Satellite image deep learning

In this episode Robin catches up with Inon Sharony to learn about the fascinating world of machine learning with SAR imagery. The unique attributes of SAR imagery, such as its intensity, phase, and polarisation, provide rich information for deep learning models to learn features from. The discussion covers the innovative applications ASTERRA is developing, and the nuances of machine learning with SAR imagery. This video of this episode is available on YouTube* https://asterra.io/* https://www.linkedin.com/in/inonsharony/Bio: Inon Sharony is the Head of AI at ASTERRA, where he is responsible for pushing boundaries in the field of deep learning for earth observation. Sharony brings a decade of experience leading development of cutting-edge AI technology that meets real-world business and product needs. His previous roles include Algorithm Group Manager at Rail Vision Ltd and R&D Group Lead & Head of Automotive Intelligence at L4B Software. He was PhD trained in Chemical Physics at Tel Aviv University and combines his extensive academic background in Physics and his hands-on experience with machine learning to develop strategic AI solutions for ASTERRA. 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

In this episode, Robin catches up up with Alistair Francis and Mikolaj Czerkawski to learn about Major TOM, which is a significant new public dataset of Sentinel 2 imagery. Noteworthy for its immense size at 45 TB, Major TOM also introduces a set of standards for dataset filtering and integration with other datasets. Their aim in releasing this dataset is to foster a community-centred ecosystem of datasets, open to bias evaluation and adaptable to new domains and sensors. The potential of Major TOM to spur innovation in our field is truly exciting. Note you can also view the video of this recording on YouTube here. The video also includes a demonstration of accessing the dataset and a walkthrough of the associated Jupyter notebooks.* Dataset on HuggingFace* PaperAlistair Francis is a Research Fellow at the European Space Agency’s Φ-lab in Frascati, Italy. Having studied for his PhD at the Mullard Space Science Laboratory, UCL, his research is focused on image analysis problems in remote sensing, using a variety of supervised, self-supervised and unsupervised approaches to tackle problems such as cloud masking, crater detection and land use mapping. Through this work, he has been involved in the creation of several public datasets for both Earth Observation and planetary science. Mikolaj Czerkawski is a Research Fellow at the European Space Agency’s Φ-lab in Frascati, Italy. He received the B.Eng. degree in electronic and electrical engineering in 2019 from the University of Strathclyde in Glasgow, United Kingdom, and the Ph.D. degree in 2023 at the same university, specialising in applications of computer vision to Earth observation data. His research interests include image synthesis, generative models, and use cases involving restoration tasks of satellite imagery. Furthermore, he is a keen supporter and contributor to open-access and open-source models and datasets in the domain of AI and Earth observation. 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

In this video Robin catches up with Konstantin Klemmer to discus SatClip, which is a new global & general purpose location encoder trained on Sentinel 2 imagery. The conversation covered the training of encoders such as CLIP, and discussed the implications for downstream applications. Note you can also view the video of this recording on YouTube here* Konstantin on LinkedIn* SatCLIPBio: Konstantin is a postdoctoral researcher at Microsoft Research New England. His research interests lie broadly within geospatial machine learning and bridging adjacent domains like remote sensing or spatial statistics. Konstantin has a PhD from the University of Warwick and NYU, a Master's from Imperial College London and an undergraduate degree from the University of Freiburg, Germany. 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

James Gallagher, technical marketer at Roboflow, discusses the latest AI innovations in image annotation, including models like Segment Anything and GroundingDINO. They explore automated labeling with CLIP and the use of Auto Distill for image annotation in pipelines. The podcast also covers the changing process of creating datasets and highlights resources like Roboflow.com for AI-powered image annotation.

A large fraction of acquired satellite images contain 2D projections of Earth. However, for many downstream applications, 3D understanding is beneficial or necessary. In recent years, deep learning has enabled a number of solutions for learning 3D representations from 2D satellite images. This episode delivers an overview of some of the prominent works in this area. Mikolaj hosts 3 guests: Dawa Derksen, Roger Marí, and Yujiao Shi, providing a summary of each guest’s contributions on the topic as well as a panel discussion. Note you can also view the video of this recording on YouTube hereDawa Derksen - Origins of Shadow-NeRF Dawa pursued a post-doctoral research fellowship at the European Space Agency from 2020-2022, and is currently working at the Centre National d’Etudes Spatiales (French Space Agency) where he is involved in the field of 3D Implicit Representation Learning applied to Remote Sensing. * 🖥️ Shadow-NeRFRoger Marí - EO-NeRF Roger is a post-doc researcher from Barcelona specialised in 3D vision tasks. He is currently working at the Centre Borelli, ENS Paris-Saclay, in France, where his research topic is the application of neural rendering methods to satellite image collections. He is the author of Sat-NeRF and EO-NeRF, some of the first models in the literature to provide quantitatively convincing results in terms of surface reconstruction.* 🖥️ https://rogermm14.github.io/* 🖥️ EO-NeRFYujiao Shi - Connecting Satellite Image with StreetViewYujiao is a research fellow at the Australian National University. She obtained her PhD degree at the same institute. Her research interests include satellite image-based localisation, cross-view synthesis, 3D vision-related tasks, and self-supervised learning.* 🖥️ https://shiyujiao.github.io/* 📖 Geometry-Guided Street-View Panorama Synthesis from Satellite ImageryHost & Production: Mikolaj Czerkawskihttps://mikonvergence.github.io 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

In this episode Robin catches up with Jake Wilkins to learn about Deep learning in Google Earth Engine. Jake has been building commercial Earth Engine applications for the past three years and in this conversation he describes the pros and cons of several approaches to using deep learning models with Earth Engine. Note you can also view the video of this recording on YouTube here* Jake on LinkedIn* https://earthengine.google.com/Bio: Jake is a Software Engineer and Data Scientist based in London, UK. He has been building commercial Google Earth Engine applications for the past three years. His significant contributions include the no-code platform, Earth Blox, and the climate monitoring platform STRATA for UNEP (United Nations Environmental Programme). Alongside this, Jake has consistently developed his skills in machine learning, and a notable accomplishment in this field is winning the Earth-i hackathon last year. Jake has a deep passion for addressing the climate crisis and is committed to making Earth Observation more accessible to combat it. 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

In this episode Robin catches up with Roberto Del Prete to learn about PyRaws. PyRaws is a powerful open source Python package that provides a comprehensive set of tools for working with Sentinel 2 raw imagery. It provides tools for band coregistration, geo-referencing, data visualisation, and image processing. What is particularly exciting is that this software could be deployed onto future satellites, enabling on-board processing using python. Note you can also view the video of this recording on YouTube here* https://github.com/ESA-PhiLab/PyRawS * https://www.linkedin.com/in/roberto-del-prete-8175a7147/ Bio: Roberto Del Prete is a PhD candidate focused on expanding the uptake of Deep Learning for enhancing the applications of onboard edge computing. His aim is to improve decision-making in time-critical scenarios by reducing the time lag required to process and deliver useful information to the ground. He is also working on developing autonomous spacecraft navigation systems using onboard instruments like cameras. Through his research he wants to contribute to the advancement of AI technology and its real-world applications, pushing the boundaries of what is possible to accomplish onboard. 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

In this episode Robin catches up with Nathan Kundtz to learn about the creation, and use of synthetic image data in training machine machine models. Nathan has a PhD in physics, and over 40 peer reviewed papers and 15 patents to his name. As a serial entrepreneur, he has successfully founded multiple companies and raised over $250 million in venture capital funding. Note you can also view the video of this recording on YouTube here* Nathan LinkedIn* rendered.ai* DIRSIG 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

In the episode I caught up with the co-founder of the company developing Orbuculum, Derek Ding, to learn more about this innovative new platform. What makes Derek's story even more intriguing is that he doesn't have a traditional background in remote sensing. However, fuelled by ambition and a desire to introduce new technologies, he is determined to transform the landscape of the Earth observation data market. My conversation with Derek was thought-provoking, and offered valuable insights into the innovative possibilities within our field. I hope you enjoy this episode. Please note the video is also available on YouTube* 🖥️ Orbuculum website* 📺 Demo video of Orbuculum platform* 🗣️ Orbuculum Discord* 💻 Orbuculum Github* 🐦 Orbuculum Twitter 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

In this episode, Robin catches up with Ryan Avery to learn about the machine learning workflow at Development Seed. The making of this episode was inspired by a three part blog series Ryan has authored on the ML tooling stack used at Development Seed. Please note the video is also available on YouTube- https://developmentseed.org/blog/2023-04-13-ml-tooling-3 - https://www.linkedin.com/in/ryan-avery-75b165a8/ Bio: Ryan is an expert in developing machine learning-powered services for processing satellite and camera trap imagery, and he is deeply passionate about leveraging machine learning to enhance environmental outcomes and improve livelihoods. In addition to his work at Development Seed, Ryan has made significant contributions to open-source. These include a comprehensive two-day geospatial python curriculum, an image segmentation model service, and a torchserve deployment of Megadetector for wildlife monitoring. 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