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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 1–50 of 518 papers

TitleStatusHype
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance SegmentationCode0
Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing—0
Towards Scalable and Generalizable Earth Observation Data Mining via Foundation Model Composition—0
TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis—0
High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation DataCode1
Video Compression for Spatiotemporal Earth System DataCode2
Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study—0
Scaling-Up the Pretraining of the Earth Observation Foundation Model PhilEO to the MajorTOM DatasetCode0
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
Atomizer: Generalizing to new modalities by breaking satellite images down to a set of scalars—0
CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities—0
FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping—0
Short-Term Power Demand Forecasting for Diverse Consumer Types to Enhance Grid Planning and Synchronisation—0
Training-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networks—0
FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic ReviewCode0
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images—0
Geospatial Foundation Models to Enable Progress on Sustainable Development Goals—0
DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding—0
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K ResolutionCode1
RemoteSAM: Towards Segment Anything for Earth ObservationCode3
REOBench: Benchmarking Robustness of Earth Observation Foundation ModelsCode1
InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object RecognitionCode2
MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation TasksCode0
Event-based Star Tracking under Spacecraft Jitter: the e-STURT Dataset—0
EarthSynth: Generating Informative Earth Observation with Diffusion Models—0
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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