Advancing Data Fusion in Earth Sciences

Dalton Lunga; Philipe Dias

doi:10.1109/IGARSS46834.2022.9883176

Advancing Data Fusion in Earth Sciences

Dalton Lunga, Philipe Dias

GeoAI

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Artificial intelligence (AI) algorithms have proven to be quite effective in Earth observation applications, often, when extensive amounts of representative training data are available. At large, processing large volumes of observation data can be challenging due to a myriad of reasons that include the cost of acquiring labeled samples, computing resources, identifying critical data features for model prototyping, standardization of model building, and deployment. Practical novel tools and approaches are emerging across different communities. In this paper, we discuss several such recent methods from machine learning and share lessons from advanced, scalable workflows that could impact the advancement of multimodal data fusion for Earth Science applications.

Original language	English
Title of host publication	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5077-5080
Number of pages	4
ISBN (Electronic)	9781665427920
DOIs	https://doi.org/10.1109/IGARSS46834.2022.9883176
State	Published - 2022
Event	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia Duration: Jul 17 2022 → Jul 22 2022

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2022-July

Conference

Conference	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	07/17/22 → 07/22/22

Funding

We acknowledge that this manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Earth sciences
data and model distillation
datasheets
model catalog
no-code platform
search and retrieval

Access to Document

10.1109/IGARSS46834.2022.9883176

Cite this

@inproceedings{cf301d22a0464c228da04624195e3862,

title = "Advancing Data Fusion in Earth Sciences",

abstract = "Artificial intelligence (AI) algorithms have proven to be quite effective in Earth observation applications, often, when extensive amounts of representative training data are available. At large, processing large volumes of observation data can be challenging due to a myriad of reasons that include the cost of acquiring labeled samples, computing resources, identifying critical data features for model prototyping, standardization of model building, and deployment. Practical novel tools and approaches are emerging across different communities. In this paper, we discuss several such recent methods from machine learning and share lessons from advanced, scalable workflows that could impact the advancement of multimodal data fusion for Earth Science applications.",

keywords = "Earth sciences, data and model distillation, datasheets, model catalog, no-code platform, search and retrieval",

author = "Dalton Lunga and Philipe Dias",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",

year = "2022",

doi = "10.1109/IGARSS46834.2022.9883176",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5077--5080",

booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",

}

Lunga, D & Dias, P 2022, Advancing Data Fusion in Earth Sciences. in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2022-July, Institute of Electrical and Electronics Engineers Inc., pp. 5077-5080, 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022, Kuala Lumpur, Malaysia, 07/17/22. https://doi.org/10.1109/IGARSS46834.2022.9883176

Advancing Data Fusion in Earth Sciences. / Lunga, Dalton ; Dias, Philipe.
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2022. p. 5077-5080 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Advancing Data Fusion in Earth Sciences

AU - Lunga, Dalton

AU - Dias, Philipe

PY - 2022

Y1 - 2022

N2 - Artificial intelligence (AI) algorithms have proven to be quite effective in Earth observation applications, often, when extensive amounts of representative training data are available. At large, processing large volumes of observation data can be challenging due to a myriad of reasons that include the cost of acquiring labeled samples, computing resources, identifying critical data features for model prototyping, standardization of model building, and deployment. Practical novel tools and approaches are emerging across different communities. In this paper, we discuss several such recent methods from machine learning and share lessons from advanced, scalable workflows that could impact the advancement of multimodal data fusion for Earth Science applications.

AB - Artificial intelligence (AI) algorithms have proven to be quite effective in Earth observation applications, often, when extensive amounts of representative training data are available. At large, processing large volumes of observation data can be challenging due to a myriad of reasons that include the cost of acquiring labeled samples, computing resources, identifying critical data features for model prototyping, standardization of model building, and deployment. Practical novel tools and approaches are emerging across different communities. In this paper, we discuss several such recent methods from machine learning and share lessons from advanced, scalable workflows that could impact the advancement of multimodal data fusion for Earth Science applications.

KW - Earth sciences

KW - data and model distillation

KW - datasheets

KW - model catalog

KW - no-code platform

KW - search and retrieval

UR - http://www.scopus.com/inward/record.url?scp=85140363180&partnerID=8YFLogxK

U2 - 10.1109/IGARSS46834.2022.9883176

DO - 10.1109/IGARSS46834.2022.9883176

M3 - Conference contribution

AN - SCOPUS:85140363180

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 5077

EP - 5080

BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022

Y2 - 17 July 2022 through 22 July 2022

ER -

Advancing Data Fusion in Earth Sciences

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this