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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 151–175 of 518 papers

TitleStatusHype
Specialized Change Detection using Segment Anything—0
Seg-CycleGAN : SAR-to-optical image translation guided by a downstream task—0
Estimating Earthquake Magnitude in Sentinel-1 Imagery via Ranking—0
Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks—0
Increasing the Robustness of Model Predictions to Missing Sensors in Earth ObservationCode0
HTD-Mamba: Efficient Hyperspectral Target Detection with Pyramid State Space ModelCode1
DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremesCode0
SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing ImageryCode2
Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI—0
Improving EO Foundation Models with Confidence Assessment for enhanced Semantic segmentationCode0
HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation ModelCode3
Low-power Ship Detection in Satellite Images Using Neuromorphic Hardware—0
PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing ImageryCode5
A Late-Stage Bitemporal Feature Fusion Network for Semantic Change DetectionCode0
Towards Vision-Language Geo-Foundation Model: A SurveyCode2
Data Augmentation in Earth Observation: A Diffusion Model Approach—0
M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral DataCode1
Global High Categorical Resolution Land Cover Mapping via Weak Supervision—0
Responsible AI for Earth Observation—0
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of PovertyCode1
Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation PretrainingCode1
Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning—0
Serving economic prosperity: economic impact assessments (EIA) on Earth observation-based services and tools by SERVIR—0
Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds—0
Confidence Estimation in Unsupervised Deep Change Vector Analysis—0
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