Privacy-Preserving Soil Data: Federated Learning for Topsoil Descriptors Via Remote Sensing

Nikolaos Tsakiridis; Eleni Kalopesa; Nikolaos Tziolas; Nick Berkvens; Theodoros Chalazas; Tuna Coppens; Panagiotis Ilias

doi:10.1109/IGARSS55030.2025.11242409

Privacy-Preserving Soil Data: Federated Learning for Topsoil Descriptors Via Remote Sensing

Nikolaos Tsakiridis
, Eleni Kalopesa
, Nikolaos Tziolas
, Nick Berkvens
, Theodoros Chalazas
, Tuna Coppens
, Panagiotis Ilias

Technologie en Voeding

Onderzoeksoutput: Bijdrage aan congres › C3: Congres - Meeting abstract › peer review

Uittreksel

This paper explores the application of federated learning in soil science to address the challenges of data privacy and collaboration. Leveraging decentralized machine learning, we demonstrate how to predict soil properties effectively while preserving privacy. Our study focuses on cropland data from Flanders, Belgium, and Central Macedonia, Greece, and utilizes the LUCAS topsoil database and Copernicus Sentinel-2 data as an initial model. The results showcase the potential of federated learning combined with Earth observation to advance soil monitoring and sustainable land management.

Oorspronkelijke taal	Engels
DOI's	https://doi.org/10.1109/IGARSS55030.2025.11242409
Publicatiestatus	Gepubliceerd - 5-aug-2025
Evenement	IEEE International Geoscience and Remote Sensing Symposium 2025 - Brisbane, Australië Duur: 3-aug-2025 → 8-aug-2025 https://2025.ieeeigarss.org/

Congres

Congres	IEEE International Geoscience and Remote Sensing Symposium 2025
Land/Regio	Australië
Stad	Brisbane
Periode	3/08/25 → 8/08/25
Internet adres	https://2025.ieeeigarss.org/

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10.1109/IGARSS55030.2025.11242409

SCALEAGDATA: Opschalen van landbouwdata voor een betere monitoring van agro milieuomstandigheden
Vangeyte, J. (Projectbegeleider), Berkvens, N. (Onderzoeker), Coppens, T. (Projectverantwoordelijke), Chalazas, T. (Onderzoeker), Van Loo, K. (Onderzoeker), Saberioon, M. (Voormalig Onderzoeker), Ilias, P. (Voormalig Onderzoeker), De Man, W. (Onderzoeker) & Bauwens, J. (Onderzoeker)
1/01/23 → 31/12/26
Project: Onderzoek

Dit citeren

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title = "Privacy-Preserving Soil Data: Federated Learning for Topsoil Descriptors Via Remote Sensing",

abstract = "This paper explores the application of federated learning in soil science to address the challenges of data privacy and collaboration. Leveraging decentralized machine learning, we demonstrate how to predict soil properties effectively while preserving privacy. Our study focuses on cropland data from Flanders, Belgium, and Central Macedonia, Greece, and utilizes the LUCAS topsoil database and Copernicus Sentinel-2 data as an initial model. The results showcase the potential of federated learning combined with Earth observation to advance soil monitoring and sustainable land management.",

author = "Nikolaos Tsakiridis and Eleni Kalopesa and Nikolaos Tziolas and Nick Berkvens and Theodoros Chalazas and Tuna Coppens and Panagiotis Ilias",

year = "2025",

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doi = "10.1109/IGARSS55030.2025.11242409",

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T1 - Privacy-Preserving Soil Data: Federated Learning for Topsoil Descriptors Via Remote Sensing

AU - Tsakiridis, Nikolaos

AU - Kalopesa, Eleni

AU - Tziolas, Nikolaos

AU - Berkvens, Nick

AU - Chalazas, Theodoros

AU - Coppens, Tuna

AU - Ilias, Panagiotis

PY - 2025/8/5

Y1 - 2025/8/5

N2 - This paper explores the application of federated learning in soil science to address the challenges of data privacy and collaboration. Leveraging decentralized machine learning, we demonstrate how to predict soil properties effectively while preserving privacy. Our study focuses on cropland data from Flanders, Belgium, and Central Macedonia, Greece, and utilizes the LUCAS topsoil database and Copernicus Sentinel-2 data as an initial model. The results showcase the potential of federated learning combined with Earth observation to advance soil monitoring and sustainable land management.

AB - This paper explores the application of federated learning in soil science to address the challenges of data privacy and collaboration. Leveraging decentralized machine learning, we demonstrate how to predict soil properties effectively while preserving privacy. Our study focuses on cropland data from Flanders, Belgium, and Central Macedonia, Greece, and utilizes the LUCAS topsoil database and Copernicus Sentinel-2 data as an initial model. The results showcase the potential of federated learning combined with Earth observation to advance soil monitoring and sustainable land management.

U2 - 10.1109/IGARSS55030.2025.11242409

DO - 10.1109/IGARSS55030.2025.11242409

M3 - C3: Conference - meeting abstract

T2 - IEEE International Geoscience and Remote Sensing Symposium 2025

Y2 - 3 August 2025 through 8 August 2025

ER -

Privacy-Preserving Soil Data: Federated Learning for Topsoil Descriptors Via Remote Sensing

Uittreksel

Congres

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Vingerafdruk

Projecten

SCALEAGDATA: Opschalen van landbouwdata voor een betere monitoring van agro milieuomstandigheden

Dit citeren