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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 276–300 of 518 papers

TitleStatusHype
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
Ben-ge: Extending BigEarthNet with Geographical and Environmental DataCode1
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
SSL4EO-L: Datasets and Foundation Models for Landsat ImageryCode4
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
Geo-Tiles for Semantic Segmentation of Earth Observation ImageryCode1
Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models—0
FLAIR #2: textural and temporal information for semantic segmentation from multi-source optical imageryCode1
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
Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial IntelligenceCode1
Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation—0
Density Invariant Contrast Maximization for Neuromorphic Earth ObservationsCode0
Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction—0
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image UnderstandingCode1
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinatesCode0
Enhancing Self-Supervised Learning for Remote Sensing with Elevation Data: A Case Study with Scarce And High Level Semantic LabelsCode0
Explaining Multimodal Data Fusion: Occlusion Analysis for Wilderness Mapping—0
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