PhiDown: Fast, Simple Access to Copernicus Data

In this episode, Roberto from ESA’s Φ-lab in Frascati introduces PhiDown, a community-driven open-source tool designed to simplify data access from the Copernicus Data Space Ecosystem (CDSE). He explains why PhiDown was created, how it uses the high-speed S5 protocol for efficient downloads, and how it differs from other platforms like Google Earth Engine. The discussion highlights real-world use cases, from automating Sentinel data pipelines to building large-scale datasets for AI models. In the live demo from 9:12, you’ll see how PhiDown makes it possible to search, query, and download products with just a few lines of Python, whether working with Sentinel missions or even contributing commercial data sets. This video is a standalone demo that walks through the GitHub repository and an example notebook in detail. PhiDown is free, open source, and built for the community. Try it out, contribute to its development, and help make Copernicus data more accessible to all! * https://github.com/ESA-PhiLab/phidown * https://www.linkedin.com/in/roberto-del-prete-8175a7147/ 🚀 TIMELINE 0:38 Motivation — PhiDown created to simplify access to Copernicus data 1:55 Key Tech — Built on S5 protocol, derived from S3, ~5–10× faster 2:44 Comparison — Unlike Google Earth Engine, PhiDown gives direct access to raw products such as Level-0 Sentinel imagery 5:01 Use cases — Automating pipelines (auto-download latest Sentinel products). Accessing low-level products for algorithm testing. Building large datasets for ML / foundation models. Research applications: wildfire detection, vessel monitoring, timeliness studies with Level-0 data 6:55 Development context — Roberto notes the rise of LLMs and coding agents. Tools can help, but domain expertise still required. 8:01 Open Source — PhiDown is on GitHub. Includes documentation + example notebooks. Community-driven project — Roberto encourages contributions, feature requests, and collaboration. 9:12 Demo start — GitHub walkthrough: package installable via pip or from source. Docs + notebooks included. Credentials required (free CDSE account). 10:52 Free service — No costs, unlimited downloads (with registration). 12:16 Notebook demo — Define AOI, search Sentinel-1 products. Query supports attributes (e.g., processing level, orbit mode). Returns DataFrame: filenames, S3 paths, footprints, timestamps, mission-specific attributes. 15:01 Multi-mission support — Search Landsat, Sentinel, and even contributing/commercial missions (e.g., TerraSAR-X, COSMO, high-res imagery). Enables building multimodal datasets. 16:51 Metadata queries — Retrieve info for specific products (name, capture time, attributes). 17:29 Downloading — Simple config + 3 lines of code to fetch data. S5 commands allow syncing to own S3 bucket. 18:52 Documentation examples — Covers basic searches, multi-mission datasets, product intersections, visualization helpers, reference guide (query schemas + attributes). 20:44 Roadmap — PhiDown still in development. Community help needed to extend product coverage (currently supports Sentinel-1/2/3 fully, more to come). 21:20 Closing thoughts — Tool aims to reduce friction to access Copernicus data. By lowering barriers, more researchers can build datasets, run analyses, and “✨ let the magic happen.” Bio: Roberto is an Internal Research Fellow at ESA Phi-lab specializing in deep learning and edge computing for remote sensing. He focuses on improving time-critical decision-making through advanced AI solutions for space missions and Earth monitoring. He holds a Ph.D. at the University of Naples Federico II, where he also earned his Master's and Bachelor's degrees in Aerospace Engineering. His notable work includes the development of "FederNet," a terrain relative navigation system. Del Prete's professional experience includes roles as a Visiting Researcher at the European Space Agency's Phi-Lab and SmartSat CRC in Australia. He has contributed to key projects like Kanyini Mission, and developed AI algorithms for real-time maritime monitoring and thermal anomaly detection. He co-developed the award-winning P³ANDA project, a compact AI-powered imaging system, earning the 2024 Telespazio Technology Contest prototype prize. Co-author of more than 30 scientific publications, Del Prete is dedicated to leveraging advanced technologies to address global challenges in remote sensing and AI.