindler, Stefania Russo, Stefano D'Aronco, Yihang She, Riccardo de Lutio, Philipp Brun
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing, 182
Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts. However, classifying plant specimens based on image data alone is challenging:
Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach
Publikováno v:
Remote Sensing, Vol 14, Iss 17, p 17 (2022)
Remote Sensing, 14 (1)
Remote Sensing, 14 (1)
Long-term Global Navigation Satellite System (GNSS) height residual time series contain signals that are related to environmental influences. A big part of the residuals can be explained by environmental surface loadings, expressed through physical m
Autor:
Russo, Priyanka Chaudhary, João P. Leitão, Tabea Donauer, Stefano D’Aronco, Nathanaël Perraudin, Guillaume Obozinski, Fernando Perez-Cruz, Konrad Schindler, Jan Dirk Wegner, Stefania
Publikováno v:
Water; Volume 14; Issue 19; Pages: 2980
We propose a probabilistic deep learning approach for the prediction of maximum water depth hazard maps at high spatial resolutions, which assigns well-calibrated uncertainty estimates to every predicted water depth. Efficient, accurate, and trustwor
Optical satellite sensors cannot see the Earth's surface through clouds. Despite the periodic revisit cycle, image sequences acquired by Earth observation satellites are therefore irregularly sampled in time. State-of-the-art methods for crop classif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5c79c162bada141c421a0aa64c0bed1