WASI-AI: Synergistic Integration of AI and Physics for Retrieving Water Quality and Benthic Parameters from Multi- and Hyperspectral Images

Authors

Peter Gege (ORCID: https://orcid.org/0000-0003-0939-5267)
Milad Niroumand-Jadidi (ORCID: https://orcid.org/0000-0002-9432-3032)

Institutions

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
University of Bologna (IT)

Publication Details

Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Published: 2025-01-01
DOI: https://doi.org/10.1109/jstars.2025.3605061
Primary Topic: Hydrological Forecasting Using AI
Type: article
Field-Weighted Citation Impact: 0.00

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article

WASI-AI: Synergistic Integration of AI and Physics for Retrieving Water Quality and Benthic Parameters from Multi- and Hyperspectral Images

Peter Gege, Milad Niroumand-Jadidi

Jan 1, 2025

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Hydrological Forecasting Using AI

article

WASI-AI: Synergistic Integration of AI and Physics for Retrieving Water Quality and Benthic Parameters from Multi- and Hyperspectral Images

Peter Gege, Milad Niroumand-Jadidi

January 1, 2025article en

Abstract

No abstract available for this paper.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 18

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE), University of Bologna (IT)

Openalex Percentile: Top 79%

Hydrological Forecasting Using AI

DOI Open Access PDF OpenAlex

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WASI-AI: Synergistic Integration of AI and Physics for Retrieving Water Quality and Benthic Parameters from Multi- and Hyperspectral Images — Peter Gege, Milad Niroumand-Jadidi · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2025) | TGRS Research Map | TGRS