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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 51–100 of 518 papers

TitleStatusHype
TerraTorch: The Geospatial Foundation Models ToolkitCode4
On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?—0
Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation—0
The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data GenerationCode2
Towards a Unified Copernicus Foundation Model for Earth VisionCode2
Panopticon: Advancing Any-Sensor Foundation Models for Earth ObservationCode1
A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning PerspectivesCode0
On the Generalization of Representation Uncertainty in Earth ObservationCode1
GeoLangBind: Unifying Earth Observation with Agglomerative Vision-Language Foundation ModelsCode1
An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problem—0
Lossy Neural Compression for Geospatial Analytics: A Review—0
SSL4EO-S12 v1.1: A Multimodal, Multiseasonal Dataset for Pretraining, UpdatedCode1
DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications—0
Regression in EO: Are VLMs Up to the Challenge?—0
CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models—0
Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community ChallengeCode0
JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation FrameworkCode2
SARChat-Bench-2M: A Multi-Task Vision-Language Benchmark for SAR Image InterpretationCode2
Enhancing Ground-to-Aerial Image Matching for Visual Misinformation Detection Using Semantic SegmentationCode0
Leveraging band diversity for feature selection in EO data—0
TerraQ: Spatiotemporal Question-Answering on Satellite Image Archives—0
SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space ModelsCode0
CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image SegmentationCode0
NUDT4MSTAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the WildCode2
How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?—0
Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellites—0
A Remote Sensing Image Change Detection Method Integrating Layer Exchange and Channel-Spatial DifferencesCode1
BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster responseCode3
Joint Communications and Sensing for 6G Satellite Networks: Use Cases and Challenges—0
Microservice Deployment in Space Computing Power Networks via Robust Reinforcement Learning—0
TS-SatMVSNet: Slope Aware Height Estimation for Large-Scale Earth Terrain Multi-view Stereo—0
Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning ApproachCode0
Gaussian Splatting for Efficient Satellite Image Photogrammetry—0
The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generationf—0
Feature Spectrum Learning for Remote Sensing Change Detection—0
Low-Thrust Under-Actuated Satellite Formation Guidance and Control Strategies—0
SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing—0
Uncertainties of Satellite-based Essential Climate Variables from Deep Learning—0
REO-VLM: Transforming VLM to Meet Regression Challenges in Earth Observation—0
EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues—0
Distribution Shifts at Scale: Out-of-distribution Detection in Earth ObservationCode1
AnySat: One Earth Observation Model for Many Resolutions, Scales, and ModalitiesCode2
EOGS: Gaussian Splatting for Earth Observation—0
Scalable Data Transmission Framework for Earth Observation Satellites with Channel Adaptation—0
Deep Random Features for Scalable Interpolation of Spatiotemporal DataCode1
TS-SatFire: A Multi-Task Satellite Image Time-Series Dataset for Wildfire Detection and PredictionCode1
Practical IMT and EESS Spectrum Sharing in the 7 to 8 GHz Band—0
Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings—0
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning—0
Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space—0
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