A systematic review of machine learning techniques for GNSS use cases
A Siemuri, K Selvan, H Kuusniemi… - … on Aerospace and …, 2022 - ieeexplore.ieee.org
In terms of the availability and accuracy of positioning, navigation, and timing (PNT), the
traditional Global Navigation Satellite System (GNSS) algorithms and models perform well …
traditional Global Navigation Satellite System (GNSS) algorithms and models perform well …
[HTML][HTML] Advancing Arctic sea ice remote sensing with AI and deep learning: Opportunities and challenges
Revolutionary advances in artificial intelligence (AI) in the past decade have brought
transformative innovation across science and engineering disciplines. In the field of Arctic …
transformative innovation across science and engineering disciplines. In the field of Arctic …
FSSCat: The Federated Satellite Systems 3Cat Mission: Demonstrating the capabilities of CubeSats to monitor essential climate variables of the water cycle …
A Camps, JF Munoz-Martin… - … and Remote Sensing …, 2022 - ieeexplore.ieee.org
The Federated Satellite Systems/3 Cat-5 (FSSCat) mission was the winner of the European
Space Agency (ESA) Sentinel Small Satellite (S 3) Challenge and overall winner of the 2017 …
Space Agency (ESA) Sentinel Small Satellite (S 3) Challenge and overall winner of the 2017 …
Sea surface salinity and wind speed retrievals using GNSS-R and L-band microwave radiometry data from FMPL-2 onboard the FSSCat mission
JF Munoz-Martin, A Camps - Remote Sensing, 2021 - mdpi.com
The Federated Satellite System mission (FSSCat), winner of the 2017 Copernicus Masters
Competition and the first ESA third-party mission based on CubeSats, aimed to provide …
Competition and the first ESA third-party mission based on CubeSats, aimed to provide …
Ocean Remote Sensing Using Spaceborne GNSSReflectometry: A Review
Spaceborne global navigation satellite system reflectometry (GNSS-R) is an emerging
remote sensing technology that utilizes Earth surface reflections of GNSS signals to monitor …
remote sensing technology that utilizes Earth surface reflections of GNSS signals to monitor …
Retrieval and assessment of significant wave height from CYGNSS mission using neural network
In this study, we investigate sea state estimation from spaceborne GNSS-R. Due to the
complex scattering of electromagnetic waves on the rough sea surface, the neural network …
complex scattering of electromagnetic waves on the rough sea surface, the neural network …
[HTML][HTML] Spaceborne GNSS-R for sea ice classification using machine learning classifiers
The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate
change. This study develops a new sea ice classification approach based on machine …
change. This study develops a new sea ice classification approach based on machine …
Application of Deep Learning in Forest Fire Prediction: A Systematic Review
Forests are among the world's most valuable ecological resources. However, they face
significant threats from Forest Fires (FFs), causing ecological damage and impacting wildlife …
significant threats from Forest Fires (FFs), causing ecological damage and impacting wildlife …
Advancing Arctic sea ice remote sensing with AI and deep learning: now and future
The revolutionary advances of Artificial Intelligence (AI) in the past decade have brought
transformative innovation across science and engineering disciplines. Also in the field of …
transformative innovation across science and engineering disciplines. Also in the field of …
Retrieval of sea ice thickness from FY-3E data using Random Forest method
In this study, we employ a Random Forest approach to estimate sea ice thickness (SIT)
using Fengyun-3E (FY-3E) and Soil Moisture Ocean Salinity (SMOS) data. This method …
using Fengyun-3E (FY-3E) and Soil Moisture Ocean Salinity (SMOS) data. This method …