Image & Signal Processing

Google Colab

GitHub - ESAOpenSR/opensr-model

SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation

Leveraging a Fully Differentiable Integrated Assessment Model for RL and Inference

Near-Real-Time Turbidity Monitoring at Global Scale Using Sentinel-2 Data and Machine Learning Techniques

Combining BioGeoChemical-Argo (BGC-Argo) floats and satellite observations for water column estimations of the particulate backscattering coefficient

Machine Learning-Based Retrieval of Cloud Droplet Number Concentration and Liquid Water Path From Satellite Spectral Data

Explainable Earth Surface Forecasting Under Extreme Events

Assessing evapotranspiration dynamics across central Europe in the context of land–atmosphere drivers

A novel calibration of global soil roughness effects for SMOS-IC soil moisture and L-VOD products

Hues and Cues: Human vs. CLIP

Response to Affine Transforms of Image Distance Metrics and Humans

Contrast Sensitivity Function of Multimodal Vision-Language Models

From Images to Perception: Emergence of Perceptual Properties by Reconstructing Images

Evolution of Low-Level and Texture Human-CLIP Alignment

Do Vision Transformers See Like Humans? Evaluating their Perceptual Alignment

Understanding flood detection models across Sentinel-1 and Sentinel-2 modalities and benchmark datasets

Sub-Seasonal Forest Carbon Dynamics Lose Persistence Under Extremes

Serendipity’s role in advancing geoscience

CloudRuler: Rule-based transformer for cloud removal in Landsat images

Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion

Calibration and uncertainty quantification for deep learning-based drought detection

Leveraging causality and explainability in digital agriculture

Out-of-distribution robustness for multivariate analysis via causal regularisation

Dynamics of Masked Image Modeling in Hyperspectral Image Classification

A Radiometrically and Spatially Consistent Super-Resolution Framework for Sentinel-2

Invertible Neural Networks for Probabilistic Aerosol Optical Depth Retrieval

Early warning of complex climate risk with integrated artificial intelligence

Large language models for causal hypothesis generation in science

A Flag Decomposition for Hierarchical Datasets

Feasibility of L-Band Sharpening With C-Band Using SMAP and AMSR Radiometry Data for Future Application to CIMR

Generative networks for spatio-temporal gap filling of Sentinel-2 reflectances

Artificial intelligence for modeling and understanding extreme weather and climate events - Nature Communications

Earth System Data Cubes: Avenues for advancing Earth system research

Emulation of Forward Modeled Top-of-Atmosphere MODIS-Based Spectral Channels Using Machine Learning

Esto es lo que la IA puede hacer (y lo que no) en fenómenos como la DANA

Learning extreme vegetation response to climate drivers with recurrent neural networks

AI-empowered next-generation multiscale climate modelling for mitigation and adaptation - Nature Geoscience

Towards data-driven discovery of governing equations in geosciences

Collaboration between artificial intelligence and Earth science communities for mutual benefit

Digital twins of the Earth with and for humans - Communications Earth & Environment

Causal discovery reveals complex patterns of drought-induced displacement

Cautionary remarks on the planetary boundary visualisation

CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2

Causal hybrid modeling with double machine learning—applications in carbon flux modeling

The AIDE Toolbox: Artificial intelligence for disentangling extreme events [Software and Data Sets]

Identifying compound weather drivers of forest biomass loss with generative deep learning | Environmental Data Science

Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference

Nonlinear Distribution Regression for Remote Sensing Applications

Global flood extent segmentation in optical satellite images

Multifidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models

Editorial: AI and remote sensing in ocean sciences

Causality and Explainability for Trustworthy Integrated Pest Management

Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction

Fewshot learning on global multimodal embeddings for earth observation tasks

Exploring interactions between socioeconomic context and natural hazards on human population displacement

Harnessing AI's potential for a greener tomorrow: Insights from ITU's AI for Good series

Pairwise causal discovery with support measure machines

Discovering causal relations and equations from data

Role of locality, fidelity and symmetry regularization in learning explainable representations

Improving air quality assessment using physics-inspired deep graph learning - npj Climate and Atmospheric Science

TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting

Learning latent functions for causal discovery

Causal inference for time series

A Scalable Unsupervised Feature Selection With Orthogonal Graph Representation for Hyperspectral Images

Artificial intelligence to advance Earth observation: A review of models, recent trends, and pathways forward

Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation

Interpretable Long Short-Term Memory Networks for Crop Yield Estimation

Soil and vegetation water content identify the main terrestrial ecosystem changes

Multi-spectral multi-image super-resolution of Sentinel-2 with radiometric consistency losses and its effect on building delineation

Estimation of vegetation traits with kernel NDVI

ML4Floods-ISP: Global flood extent segmentation in optical satellite images