{"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":"Building Damage Assessment","slug":"building-damage-assessment","published_at":"2025-01-08T08:33:57+00:00","page_url":"https://stenobird.com/podcast/satellite-image-deep-learning-5989248/building-damage-assessment","show_page_url":"https://stenobird.com/podcast/satellite-image-deep-learning-5989248","url":"https://www.satellite-image-deep-learning.com/p/building-damage-assessment","audio_url":"https://api.substack.com/feed/podcast/153847809/f0188af0c8c4502543e88e7a0576c945.mp3","summary":"In this episode, I caught up with Caleb Robinson to learn about the building damage assessment toolkit from the Microsoft AI for Good lab. This toolkit enables first responders to carry out an end-to-end workflow for assessing damage to buildings after natural disasters using post-disaster satellite imagery. It includes tools for annotating imagery, fine-tuning deep learning models, and visualizing model predictions on a map. Caleb shared an example where an organisation was able to train a useful model with just 100 annotations and complete the entire workflow in half a day. I believe this represents a significant new capability, enabling more rapid response in times of crisis. * 📺 Video of this conversation on YouTube * 👤 Caleb on LinkedIn * 🖥️ The toolkit on Github Bio: Caleb is a Research Scientist in the Microsoft AI for Good Research Lab. His work focuses on tackling large scale problems at the intersection of remote sensing and machine learning/computer vision. Some of the projects he works on include: estimating land cover, poultry barns, solar panels, and cows from high-resolution satellite imagery. Caleb is interested in research topics that facilitate using remotely sensed imagery more effectively. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.satellite-image-deep-learning.com","meta_description":"In this episode, I caught up with Caleb Robinson to learn about the building damage assessment toolkit from the Microsoft AI for Good lab. This toolkit en…","key_points":[],"chapters":[],"topics":[],"duration_seconds":1030,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/satellite-image-deep-learning-5989248/episodes/building-damage-assessment/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/building-damage-assessment.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}