Image & Signal Processing

Image & Signal Processing has been a member of Linktree for 1 year and joined in March 2025. The social media accounts linked to from Image & Signal Processing are: • Instagram • YouTube • LinkedIn • Threads • Bluesky • GitHub • Website Besides social media accounts, ISPUV has populated their site with: • 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 • Understanding flood detection models across Sentinel-1 and Sentinel-2 modalities and benchmark datasets • 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 • Serendipity’s role in advancing geoscience • CloudRuler: Rule-based transformer for cloud removal in Landsat images • Sub-Seasonal Forest Carbon Dynamics Lose Persistence Under Extremes • Google Colab • Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion • Calibration and uncertainty quantification for deep learning-based drought detection • Out-of-distribution robustness for multivariate analysis via causal regularisation • A Radiometrically and Spatially Consistent Super-Resolution Framework for Sentinel-2 • Leveraging causality and explainability in digital agriculture • Dynamics of Masked Image Modeling in Hyperspectral Image Classification • Estimation of vegetation traits with kernel NDVI • Multi-spectral multi-image super-resolution of Sentinel-2 with radiometric consistency losses and its effect on building delineation • Soil and vegetation water content identify the main terrestrial ecosystem changes • Interpretable Long Short-Term Memory Networks for Crop Yield Estimation • Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation • Artificial intelligence to advance Earth observation: A review of models, recent trends, and pathways forward • A Scalable Unsupervised Feature Selection With Orthogonal Graph Representation for Hyperspectral Images • Learning latent functions for causal discovery • TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting • Improving air quality assessment using physics-inspired deep graph learning - npj Climate and Atmospheric Science • Role of locality, fidelity and symmetry regularization in learning explainable representations • Discovering causal relations and equations from data • Harnessing AI's potential for a greener tomorrow: Insights from ITU's AI for Good series • Exploring interactions between socioeconomic context and natural hazards on human population displacement • Fewshot learning on global multimodal embeddings for earth observation tasks • Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction • Causality and Explainability for Trustworthy Integrated Pest Management • Editorial: AI and remote sensing in ocean sciences • Multifidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models • Global flood extent segmentation in optical satellite images • Nonlinear Distribution Regression for Remote Sensing Applications • Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference • The AIDE Toolbox: Artificial intelligence for disentangling extreme events [Software and Data Sets] • Causal hybrid modeling with double machine learning—applications in carbon flux modeling • CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2 • Cautionary remarks on the planetary boundary visualisation • Causal discovery reveals complex patterns of drought-induced displacement • Digital twins of the Earth with and for humans - Communications Earth & Environment • Collaboration between artificial intelligence and Earth science communities for mutual benefit • Towards data-driven discovery of governing equations in geosciences • AI-empowered next-generation multiscale climate modelling for mitigation and adaptation - Nature Geoscience • ML4Floods-ISP: Global flood extent segmentation in optical satellite images • Learning extreme vegetation response to climate drivers with recurrent neural networks • Esto es lo que la IA puede hacer (y lo que no) en fenómenos como la DANA • Earth System Data Cubes: Avenues for advancing Earth system research • Invertible Neural Networks for Probabilistic Aerosol Optical Depth Retrieval • Large language models for causal hypothesis generation in science • Early warning of complex climate risk with integrated artificial intelligence • 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 • GitHub - ESAOpenSR/opensr-model • Identifying compound weather drivers of forest biomass loss with generative deep learning | Environmental Data Science • Emulation of Forward Modeled Top-of-Atmosphere MODIS-Based Spectral Channels Using Machine Learning • Reading: Systematic Assessment of MODTRAN Emulators for Atmospheric Correction • Reading: Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes • Causal inference for time series • Pairwise causal discovery with support measure machines • More papers • Reading: Multifidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models • Reading: Generalized radiative transfer emulation for imaging spectroscopy reflectance retrievals • Reading: Multioutput Feature Selection for Emulation and Sensitivity Analysis • Reading: Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations • Reading: Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences • Reading: Forward model emulator for atmospheric radiative transfer using Gaussian processes and cross validation • ECML-PKDD 2025 • Join us!