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Found 19 papers, 6 papers with code
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.
Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms
The aim of this work is to perform a multitemporal analysis using the Google Earth Engine (GEE) platform for the detection of changes in urban areas using optical data and specific machine learning (ML) algorithms.
Integration of Sentinel-1 and Sentinel-2 data for Earth surface classification using Machine Learning algorithms implemented on Google Earth Engine
In this study, Synthetic Aperture Radar (SAR) and optical data are both considered for Earth surface classification.
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).
Classification Schemes for the Radar Reference Window: Design and Comparisons
In this paper, we address the problem of classifying data within the radar reference window in terms of statistical properties.
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.
Multiple Sub-Pixel Target Detection for Hyperspectral Imaging Systems
Hyperspectral target detection is a task of primary importance in remote sensing since it allows identification, location, and discrimination of target features.
On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
This article aims to investigate how circuit-based hybrid Quantum Convolutional Neural Networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing.
On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery
In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios.
Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model
Cloud removal is a relevant topic in Remote Sensing as it fosters the usability of high-resolution optical images for Earth monitoring and study.
Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification
Data fusion is a well-known technique, becoming more and more popular in the Artificial Intelligence for Earth Observation (AI4EO) domain mainly due to its ability of reinforcing AI4EO applications by combining multiple data sources and thus bringing better results.
A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networks
In recent years, machine learning (ML) algorithms have become widespread in all the fields of remote sensing (RS) and earth observation (EO).
Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms.
A New Mask R-CNN Based Method for Improved Landslide Detection
This paper presents a novel method of landslide detection by exploiting the Mask R-CNN capability of identifying an object layout by using a pixel-based segmentation, along with transfer learning used to train the proposed model.
Monitoring of Critical Infrastructures by Micro-Motion Estimation: the Mosul Dam Destabilization
In this paper, authors propose a new procedure to provide a tool for monitoring critical infrastructures.
