Dive into the world of deep learning for satellite images with your host, Robin Cole. Robin meets with experts in the field to discuss their research, products, and careers in the space of satellite image deep learning. Stay up to date on the latest trends and advancements in the industry - whether you’re an expert in the field or just starting to learn about satellite image deep learning, this a podcast for you. Head to https://www.satellite-image-deep-learning.com/ to learn more about this fascinating domain <br/><br/><a href="https://www.satellite-image-deep-learning.com?utm_medium=podcast">www.satellite-image-deep-learning.com</a>
Satellite image deep learning
Claim This Podcastby Robin Cole
5.0(3 reviews)
47 episodes
Updated Bi-weekly
Accepts GuestsHas Sponsors
30
Podcast Authority
BetaPoorBased on show quality, social media presence, reviews, charts, and more
Quality32
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Engagement68
Podcast Overview
Dive into the world of deep learning for satellite images with your host, Robin Cole. Robin meets with experts in the field to discuss their research, products, and careers in the space of satellite image deep learning. Stay up to date on the latest trends and advancements in the industry - whether you’re an expert in the field or just starting to learn about satellite image deep learning, this a podcast for you. Head to https://www.satellite-image-deep-learning.com/ to learn more about this fascinating domain <br/><br/><a href="https://www.satellite-image-deep-learning.com?utm_medium=podcast">www.satellite-image-deep-learning.com</a>
Language
🇺🇲
Publishing Since
1/26/2023
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Recent Episodes
June 24, 2026
Building OlmoEarth: AI2’s Open Foundation Model for Satellite Imagery
<p>In this episode I sat down with Joe Redmond from the Allen Institute for AI (AI2) to discuss OlmoEarth, AI2's open geospatial foundation model for Earth observation. Joe explains how the project emerged from AI2's environmental and climate initiatives, where partners needed practical tools for analysing satellite imagery across applications such as agriculture, wildfire risk, ecosystem mapping, and conservation. We discuss the unique challenges of remote sensing data, including its temporal and multispectral nature, why geospatial machine learning differs from traditional computer vision, and AI2's philosophy of building open models and tools that can be adapted to real-world environmental problems.A major focus of the conversation is Latent MIM Lite, OlmoEarth's self-supervised pretraining approach. Joe explains how the method strikes a balance between masked autoencoders, which reconstruct pixels and train reliably but often learn weaker representations, and latent-space methods such as I-JEPA and Latent MIM, which can produce stronger features but are notoriously unstable. By replacing the target encoder with a frozen random linear projection in token space, Latent MIM Lite achieves stable training while preserving the benefits of latent-space prediction. We also discuss the broader challenges of evaluating geospatial foundation models, the trade-offs between embeddings and fine-tuning, and why practical performance on partner applications often matters more than leaderboard results.</p><p>* 📺 <a target="_blank" href="https://youtu.be/wzCHJf6Ly24">Video of this conversation on YouTube</a></p><p>* 🖥️ <a target="_blank" href="https://github.com/allenai/olmoearth_pretrain">OlmoEarth on Github</a></p><p>* 🖥️ <a target="_blank" href="https://olmoearth.allenai.org/">OlmoEarth Platform</a></p><p>* 👤 <a target="_blank" href="https://pjreddie.com/">Joe’s website</a></p><p>Bio: Joseph Redmon is a research scientist at Ai2 building multimodal foundation models for geospatial data. As part of the OlmoEarth team he’s working to bring cutting edge AI research to non profits and NGOs working on conservation, ecological, and environmental problems.</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://www.satellite-image-deep-learning.com?utm_medium=podcast&utm_campaign=CTA_1">www.satellite-image-deep-learning.com</a>
June 17, 2026
A Single GPU Is All You Need for Self-Supervised Pretraining
<p>In this episode I sat down with Lakshay Sharma, a machine learning scientist at Instacart and former member of Microsoft’s geospatial AI team, to discuss self-supervised learning for remote sensing and his recent research on efficient pretraining for semantic segmentation. Lakshay explains the evolution of self-supervised learning, covering predictive, generative, and contrastive approaches, and discusses how foundation models such as DINO have transformed computer vision and geospatial machine learning. We explore the unique challenges of applying these techniques to remote sensing imagery, where assumptions that work for natural images often break down.We then dive into Lakshay’s recent paper, Sub-Image Overlap Prediction: Task-Aligned Self-Supervised Pretraining for Semantic Segmentation in Remote Sensing Imagery, presented at the Computer Vision for Earth Observation Workshop at WACV 2026. He walks through the intuition behind the method, which trains models to localize extracted sub-images within larger scenes as a proxy task for semantic segmentation. We discuss the experimental setup, comparisons against established self-supervised learning approaches, and the surprising finding that the method achieves competitive or superior results using only thousands of pretraining images rather than millions. Along the way, we explore transfer learning across datasets, the growing importance of data efficiency, and why targeted pretraining may offer a compelling alternative to increasingly resource-intensive foundation model development for niche geospatial applications.</p><p>* 📺 <a target="_blank" href="https://youtu.be/ta40N4KwMvw">Video of this conversation on YouTube</a></p><p>* 👤 <a target="_blank" href="https://www.linkedin.com/in/slakshay/">Lakshay on LinkedIn</a></p><p>* 🖥️ <a target="_blank" href="https://sharmalakshay93.github.io">Personal website of Lakshay</a></p><p>* 📖 <a target="_blank" href="https://openaccess.thecvf.com/content/WACV2026W/CV4EO/html/Sharma_Subimage_Overlap_Prediction_Task-Aligned_Self-Supervised_Pretraining_For_Semantic_Segmentation_In_WACVW_2026_paper.html">Paper</a></p><p>Bio: Lakshay Sharma is a Senior Machine Learning Scientist / Engineer at Instacart. His research spans Computer Vision (CV) and Vision-Language Models (VLMs) with a focus on Self-Supervised and Semi-Supervised Learning. He has previously worked at Microsoft on multi-modal representation learning, and using aerial/satellite and streetside imagery for maps and geospatial applications. He has also worked at Amazon where he was focused on representation learning for videos. Based in New York City, Lakshay is an avid fan of soccer, snowboarding, and cricket. He often daydreams of some day applying his computer vision chops to sports.</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://www.satellite-image-deep-learning.com?utm_medium=podcast&utm_campaign=CTA_1">www.satellite-image-deep-learning.com</a>
June 10, 2026
Mapping The World at Taylor Geospatial
<p>In this episode I sat down with Jennifer Marcus and Isaac Corley from Taylor Geospatial to explore Fields of the World - an open initiative to create globally consistent agricultural field boundary datasets from satellite imagery using AI and cloud-native geospatial infrastructure. Taylor Geospatial, a newly formed research organization, is building openly licensed global datasets as foundational public goods. Jen and Isaac explain the motivation behind the project, the challenges of scaling machine learning beyond well-labelled regions, and why openness in datasets, tooling, and intermediate model outputs, is central to their approach.We dive into the technical details behind the first global release: assembling noisy and uneven benchmark datasets from around the world, training models that generalise across diverse agricultural systems, and releasing everything from Sentinel-2 mosaics and raw segmentation probabilities to polygonised field boundaries through Source Cooperative. Along the way, we discuss community-driven improvement loops inspired by OpenStreetMap, the limitations of 10 m imagery for smallholder agriculture, and the importance of pairing academic researchers with engineering teams to rapidly operationalise new methods. Finally, we look ahead to Taylor Geospatial’s next phase - richer agricultural datasets, “Features of the World,” and a benchmarking initiative aimed at improving evaluation standards and reproducibility across geospatial foundation models.</p><p>* 📺 <a target="_blank" href="https://youtu.be/b5NZfl1xWgQ">Video of this conversation on YouTube</a></p><p>* 🖥️ <a target="_blank" href="https://taylorgeospatial.org/">Taylor Geospatial website</a></p><p>* 🖥️ <a target="_blank" href="https://fieldsofthe.world/">FTW website</a></p><p>Bio: Jennifer Marcus is Vice President of Strategic Innovation Programs at Taylor Geospatial, where she advances partnerships and programs that translate breakthrough geospatial AI research into real-world impact. With deep experience across defence, federal government, and open-source geospatial ecosystems, Jennifer brings decades of expertise translating emerging technologies into mission-critical impact. She previously served as the inaugural Executive Director of Taylor Geospatial Engine, which in 2024, launched what would become Fields of The World, and has held leadership roles at Planet, Boundless Spatial, and Northrop Grumman.</p><p>Bio: Isaac Corley is Director of AI/ML Research at Taylor Geospatial, where he leads a team to build the models behind earth observation research and to create open data products that elevate the geospatial market and community as a whole. Isaac builds and publishes geospatial AI from research through production, including the RasterFlow platform at Wherobots, which was used to run Fields of The World. He has served as PI on the IARPA SMART program at BlackSky and maintains widely-used open-source projects, including <a target="_blank" href="https://github.com/torchgeo/torchgeo">TorchGeo</a> and <a target="_blank" href="https://github.com/qubvel-org/segmentation_models.pytorch">SMP</a>. Check out his blog with Caleb Robinson at <a target="_blank" href="http://geospatialml.com/">geospatialml.com</a>.</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://www.satellite-image-deep-learning.com?utm_medium=podcast&utm_campaign=CTA_1">www.satellite-image-deep-learning.com</a>
47 total episodes available
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- What is Satellite image deep learning?
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This podcast updates bi-weekly.
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