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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 301–350 of 518 papers

TitleStatusHype
On-Board Federated Learning for Dense LEO Constellations—0
Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends—0
Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a general-purpose sensor-fusion semantic embedding model—0
On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation—0
On the semantics of big Earth observation data for land classification—0
On the usability of deep networks for object-based image analysis—0
On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?—0
OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing—0
Optimal Dynamic Multi-Resource Management in Earth Observation Oriented Space Information Networks—0
ORBIT: Ordering Based Information Transfer Across Space and Time for Global Surface Water Monitoring—0
Over-the-Air Federated Learning in Satellite systems—0
Ai4Fapar: How artificial intelligence can help to forecast the seasonal earth observation signal—0
Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning—0
Unsupervised Self-training Algorithm Based on Deep Learning for Optical Aerial Images Change Detection—0
Pattern Recognition Scheme for Large-Scale Cloud Detection over Landmarks—0
Pest presence prediction using interpretable machine learning—0
Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions—0
Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction—0
Towards LLM Agents for Earth Observation—0
Physics-Aware Gaussian Processes in Remote Sensing—0
Towards Natural Language Question Answering over Earth Observation Linked Data using Attention-based Neural Machine Translation—0
Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach—0
Portraying the Need for Temporal Data in Flood Detection via Sentinel-1—0
Poverty rate prediction using multi-modal survey and earth observation data—0
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
Towards Scalable and Generalizable Earth Observation Data Mining via Foundation Model Composition—0
Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation—0
A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa—0
Probabilistic Machine Learning for Noisy Labels in Earth Observation—0
Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation—0
Towards seamless multi-view scene analysis from satellite to street-level—0
A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery—0
Towards Sustainable Satellite Edge Computing—0
QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering—0
Quantum algorithms applied to satellite mission planning for Earth observation—0
Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks—0
Quick unsupervised hyperspectral dimensionality reduction for earth observation: a comparison—0
Randomized kernels for large scale Earth observation applications—0
Rapid Adaptation of Earth Observation Foundation Models for Segmentation—0
Reducing Uncertainties of a Chained Hydrologic-hydraulic Model to Improve Flood Forecasting Using Multi-source Earth Observation Data—0
Regression in EO: Are VLMs Up to the Challenge?—0
A General Purpose Neural Architecture for Geospatial Systems—0
Training-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networks—0
Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images—0
REO-VLM: Transforming VLM to Meet Regression Challenges in Earth Observation—0
Responsible AI for Earth Observation—0
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method—0
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation—0
Road Segmentation of Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields—0
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