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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 326–350 of 518 papers

TitleStatusHype
A General Purpose Neural Architecture for Geospatial Systems—0
An Interpretable Deep Semantic Segmentation Method for Earth Observation—0
Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery—0
On Advances, Challenges and Potentials of Remote Sensing Image Analysis in Marine Debris and Suspected Plastics Monitoring—0
QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-ResolutionCode0
EarthNets: Empowering AI in Earth Observation—0
Cross-Geography Generalization of Machine Learning Methods for Classification of Flooded Regions in Aerial Images—0
Advances in Fine Line-Of-Sight Control for Large Space Flexible Structures—0
OBBStacking: An Ensemble Method for Remote Sensing Object DetectionCode1
EOD: The IEEE GRSS Earth Observation Database—0
High-Resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production—0
Changer: Feature Interaction is What You Need for Change Detection—0
Spotting Virus from Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks—0
Fast Fourier Convolution Based Remote Sensor Image Object Detection for Earth Observation—0
Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery—0
Satellite Image Search in AgoraEO—0
Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution—0
Noise-Adaptive Intelligent Programmable Meta-Imager—0
Deep learning for satellite image forecasting of vegetation greennessCode1
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing—0
METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping—0
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image ClassificationCode2
Earthformer: Exploring Space-Time Transformers for Earth System ForecastingCode2
Three multi-objective memtic algorithms for observation scheduling problem of active-imaging AEOS—0
Multi-strip observation scheduling problem for ac-tive-imaging agile earth observation satellites—0
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