Satellite image deep learning

About Satellite image deep learning

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 www.satellite-image-deep-learning.com (https://www.satellite-image-deep-learning.com?utm_medium=podcast)

Publisher

Robin Cole

Category

science · technology

Language

en

Explicit

No

First episode

26 Jan 2023

Latest episode

28 Jul 2026

Latest episodes

48 episodes in the feed.

1. 

   28 Jul 2026

   Ai Agents with Hyades & BlazerOps

   In this episode I caught up with Ash Alex and Jimin Seo, co-founders of Hyades, to discuss BlazerOps, an agentic platform for building machine-learning models and pipelines for geospatial data. They explain how the platform aims to make spatial ML faster and more accessible, helping engineers automate complex workflows while enabling analysts to define objectives in plain English. We discuss how geospatial agents differ from conventional LLM and coding-agent workflows, why spatial data requires specialist tools, and how multiple agents can coordinate data inspection, preprocessing, model development, and evaluation.A major focus is how BlazerOps combines general-purpose language models with specialist geospatial models and curated technical knowledge. Ash and Jimin describe their in-house foundation model for spectral imagery and explain why geospatial applications may require a toolkit of specialist models rather than a single universal “supermodel.” We also discuss embeddings, fine-tuning, bespoke architectures, organisational knowledge, and how agentic development could free ML engineers and researchers to focus on more advanced work. * BlazerOps website (https://www.blazerops.ai/) * Video of this conversation on YouTube (https://youtu.be/4OiXDG7-izI) * Demo of BlazerOps (https://youtu.be/aC642jg2sTA) Jimin Seo leads operations and global partnerships at Hyades, with a background in commerce and law, and passion for designing and delivering software solutions. He works closely with the technical team to translate organization-level data, privacy, IP and trustability requirements into a trustable, personal and powerful platform to enable spatial ML development for everyone. Ash Alex is the CEO of Hyades. Coming from a physics background, Ash has experience designing optical communications systems for commercial satellites at the University of Auckland, Cornell, and NASA’s Decadal Survey Program, where he was exposed to the complexities of working with geospatial data. His mission is to embed spatial insights everywhere. 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 (https://www.satellite-image-deep-learning.com?utm_medium=podcast&utm_campaign=CTA_1)

2. 

   24 Jun 2026

   Building OlmoEarth: AI2’s Open Foundation Model for Satellite Imagery

   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. * 📺 Video of this conversation on YouTube (https://youtu.be/wzCHJf6Ly24) * 🖥️ OlmoEarth on Github (https://github.com/allenai/olmoearth_pretrain) * 🖥️ OlmoEarth Platform (https://olmoearth.allenai.org/) * 👤 Joe’s website (https://pjreddie.com/) 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. 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 (https://www.satellite-image-deep-learning.com?utm_medium=podcast&utm_campaign=CTA_1)

3. 

   17 Jun 2026

   A Single GPU Is All You Need for Self-Supervised Pretraining

   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. * 📺 Video of this conversation on YouTube (https://youtu.be/ta40N4KwMvw) * 👤 Lakshay on LinkedIn (https://www.linkedin.com/in/slakshay/) * 🖥️ Personal website of Lakshay (https://sharmalakshay93.github.io) * 📖 Paper (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) 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. 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 (https://www.satellite-image-deep-learning.com?utm_medium=podcast&utm_campaign=CTA_1)

Who has been a guest on Satellite image deep learning

Names identified in recent episode analyses. Showing up to five guests.

• Shahab Jozdani
• Nick Wright
• Marek Kraft

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

Apple Podcasts (US)

5.0 / 5

1 ratings

Spotify

5.0 / 5

3 ratings

Podcast Authority Score: 30 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

32

Social presence

0

YouTube

0

Engagement

68

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