TERRA Innovations
TERRA is at the forefront of AI-driven advancements for climate services, integrating cutting-edge technologies to enhance forecasting, data analysis, and digital modeling. Our innovations span multiple domains, each designed to tackle pressing environmental challenges with precision and efficiency.
Artificial Intelligence (AI) for Knowledge Extraction and Forecasting
(i) Resilient Multivariate Forecasting Models
TERRA will develop AI models with robust multivariate forecasting capabilities, focusing on challenging data sequences like time-series. By combining transfer learning mechanisms with attention-based machine learning techniques, TERRA aims to deliver highly accurate predictions for complex scenarios.
(ii) Integration of Diverse Machine Learning (ML) Approaches
The project will apply a wide range of ML algorithms to process large datasets across diverse Use Cases, including climate emergencies, flooding, and coastline management. Each domain’s specific requirements will inform customized ML models to optimize outcomes while addressing unique constraints.
(iii) Responsible and Optimized AI Training
To reduce the computational complexity of deep neural networks, TERRA will employ techniques such as transfer learning, fine-tuning, and layer freezing. The project will develop innovative methods that balance training time and accuracy, integrating these advances into its platform for improved efficiency.
Image Processing Techniques for Climate Services
(i) Small-Object Detection in Satellite Images
TERRA will create reliable models for detecting smaller, densely clustered objects often found in satellite imagery.
(ii) Advanced Object Processing
The project will develop techniques to efficiently process rotated objects and axis-aligned bounding boxes, improving annotation and detection capabilities.
(iii) Confidence in Detection
TERRA will ensure a high degree of reliability and robustness in detecting small objects, enhancing confidence in satellite image analysis.
From Copernicus to product chains solutions
Digital Twins and Data Augmentation for Climate Services
(i) Memory-Efficient Digital Twins
TERRA’s Digital Twins (DTs) will implement a memory-efficient data augmentation framework. This approach integrates data pruning and nonlinear model reduction to optimize memory usage while maintaining accuracy and scalability.
(ii) Heterogeneous Data Processing
The DT framework will handle multimodal data, combining experimental and simulated datasets. This capability will enhance data across spatial, temporal, and functional dimensions, improving prediction accuracy and testing reliability.
(iii) Adaptive Digital Twins
TERRA will introduce adaptive DTs trained on synthetic data for supervised and self-learning ML applications. These will be validated within the project’s Use Cases to ensure reliability. A semi-supervised component will further bridge gaps between the DT system and real-world Demonstrators, enhancing effectiveness.
Copernicus Service Integration and Product Provisioning
(i) Service Reusability
The TERRA platform is designed to be flexible and reusable across various application domains, enabling broad adoption and scalability.
(ii) Model Flexibility
AI models developed under TERRA will support knowledge transfer across multiple applications, such as object detection, time-series forecasting, and multivariate pattern recognition, ensuring adaptability to diverse use cases.
(iii) Multi-Source Data Integration
TERRA integrates data from Copernicus repositories with ICT modules and onsite-collected datasets, creating a comprehensive and cohesive data-processing ecosystem.
(iv) High Data Heterogeneity for Coastal Hydrological Observation
The platform processes highly heterogeneous data collected from various domains and structured in multiple classes. This capability enhances coastal hydrological observation by incorporating diverse parameters and factors.
(v) Alignment with Green Deal and SWOT Mission Objectives
TERRA’s product chains will support the SWOT mission by monitoring and mapping water bodies such as rivers and coastline areas. These tools will enable policymakers to track changes over time, assess climate change impacts, and implement measures for flood prevention and coastline erosion mitigation.