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Artwork for Satellite image deep learning

Satellite image deep learning

Robin Cole
Machine Learning
Satellite Imagery
Methane Detection
Satellite Imaging
Cloud Masking
Deep Learning
TROPOMI
Remote Sensing
Microsoft AI For Good Lab
Building Damage Assessment Toolkit
Python
Chat2geo
Solar Panels
Torchgeo
Instageo
Sentinel-5b
Icecloudnet
Geospatial Analysis
Openstreetmap
Disaster Response

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 ... more

PublishesMonthlyEpisodes42Founded3 years ago
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Artwork for Satellite image deep learning

Latest Episodes

In this episode I sat down with Kentaro Wada, a computer vision engineer at Mujin and creator of LabelMe, to explore the evolution of image annotation workflows. We discuss how his need to label data for a robotics challenge led to building one of th... more

In this episode I sat down with Hannah Kerner and Tristan Grupp to discuss Fields of The World (FTW), an open-source benchmark and ecosystem for global field boundary segmentation from satellite imagery. We explore the core challenge of building mode... more

In this episode I sat down with Isaac to discuss RF-DETR, a new state-of-the-art family of real-time object detection and segmentation models from Roboflow. We cover the motivation for building models that are not just accurate but also fast, cost-ef... more

In this episode I caught up with Sadiq Jaffer and Frank Feng to discuss Tessera, a large-scale foundation model for Earth observation that produces annual, pixel-level temporal embeddings from multi-sensor satellite data. They explain why moving beyo... more

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Recent Guests

Adam Stewart
Postdoctoral researcher at the Technical University of Munich and author of TorchGeo
Technical University of Munich
Episode: TorchGeo 1.0 with Adam Stewart
Shahab Jozdani
Founder at GeoRetina with a focus on remote sensing and AI
GeoRetina
Episode: Chat2Geo and the Power of LLMs
Kai Jeggle
PhD candidate focusing on machine learning applications in climate science
ETH Zurich
Episode: IceCloudNet and the PhD Journey
Daniele Rege Cambrin
Organizer of the SMAC Earthquake Detection Challenge
Episode: Insights from the SMAC earthquake detection challenge
Giorgio Morales
Winner of the SMAC Earthquake Detection Challenge
Episode: Insights from the SMAC earthquake detection challenge
Caleb Robinson
Principal Research Scientist at Microsoft on the AI for Good team
Microsoft
Episode: Building Damage Assessment

Host

Robin Cole
Host of the Satellite Image Deep Learning podcast (facilitating conversations with experts on research, products, and careers in satellite image deep learning).

Chart Rankings

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Apple Podcasts
#146
Argentina/Technology

Talking Points

Recent interactions between the hosts and their guests.

Chained Models for High-Res Aerial Solar Fault Detection
Q: Is there a way to benchmark your product against a fully human-based approach?
Currently, we haven't done a fully comprehensive comparison between human and AI analysis, but we've seen improvements in turnaround time for human-reviewed processes.
Chained Models for High-Res Aerial Solar Fault Detection
Q: What role will humans play in maintaining and building on these systems?
Humans will always be involved to ensure high precision and recall in anomaly detection.
Chained Models for High-Res Aerial Solar Fault Detection
Q: Why did you choose to use object detection for this project?
It became clear that treating this as a standard object detection problem was the most scalable solution after trying different approaches.
AutoML for Spaceborne AI
Q: What hardware do you work with for these AI models?
Roberto discusses using various devices like Nvidia Jetson chipsets, MediaTek from Intel, and boards developed by Bautica and KP Labs for processing models on satellites.
AutoML for Spaceborne AI
Q: How do you achieve the best performance with minimal power consumption?
It involves customizing models for specific tasks like wildfire detection and finding the right trade-off between accuracy and power usage to extend the lifetime of the satellite's mission.

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Frequently Asked Questions About Satellite image deep learning

What is Satellite image deep learning about and what kind of topics does it cover?

This show centers on practical advances in AI applied to satellite imagery, featuring researchers and engineers who build and deploy geospatial machine learning systems. Recent episodes cover topics like AutoML and hardware-aware model design for spaceborne AI, methane detection from Sentinel-5B data, tools for social good such as InstaGeo, streamlined data access for Copernicus data, high-resolution solar fault detection from aerial imagery, and open-source geospatial ML libraries like TorchGeo. Guests often discuss real-world constraints (power, bandwidth, data availability), deployment challenges, data curation, and the evolving ecosystem of geospatial AI tools and collaborations. The format tends to blend technical depth with pragmatic ... more

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Satellite image deep learning launched 3 years ago and published 42 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on Satellite image deep learning?

Recent guests on Satellite image deep learning include:

1. Adam Stewart
2. Shahab Jozdani
3. Kai Jeggle
4. Daniele Rege Cambrin
5. Giorgio Morales
6. Caleb Robinson

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