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Earth Observation
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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.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data
Towards Scalable and Generalizable Earth Observation Data Mining via Foundation Model Composition
TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis
Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing
Video Compression for Spatiotemporal Earth System Data
Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study
Scaling-Up the Pretraining of the Earth Observation Foundation Model PhilEO to the MajorTOM Dataset
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis
Atomizer: Generalizing to new modalities by breaking satellite images down to a set of scalars
CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities
FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping
FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review
Training-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networks
Short-Term Power Demand Forecasting for Diverse Consumer Types to Enhance Grid Planning and Synchronisation
Geospatial Foundation Models to Enable Progress on Sustainable Development Goals
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
RemoteSAM: Towards Segment Anything for Earth Observation
REOBench: Benchmarking Robustness of Earth Observation Foundation Models
InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition
MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks
Event-based Star Tracking under Spacecraft Jitter: the e-STURT Dataset
EarthSynth: Generating Informative Earth Observation with Diffusion Models
Covariance Symmetries Classification in Multitemporal/Multipass PolSAR Images
Adaptive and Robust Image Processing on CubeSats
Towards Efficient Benchmarking of Foundation Models in Remote Sensing: A Capabilities Encoding Approach
Integrating earth observation data into the tri-environmental evaluation of the economic cost of natural disasters: a case study of 2025 LA wildfire
Core-Set Selection for Data-efficient Land Cover Segmentation
Assimilation of SWOT Altimetry Data for Riverine Flood Reanalysis: From Synthetic to Real Data
Geolocating Earth Imagery from ISS: Integrating Machine Learning with Astronaut Photography for Enhanced Geographic Mapping
Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation
Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor Mapping
A Complex-valued SAR Foundation Model Based on Physically Inspired Representation Learning
Towards LLM Agents for Earth Observation
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