A Learnable Model With Calibrated Uncertainty Quantification for Estimating Canopy Height From Spaceborne Sequential Imagery-Reference-Cited by-同舟云学术

A Learnable Model With Calibrated Uncertainty Quantification for Estimating Canopy Height From Spaceborne Sequential Imagery

Author:

Alagialoglou Leonidas¹^ORCID,Manakos Ioannis²^ORCID,Heurich Marco³,Cervenka Jaroslav⁴^ORCID,Delopoulos Anastasios¹

Affiliation:

1. Department of Electrical and Computer Engineering, Multimedia Understanding Group, Aristotle University of Thessaloniki, Thessaloniki, Greece

2. Centre for Research and Technology Hellas (CERTH), Information Technologies Institute, Thessaloniki, Greece

3. Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, Grafenau, Germany

4. Šumava National Park, Kašperské Hory, Czech Republic

Funder

European Union’s Horizon 2020 Research and Innovation Program

NEXTLAND

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

Reference35 articles.

1. Accurate uncertainties for deep learning using calibrated regression;kuleshov;Proc 35th Int Conf Mach Learn (ICML),2018

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