Federated Learning enables collaborative model training across distributed clients without exposing raw data, offering clear advantages for privacy and scalability. However existing approaches often fail to provide comprehensive protection across the entire lifecycle and struggle with client heterogeneity. This paper introduces a multi-stage privacy-preserving Federated Learning framework for short-term forecasting. On the privacy side, it integrates a threshold-based secret sharing protocol with mix-net–inspired anonymization. On the aggregation side, a lightweight clustering strategy that groups clients with similar consumption patterns is proposed, improving accuracy while preserving privacy. Real industrial and residential datasets have been used for validation purpose.

The present contribution focuses on applying Denoising Diffusion Probabilistic Models to Remote Sensing image classification, generation and super-resolution. Diffusion models are enhanced, including an attention block embedded in a UNet architecture is used to generate images to complement the EuroSAT data set. Furthermore, models with the same architecture super-resolve sentinel-2 optical images. The results indicate that diffusion models with attention can provide a promising methodological path for applications such as estimating the NDVI images most probably associated with given electro-optical and SAR acquisitions thereby overcoming limitations in observability due to cloud cover or solar illumination.

The creation of image classification and segmentation Machine Learning products requires the annotation of different classes by experts as well as the management of large volumes of data. This paper introduces a new methodology to optimize the computational execution and expert-user contribution by introducing a pixel quality indicator and, therefore reducing the number of annotated data used for model training, based on the geometric information of each pixel. The developed pixel-level quality indicator shows beneficial results, as a result of improving semantic segmentation and classification tasks’ performance, validated through the IRIS1platform.

The Normalized Difference Vegetation Index (NDVI) is an index for quantifying the health and density of vegetation, as it has a high correlation with the actual state of the vegetation on the ground. However, cloud cover is a major limitation in data acquisition. A possible solution is to estimate NDVI from radar images by means of a deep learning model.This study aims to improve quality of the NDVI estimations in a Region of Interest (ROI) with an averaging strategy that combines different available approximations from the SARtoNDVI estimator, a Conditional Generative Adversarial Network (cGAN) that approximates NDVI from processed Synthetic Aperture Radar (SAR) images.

The Normalized Difference Vegetation Index (NDVI), an indicator of vegetation health, is derived from the visible and near-infrared light reflected by vegetation, which can be measured with multi-spectral sensors. However, clouds can often obstruct land areas, making it challenging to obtain NDVI maps of the surface. This study explores the possibility of estimating NDVI from synthetic aperture radar (SAR) images using a Conditional Generative Adversarial Network (cGAN). Results encourage using a cGAN to address the task and provide valuable insights for future improvements.

We present a strategy to create an online phenology prediction service for non-technical users based on machine learning. The presented methodology is explained into two interrelated parts. The first part describes the core of the service, the automated machine learning procedure that enables the prediction modeling of the phenology phases evolution. The second part summarizes the architecture of the pipeline of the service (from data acquisition to interactive interface) considered for the proof of concept. Finally, the olive phenology prediction service developed in the DEMETER H2020 European project is presented as a real usage example.

Using on-demand processing pipelines to generate virtual geospatial products is beneficial to optimizing resource management and decreasing processing requirements and data storage space. Additionally, pre-processed products improve data quality for data-driven analytical algorithms, such as machine learning or deep learning models. This paper proposes a method to integrate virtual products based on integrating open-source processing pipelines. In order to validate and evaluate the functioning of this approach, we have integrated it into a geo-imagery management framework based on Open Data Cube (ODC). To validate the methodology, we have performed three experiments developing on-demand processing pipelines using multi-sensor remote sensing data, for instance, Sentinel-1 and Sentinel-2. These pipelines are integrated using open-source processing frameworks.

We propose a methodology to manage and process remote sensing and geo-imagery data for non-expert users. The proposed system provides automated data ingestion and manipulation capability for analytical data-driven purposes. In this paper, we describe the technological basis of the proposed method in addition to describing the tool architecture, the inherent data flow, and its operation in a specific use case to provide statistical summaries of Sentinel-2 regions of interest corresponding to the cultivation polygonal areas located in the Basque Country (ES).