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Earth Observation

Earth Observation (EO) refers to the use of remote sensing technologies to monitor land, marine (seas, rivers, lakes) and atmosphere. Satellite-based EO relies on the use of satellite-mounted payloads to gather imaging data about the Earth’s characteristics. The images are then processed and analyzed in order to extract different types of information that can serve a very wide range of applications and industries.

Papers

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Showing 426–450 of 518 papers

TitleStatusHype
Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover ClassificationCode0
EuroCrops: A Pan-European Dataset for Time Series Crop Type Classification—0
Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions—0
Measuring economic activity from space: a case study using flying airplanes and COVID-19Code0
A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural NetworksCode0
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions—0
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion—0
Big Plastic Masses Detection using Sentinel 2 Images—0
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems—0
Energy-based Models for Earth Observation Applications—0
Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a general-purpose sensor-fusion semantic embedding model—0
ICEYE microsatellite SAR constellation status update: Evaluation of first commercial imaging modes—0
Bidirectional Multi-scale Attention Networks for Semantic Segmentation of Oblique UAV ImageryCode0
Towards Natural Language Question Answering over Earth Observation Linked Data using Attention-based Neural Machine Translation—0
Class Attention Network for Semantic Segmentation of Remote Sensing Images—0
Learning Structures in Earth Observation Data with Gaussian Processes—0
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation—0
Pattern Recognition Scheme for Large-Scale Cloud Detection over Landmarks—0
Gradient-based Automatic Look-Up Table Generator for Atmospheric Radiative Transfer Models—0
Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations—0
Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources—0
Statistical Learning for End-to-End Simulations—0
Physics-Aware Gaussian Processes in Remote Sensing—0
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method—0
Randomized kernels for large scale Earth observation applications—0
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