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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 51–75 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
Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community ChallengeCode0
CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models—0
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
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