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Found 8 papers, 1 papers with code
AI techniques for near real-time monitoring of contaminants in coastal waters on board future Phisat-2 mission
Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sensing (RS) data, Artificial Intelligence (AI) techniques, and onboard processing.
QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering
The use of Synthetic Aperture Radar (SAR) has greatly advanced our capacity for comprehensive Earth monitoring, providing detailed insights into terrestrial surface use and cover regardless of weather conditions, and at any time of day or night.
A Hybrid MLP-Quantum approach in Graph Convolutional Neural Networks for Oceanic Nino Index (ONI) prediction
This paper explores an innovative fusion of Quantum Computing (QC) and Artificial Intelligence (AI) through the development of a Hybrid Quantum Graph Convolutional Neural Network (HQGCNN), combining a Graph Convolutional Neural Network (GCNN) with a Quantum Multilayer Perceptron (MLP).
Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data
For this, our study pioneers a novel approach to monitor the Turbidity contaminant, integrating CatBoost Machine Learning (ML) with high-resolution data from Sentinel-2 Level-2A.
Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies mapping
Climate change is intensifying extreme weather events, causing both water scarcity and severe rainfall unpredictability, and posing threats to sustainable development, biodiversity, and access to water and sanitation.
Estimation of Ground NO2 Measurements from Sentinel-5P Tropospheric Data through Categorical Boosting
This study aims to analyse the Nitrogen Dioxide (NO2) pollution in the Emilia Romagna Region (Northern Italy) during 2019, with the help of satellite retrievals from the Sentinel-5P mission of the European Copernicus Programme and ground-based measurements, obtained from the ARPA site (Regional Agency for the Protection of the Environment).
A Machine Learning Approach to Long-Term Drought Prediction using Normalized Difference Indices Computed on a Spatiotemporal Dataset
Climate change and increases in drought conditions affect the lives of many and are closely tied to global agricultural output and livestock production.
SEN2DWATER: A Novel Multispectral and Multitemporal Dataset and Deep Learning Benchmark for Water Resources Analysis
Climate change has caused disruption in certain weather patterns, leading to extreme weather events like flooding and drought in different parts of the world.
