{"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":"BetaEarth: Open Embeddings of Sentinel-2 and Sentinel-1 with a Little Help of AlphaEarth","slug":"betaearth-open-embeddings-of-sentinel-2-and-sentinel-1-with-a-little-help-of-alphaearth","published_at":"2026-04-29T09:14:01+00:00","page_url":"https://stenobird.com/podcast/satellite-image-deep-learning-5989248/betaearth-open-embeddings-of-sentinel-2-and-sentinel-1-with-a-little-help-of-alphaearth","show_page_url":"https://stenobird.com/podcast/satellite-image-deep-learning-5989248","url":"https://www.satellite-image-deep-learning.com/p/betaearth-open-embeddings-of-sentinel","audio_url":"https://api.substack.com/feed/podcast/195348103/9c791ca00ab396e1a48912c1acc0ff17.mp3","summary":"In this episode I sat down with Mikolaj (Miko) Czerkawski from Asterisk Labs to explore BetaEarth, an experimental open-source emulator trained on AlphaEarth Foundations' public embedding archive. AEF — released by Google and Google DeepMind as a global 10 m embedding product derived from a wide range of Earth-observation modalities — is what makes BetaEarth possible: its openness lets the community build lightweight independent emulators that approximate AEF's pixelwise outputs from standard Sentinel inputs, and use them to probe how much of a model's behaviour is captured in its public embeddings. Miko walks through BetaEarth's design — compact architectures based on SegFormer-B2 with separate per-modality encoders, and a shared DINOv3 backbone over 3-band spectral primitives — and the surprising finding that reasonably strong approximations can be achieved even from simple RGB inputs.We then dive into a live demo: generating BetaEarth embeddings for arbitrary regions and time ranges using Sentinel-1, Sentinel-2, and COP-DEM data. Along the way, we cover practical considerations such as cloud contamination, modality trade-offs, tiling artefacts, and strategies for merging multi-temporal signals. Finally, we discuss what this complementary tooling enables for the geospatial ML community — embeddings as pretraining or regularisation signals, lightweight local inference alongside AEF's global annual rasters, and what the combination of large proprietary archives and open emulator-style tools could unlock next. * 📺 Video of this conversation & demo on YouTube * 🖥️ BetaEarth Github page * 🖥️ BetaEarth demo on Huggingface Bio: Miko is a researcher specialising in AI, computer vision, signal processing and Earth observation. Before co-founding Asterisk Labs he was a postdoc…","meta_description":"In this episode I sat down with Mikolaj (Miko) Czerkawski from Asterisk Labs to explore BetaEarth, an experimental open-source emulator trained on AlphaEa…","key_points":[],"chapters":[],"topics":[],"duration_seconds":1575,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/satellite-image-deep-learning-5989248/episodes/betaearth-open-embeddings-of-sentinel-2-and-sentinel-1-with-a-little-help-of-alphaearth/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/betaearth-open-embeddings-of-sentinel-2-and-sentinel-1-with-a-little-help-of-alphaearth.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}