Machine learning can be considered as a very important area within artificial intelligence and it is characterized by algorithms and techniques that learn by examples. In the last decade, mainly due to the improvements obtained in the field of high performance computing, such as the enhanced exploitation of cloud technology and of graphics processing units (GPU), machine learning models have gained considerable progress as far as remote sensing and Earth Observation (EO) applications are concerned. However, the need of huge quantities of data necessary for the training phase, may be still a limiting factor especially in problems addressing the quantitative estimation of geo-physical parameters. In this paper, we report about the design and the development of a new platform capable of meeting the requirements of scientists and researchers who are attracted by the use of machine learning but meet difficulties in the generation of reliable data sets. The platforms relies on the implementation of radiative transfer models, plus a bunch of appropriate functionalities, in order that simulated data can be added to those available by ground-truth campaigns.
de Laurentiis, L., de Santis, D., Latini, D., Schiavon, G., Marin, A., Pace, G., et al. (2021). A New User Oriented Platform to Develop AI for the Estimation of Bio-Geophysical Parameters from EO Data. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp.262-265). New York : IEEE [10.1109/IGARSS47720.2021.9553913].
A New User Oriented Platform to Develop AI for the Estimation of Bio-Geophysical Parameters from EO Data
de Laurentiis L.;de Santis D.;Latini D.;Schiavon G.;Pace G.;Marra S.;Del Frate F.
2021-01-01
Abstract
Machine learning can be considered as a very important area within artificial intelligence and it is characterized by algorithms and techniques that learn by examples. In the last decade, mainly due to the improvements obtained in the field of high performance computing, such as the enhanced exploitation of cloud technology and of graphics processing units (GPU), machine learning models have gained considerable progress as far as remote sensing and Earth Observation (EO) applications are concerned. However, the need of huge quantities of data necessary for the training phase, may be still a limiting factor especially in problems addressing the quantitative estimation of geo-physical parameters. In this paper, we report about the design and the development of a new platform capable of meeting the requirements of scientists and researchers who are attracted by the use of machine learning but meet difficulties in the generation of reliable data sets. The platforms relies on the implementation of radiative transfer models, plus a bunch of appropriate functionalities, in order that simulated data can be added to those available by ground-truth campaigns.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
