1 code implementation • Proceedings of the VLDB Endowment 2022 • Matteo Paganelli, Francesco Del Buono, Andrea Baraldi, Francesco Guerra
State-of-the-art Entity Matching (EM) approaches rely on transformer architectures, such as BERT, for generating highly contex-tualized embeddings of terms.
no code implementations • 16 Jan 2017 • Andrea Baraldi
The proposed Earth observation (EO) based value adding system (EO VAS), hereafter identified as AutoCloud+, consists of an innovative EO image understanding system (EO IUS) design and implementation capable of automatic spatial context sensitive cloud/cloud shadow detection in multi source multi spectral (MS) EO imagery, whether or not radiometrically calibrated, acquired by multiple platforms, either spaceborne or airborne, including unmanned aerial vehicles (UAVs).
no code implementations • 8 Jan 2017 • Andrea Baraldi, João V. B. Soares
These two published sets of intuitive geometric features were selected as initial conditions by the present R&D software project, whose multi-objective goal was to accomplish: (i) a minimally dependent and maximally informative design (knowledge/information representation) of a general purpose, user and application independent dictionary of 2D shape terms provided with a physical meaning intuitive to understand by human end users and (ii) an effective (accurate, scale invariant, easy to use) and efficient implementation of 2D shape descriptors.
no code implementations • 8 Jan 2017 • Andrea Baraldi, Dirk Tiede, Stefan Lang
Collected outcome and process quality indicators, including degree of automation, computational efficiency, VQ rate and VQ error, are consistent with theoretical expectations.
no code implementations • 8 Jan 2017 • Andrea Baraldi, Francesca Despini, Sergio Teggi
Unfortunately, to date, no standard evaluation procedure for MS image PAN sharpening outcome and process is community agreed upon, in contrast with the Quality Assurance Framework for Earth Observation (QA4EO) guidelines proposed by the intergovernmental Group on Earth Observations (GEO).
no code implementations • 8 Jan 2017 • Andrea Baraldi, Michael Laurence Humber, Dirk Tiede, Stefan Lang
The present Part 2 Validation presents and discusses Stage 4 Val results collected from the test SIAM WELD map time series and the reference NLCD map by an original protocol for wall to wall thematic map quality assessment without sampling, where the test and reference map legends can differ in agreement with the Part 1.
no code implementations • 8 Jan 2017 • Andrea Baraldi, Michael Laurence Humber, Dirk Tiede, Stefan Lang
Conclusions are that the SIAM-WELD maps instantiate a Level 2 SCM product whose legend is the 4 class taxonomy of the FAO Land Cover Classification System at the Dichotomous Phase Level 1 vegetation/nonvegetation and Level 2 terrestrial/aquatic.