{"podcast":{"title":"Satellite image deep learning","slug":"satellite-image-deep-learning-5989248","podcast_index_feed_id":5989248,"rss_url":"https://api.substack.com/feed/podcast/1186793.rss","website_url":"https://www.satellite-image-deep-learning.com/podcast","image_url":"https://substackcdn.com/feed/podcast/1186793/160a520ab8f4689dddcc760f14d20aa3.jpg","author":"Robin Cole","episode_count":46,"summary":"Newsletter on deep learning with satellite & aerial imagery","last_synced_at":"2026-06-24T18:18:37.974658+00:00","page_url":"https://stenobird.com/podcast/satellite-image-deep-learning-5989248"},"episode":{"title":"Solar Panel Detection with Satellite Imagery","slug":"solar-panel-detection-with-satellite-imagery","published_at":"2025-07-11T05:10:02+00:00","page_url":"https://stenobird.com/podcast/satellite-image-deep-learning-5989248/solar-panel-detection-with-satellite-imagery","show_page_url":"https://stenobird.com/podcast/satellite-image-deep-learning-5989248","url":"https://www.satellite-image-deep-learning.com/p/solar-panel-detection-with-satellite","audio_url":"https://api.substack.com/feed/podcast/167358908/c8a996bf4b0dfc2676ace1d046a810f4.mp3","summary":"In this episode, I catch up with Federico Bessi to dive into a fascinating end-to-end project on the automatic detection of photovoltaic (PV) solar plants using satellite imagery and deep learning. Federico walks us through how he built a complete pipeline—from sourcing and preprocessing data using the Brazil Data Cube, to annotating solar farms in QGIS, training models in PyTorch, and finally deploying a web app on AWS to visualise the predictions. This is interesting because solar energy infrastructure is expanding rapidly, yet tracking it globally remains a major challenge. This project demonstrates how open data and modern ML tools can be combined to monitor solar installations at scale—automatically and remotely. It's a compelling example of applied geospatial AI in action. This video is ideal for remote sensing practitioners, machine learning engineers, and anyone interested in environmental monitoring, Earth observation, or building practical AI systems for real-world deployment. * 🖥️ Project code on Github * 👤 Federico on Linkedin * 📺 Video of this conversation on YouTube * 📺 Project demo on YouTube Bio: Federico Bessi is a Software Engineer specializing in Machine Learning, with an international background in the software, computer vision, and biometrics industries. He spent over a decade working in biometric identification for global tech companies, contributing to national ID systems across more than seven countries. In these roles, he developed software, led engineering teams, and oversaw large-scale system operations. Building on this foundation, Federico has deepened his work in machine learning and deep learning, applying it to business intelligence, user satisfaction modeling, and geospatial analysis using satellite imagery. He also became a contributor…","meta_description":"In this episode, I catch up with Federico Bessi to dive into a fascinating end-to-end project on the automatic detection of photovoltaic (PV) solar plants…","key_points":[],"chapters":[],"topics":[],"duration_seconds":1864,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/satellite-image-deep-learning-5989248/episodes/solar-panel-detection-with-satellite-imagery/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/satellite-image-deep-learning-5989248/solar-panel-detection-with-satellite-imagery.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}