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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 26–50 of 518 papers

TitleStatusHype
Adaptive and Robust Image Processing on CubeSats—0
Covariance Symmetries Classification in Multitemporal/Multipass PolSAR Images—0
Towards Efficient Benchmarking of Foundation Models in Remote Sensing: A Capabilities Encoding ApproachCode0
Integrating earth observation data into the tri-environmental evaluation of the economic cost of natural disasters: a case study of 2025 LA wildfire—0
Core-Set Selection for Data-efficient Land Cover SegmentationCode0
Assimilation of SWOT Altimetry Data for Riverine Flood Reanalysis: From Synthetic to Real Data—0
Geolocating Earth Imagery from ISS: Integrating Machine Learning with Astronaut Photography for Enhanced Geographic MappingCode0
Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor MappingCode0
Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation—0
A Complex-valued SAR Foundation Model Based on Physically Inspired Representation Learning—0
Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover MappingCode0
Towards LLM Agents for Earth Observation—0
GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image ClassificationCode0
TerraMind: Large-Scale Generative Multimodality for Earth Observation—0
TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data—0
SAR-to-RGB Translation with Latent Diffusion for Earth Observation—0
Foundation Models for Remote Sensing: An Analysis of MLLMs for Object Localization—0
A Self-Supervised Framework for Space Object Behaviour Characterisation—0
A metrological framework for uncertainty evaluation in machine learning classification models—0
Probabilistic Machine Learning for Noisy Labels in Earth Observation—0
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation—0
Fault injection analysis of Real NVP normalising flow model for satellite anomaly detection—0
FlexiMo: A Flexible Remote Sensing Foundation Model—0
Generating Synthetic Oracle Datasets to Analyze Noise Impact: A Study on Building Function Classification Using Tweets—0
HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagery—0
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