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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
Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection—0
SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images—0
MS-Net: A Multi-modal Self-supervised Network for Fine-Grained Classification of Aircraft in SAR Images—0
An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 SatelliteCode0
Multi-Task Hypergraphs for Semi-supervised Learning using Earth ObservationsCode0
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery—0
Deep Learning Model Transfer in Forest Mapping using Multi-source Satellite SAR and Optical Images—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric SpaceCode0
Poverty rate prediction using multi-modal survey and earth observation data—0
Understanding the impacts of crop diversification in the context of climate change: a machine learning approach—0
SepHRNet: Generating High-Resolution Crop Maps from Remote Sensing imagery using HRNet with Separable Convolution—0
General-Purpose Multimodal Transformer meets Remote Sensing Semantic SegmentationCode0
Sparse Graphical Linear Dynamical Systems—0
A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery—0
On-orbit model training for satellite imagery with label proportionsCode0
Joint multi-modal Self-Supervised pre-training in Remote Sensing: Application to Methane Source Classification—0
Context-Aware Change Detection With Semi-Supervised Learning—0
Reducing Uncertainties of a Chained Hydrologic-hydraulic Model to Improve Flood Forecasting Using Multi-source Earth Observation Data—0
Over-the-Air Federated Learning in Satellite systems—0
Improve State-Level Wheat Yield Forecasts in Kazakhstan on GEOGLAM's EO Data by Leveraging A Simple Spatial-Aware Technique—0
Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models—0
On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites—0
Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward—0
Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation—0
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