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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 1–25 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
Atomizer: Generalizing to new modalities by breaking satellite images down to a set of scalars—0
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities—0
FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping—0
Training-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networks—0
Short-Term Power Demand Forecasting for Diverse Consumer Types to Enhance Grid Planning and Synchronisation—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
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