Best Practices for EO+AI Workflows
Modern Earth Observation (EO) data processing combined with artificial intelligence requires an approach that integrates scalable data storage, an efficient computational layer, and practices ensuring data integrity and reproducibility. Within the NSIS Cloud architecture, built on open technologies, it is possible to create an environment that meets the growing demands of large-scale satellite data analysis and AI model training.
A key element is data architecture. Satellite data are characterized by massive volume and diversity, which makes efficient storage mechanisms essential. Equally important is the choice of formats optimized for analysis: JPEG2000, Cloud Optimized GeoTIFF (COG), Zarr, or Parquet enable fast, parallel access to data fragments without downloading entire files, significantly reducing processing time. This approach is complemented by metadata catalogs compliant with OGC standards, such as STAC (SpatioTemporal Asset Catalogs), which facilitate interoperability and efficient data discovery. It is also recommended to clearly separate raw data from processed analytical products, as such layering simplifies data lifecycle management. At the hardware level, high-speed NVMe SSDs play a critical role by eliminating bottlenecks during intensive I/O operations. Dataset integrity should be maintained through checksum validation, while data and model evolution should be tracked with versioning systems such as DVC or MLflow.
The second pillar of the processing chain is the computational layer. For satellite data, standardized execution of computations is crucial. Containerization (Docker) and orchestration (Kubernetes, Slurm) have become standard practices, enabling scalable and portable processes. For AI tasks, GPU resources are critical. Their efficient use requires not only code optimization (batching, profiling with PyTorch Profiler or Nsight) but also continuous monitoring. Tools like nvidia-smi allow real-time tracking of memory usage and GPU load, enabling configuration tuning and preventing resource waste. Optimal results are achieved using the latest CUDA drivers and libraries such as PyTorch or TensorFlow, which ensure compatibility and maximum performance.
At the same time, efficient CPU utilization should not be overlooked. Multithreading and parallel processing can significantly accelerate data preparation, particularly when reading satellite imagery. Libraries such as GDAL support parallel I/O, allowing multiple files to be processed simultaneously. In practice, optimal analytical pipelines combine CPU power for preprocessing with GPU acceleration for model training and inference. End-to-end automation from data acquisition, through preprocessing, to analytical outputs—is supported by workflow management tools such as Airflow, Prefect, or Kubeflow.
Finally, best practices also encompass monitoring and auditing. Systematic logging of computational metrics (time, GPU/CPU utilization, I/O operations) and tracking of data lineage enhance transparency and enable future reproducibility. Reproducibility is further strengthened by container-based environment definitions, which eliminate discrepancies between development and production systems.
This structured approach combining efficient data storage, modern computational strategies, and quality assurance mechanisms enables the development of innovative EO+AI workflows in NSIS Cloud. As a result, it becomes possible to both process massive satellite data volumes and train advanced AI models in a scalable, optimized, and fully reproducible manner.