In this episode I sat down with Mikolaj (Miko) Czerkawski from Asterisk Labs to explore BetaEarth, an experimental open-source emulator trained on AlphaEarth Foundations' public embedding archive. AEF — released by Google and Google DeepMind as a global 10 m embedding product derived from a wide range of Earth-observation modalities — is what makes BetaEarth possible: its openness lets the community build lightweight independent emulators that approximate AEF's pixelwise outputs from standard Sentinel inputs, and use them to probe how much of a model's behaviour is captured in its public embeddings. Miko walks through BetaEarth's design — compact architectures based on SegFormer-B2 with separate per-modality encoders, and a shared DINOv3 backbone over 3-band spectral primitives — and the surprising finding that reasonably strong approximations can be achieved even from simple RGB inputs.We then dive into a live demo: generating BetaEarth embeddings for arbitrary regions and time ranges using Sentinel-1, Sentinel-2, and COP-DEM data. Along the way, we cover practical considerations such as cloud contamination, modality trade-offs, tiling artefacts, and strategies for merging multi-temporal signals. Finally, we discuss what this complementary tooling enables for the geospatial ML community — embeddings as pretraining or regularisation signals, lightweight local inference alongside AEF's global annual rasters, and what the combination of large proprietary archives and open emulator-style tools could unlock next.

* 📺 Video of this conversation & demo on YouTube

* 🖥️ BetaEarth Github page

* 🖥️ BetaEarth demo on Huggingface

Bio: Miko is a researcher specialising in AI, computer vision, signal processing and Earth observation. Before co-founding Asterisk Labs he was a postdoctoral research fellow at the European Space Agency. His research interests include data-centric analyses of large-scale Earth observation data, dataset curation, generative modelling, and restoration tasks for satellite imagery. He is a co-founder of the Major TOM community project, a platform for collaborating and reusing Earth observation datasets designed specifically for AI pipelines. 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 institution, specialising in applications of computer vision to Earth observation data.

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 the most widely used open-source annotation tools, and how it has evolved alongside the shift from traditional computer vision to deep learning. Kentaro explains the impact of foundation models like Segment Anything (SAM), and how annotation is rapidly moving toward a prompt-and-verify paradigm where models do the heavy lifting and humans focus on quality control. We also dive into his recent work integrating SAM into LabelMe, the challenges of applying these models to satellite imagery, and why approaches like bounding-box prompting outperform text in that domain. Finally, we cover new support for large, multi-channel geospatial data, practical deployment considerations, and what this means for scaling annotation in real-world machine learning systems. Note that a recording of this conversation, along with a demonstration of geospatial annotation using LabelMe, is available on YouTube via the links below:

* 🖥️ LabelMe website

* 🖥️ Kentaro’s personal website

* 📺 Video of this conversation on YouTube

* 📺 Demo video on YouTube

Bio: Kentaro Wada was born in Japan in 1994. He received his B.Sc. (2016) and M.Sc. (2018) from Mechanical Engineering and Computer Science Department in The University of Tokyo (UTokyo). In his research at UTokyo, he was working on learning-based scene understanding for robotic manipulation at JSK Laboratory supervised by Prof. Masayuki Inaba and Prof. Kei Okada. He received his PhD in 2022, at Dyson Robotics Laboratory in Imperial College London supervised by Prof. Andrew Davison. During his PhD, he worked on object-level semantic scene understanding, a general scene representation useful for robotic manipulation, and showed several novel capabilities of robots. He joined Mujin, Inc. in 2022 as a computer vision engineer, and is working on advancing robots' capabilities in the real-world environment.

In this episode I caught up with Adam Stewart, creator of TorchGeo, to hear all the latest updates related to this pivotal piece of geospatial AI software. We discuss TorchGeo’s strong adoption in the geospatial ML community and the upcoming 1.0 release, which will introduce long-awaited time series support. Adam shares insights from a recent software literature review covering available geospatial data handling tools, sampling strategies, and the broader machine learning ecosystem. He also talks about the newly formed Technical Steering Committee, outlining its role in guiding the project’s direction. Other topics include upcoming breaking changes to geospatial datasets and samplers, how TorchGeo integrates with other libraries and tools, the project’s growing community, the role of foundation models in handling diverse geospatial products, the promise of zero-shot learning for effortless data labelling, and why no single model can dominate across all domains.

* 👤 Adam on LinkedIn

* 🖥️ TorchGeo

* 📺 Video of this conversation on YouTube

Bio: Adam J. Stewart's research interests lie at the intersection of machine learning and Earth science, especially remote sensing. He is the creator and lead developer of the popular TorchGeo library, a PyTorch domain library for working with geospatial data and satellite imagery. His current research focuses on building foundation models for multispectral imagery. He received his B.S. from the Department of Earth and Atmospheric Sciences at Cornell University and his Ph.D. from the Department of Computer Science at the University of Illinois Urbana-Champaign. He currently works as a postdoctoral researcher at the Technical University of Munich under the guidance of Prof. Xiaoxiang Zhu.

In this episode I caught up with Tobias Augspurger to explore the Map Your Grid initiative at Open Energy Transition, an ambitious project funded by Breakthrough Energy to build a digital twin of the global electrical grid. While AI and machine learning are being used to detect substations, pylons, and transmission lines in satellite imagery, Toby explains why these approaches alone can’t deliver a complete, accurate map. We discussed the false positives, missing connections, and contextual details that challenge automated models, and how human validation and open-source mapping remain essential to producing reliable, global-scale infrastructure data.

* 👤 Toby on LinkedIn

* 🖥️ mapyourgrid.org

* 📺 MapYourGrid YouTube Channel

* 📺 Video of this conversation on YouTube

Bio: Tobias Augspurger is a climate technology innovator and open-source advocate. At Open Energy Transition, he is accelerating the global energy transition by standardising electrical grid data within OpenStreetMap as part of the MapYourGrid initiative. With a PhD in atmospheric sciences and a background in aerospace engineering, Tobias combines technical expertise in remote sensing with inclusive collaboration. In his spare time, he works on OpenSustain.tech and ClimateTriage.com, connecting and promoting open projects to combat climate change and biodiversity loss

In this episode, I catch up with Federico Bessi to dive into a fascinating end-to-end project on the automatic detection of photovoltaic (PV) solar plants using satellite imagery and deep learning. Federico walks us through how he built a complete pipeline—from sourcing and preprocessing data using the Brazil Data Cube, to annotating solar farms in QGIS, training models in PyTorch, and finally deploying a web app on AWS to visualise the predictions.

This is interesting because solar energy infrastructure is expanding rapidly, yet tracking it globally remains a major challenge. This project demonstrates how open data and modern ML tools can be combined to monitor solar installations at scale—automatically and remotely. It's a compelling example of applied geospatial AI in action.

This video is ideal for remote sensing practitioners, machine learning engineers, and anyone interested in environmental monitoring, Earth observation, or building practical AI systems for real-world deployment.

* 🖥️ Project code on Github

* 👤 Federico on Linkedin

* 📺 Video of this conversation on YouTube

* 📺 Project demo on YouTube

Bio: Federico Bessi is a Software Engineer specializing in Machine Learning, with an international background in the software, computer vision, and biometrics industries. He spent over a decade working in biometric identification for global tech companies, contributing to national ID systems across more than seven countries. In these roles, he developed software, led engineering teams, and oversaw large-scale system operations. Building on this foundation, Federico has deepened his work in machine learning and deep learning, applying it to business intelligence, user satisfaction modeling, and geospatial analysis using satellite imagery. He also became a contributor with the open-source TorchGeo project.

In this conversation, I caught up with Shahab Jozdani to learn about Chat2Geo, a web-based application designed to simplify remote-sensing-based geospatial analysis through an intuitive, chatbot-style interface.

Large language models, such as ChatGPT, are reshaping the way users interact with complex datasets, and it’s inspiring to see innovators like Shahab leverage this technology to democratise geospatial analytics. Note that we also recorded a demonstration video of Chat2Geo, which is linked below:

* 🖥️ Chat2Geo on Github

* 👤 Shahab on LinkedIn

* 📺 Video of this conversation on YouTube

* 📺 Demo of Chat2Geo on YouTube

Bio: Data Scientist and Geomatics Engineer with over 10 years of experience in academia and industry, specialising in AI, computer vision, data science, software development, and building new solutions. Founder of GeoRetina, a Canadian company that developed and open-sourced Chat2Geo, an AI-powered platform providing real-time geospatial insights via conversational interfaces

In this episode, I caught up with Nick Wright to discuss OmniCloudMask—a Python library for state-of-the-art cloud and cloud shadow masking in satellite imagery.

Accurate cloud masking is crucial for reliable downstream analytics, yet creating models that generalise well across different sensors, resolutions, and atmospheric conditions remains a significant challenge.

OmniCloudMask addresses this through a novel image preprocessing pipeline and clever augmentation strategies that vary the image resolution presented to the model. Model generalisation is a key concern for practitioners in our field, and I found this conversation both insightful and practical—I hope you do too.

* 📃 Paper

* 🖥️ Code

* 📺 Video of this conversation on YouTube

* 👤 Nick on LinkedIn

Bio: Nick Wright is a Senior Research Scientist at the Western Australian Department of Primary Industries and Regional Development. He is also pursuing a PhD at the University of Western Australia, focusing on deep learning applications for environmental remote sensing, specifically in cloud and water detection and sensor-agnostic models.

In this episode, I caught up with James Doherty and Donal Hill, co-founders of Planetixx (formerly Plastic-i), a company using satellite imagery and AI to monitor ocean debris. Their platform not only detects plastic and other debris, but also predicts its origins and trajectory, enabling more effective interventions. Beyond plastic, they’ve expanded into monitoring algal blooms, a growing environmental concern.

The conversation covers the technical and practical challenges of building AI models that work at a global scale, as well as their newly launched platform. A live demo of the platform is available as a separate video, linked below

* 🖥️ Planetixx website

* 👤 James LinkedIn

* 👤 Donal LinkedIn

* 📺 Video of this conversation on YouTube

* 📺 Platform demo on YouTube

Bio: Dr. James Doherty is CEO and Co-Founder of Earthshot-nominated enterprise Planetixx, where he drives environmental innovation in tackling marine plastic pollution and promoting ocean health. His unique expertise spans astronomy, data science, and law, combining scientific rigour with legal acumen. James holds a PhD in Astronomy, law degrees from the Universities of Cambridge and Oxford, and is a Science to Data Science (S2DS) Fellow. His professional background includes practising as a commercial lawyer at Eversheds Sutherland before applying his diverse skill set to environmental entrepreneurship.

Bio: Dr. Donal Hill is Chief Technical Officer and Co-Founder of Planetixx, where he leads technology development initiatives in satellite data and artificial intelligence applications. His expertise spans particle physics, data science, and AI mplementation across research and industry. Donal holds a PhD in Particle Physics from the University of Oxford and spent ten years at CERN's Large Hadron Collider. His distinguished career includes serving as a Marie Curie Fellow at École Polytechnique Fédérale de Lausanne (EPFL) and holding senior data scientist positions at UEFA and the Swiss Data Science Center, where he facilitates AI adoption for industry partners.

In this episode, I caught up with Kai Jeggle to discuss his experience pursuing a PhD at the intersection of machine learning and remote sensing. The conversation covers Kai's work on IceCloudNet, a deep learning model that reconstructs 3D cloud structures from 2D imagery with sparse depth measurements. Data fusion and sparse machine learning are fascinating topics. I learned a lot from this conversation, and I hope you do to.

* 👤 Kai on LinkedIn

* 📃 IceCloudNet paper

* 🖥️ Code

* 💾 Dataset

* 📺 Video of this conversation on YouTube

Bio: Kai is passionate about leveraging machine learning to tackle climate change. His research lies at the intersection of ML, remote sensing, and climate science. He studied industrial engineering and computer science before completing his PhD in Atmospheric Physics at ETH Zurich under Prof. Ulrike Lohmann, with visiting research stays at UV Valencia and the ESA Phi Lab. He also worked as a software engineer at the Stockholm-based MLOps startup LogicalClocks. Kai is a core team member and former vice-chair of Climate Change AI, a global non-profit that catalyses impactful work at the intersection of climate change and machine learning. In his next role, he will join the meteo data team at Dexter Energy in Amsterdam, working to improve renewable energy yield forecasts.

In this episode, I caught up with Daniele Rege Cambrin, the organiser of the SMAC earthquake detection challenge, and Giorgio Morales, its winner. The challenge invited participants to leverage Sentinel 1 satellite imagery to identify earthquake-affected areas and measure the strength of events, while promoting scalable and resource-efficient solutions.

Giorgio shared his innovative approach that secured first place, and we explored the effort behind designing and solving such a meaningful challenge. This conversation provides valuable insights into developing effective solutions and showcases the potential of satellite data in earthquake monitoring.

* 📺 Video of this conversation on YouTube

* 🖥️ SMAC website

* 🖥️ Website of Giorgio

Bio: Giorgio is a PhD candidate (ABD) in computer science at Montana State University and a current member of the Numerical Intelligent Systems Laboratory (NISL). He holds a BS in mechatronic engineering from the National University of Engineering, Peru, and an MS in computer science from Montana State University, USA. His research interests are Deep Learning, Explainable Machine Learning, Computer Vision, and Precision Agriculture.

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.

In this episode, I caught up with Marek Kraft to learn about the Deepness QGIS plugin.

QGIS is a widely used open-source tool for working with geospatial data. It’s written in Python, and its functionality can be expanded with plugins. One plugin that recently caught my attention is Deepness, developed by Marek and his team.

Deepness makes it straightforward to use deep learning models in QGIS. You don’t need specialised hardware like GPUs, and it offers a range of pre-trained models through a model zoo.

As a long-time QGIS user, I was thrilled to discover Deepness, and I believe it has the potential to make deep learning much more accessible to geospatial practitioners without deep learning expertise. Marek shared some fascinating examples of how the plugin is being used, and discussed the growing community around it.

* 📺 Demo video showcasing Deepness in action

* 📺 Video of this conversation on YouTube

* 👤 Marek on LinkedIn

* 🖥️ PUT Vision Lab

* 📖 Deepness documentation

* 🖥️ Deepness Github page

Bio: Marek Kraft is an assistant professor at the Poznań University of Technology (PUT), where he leads the PUT Computer Vision Lab. The lab focuses on developing

intelligent algorithms for extracting meaningful information from images, videos, and signals. This work has applications across diverse fields, including Earth

observation, agriculture, and robotics (including space robotics). Kraft's current research involves close-range remote sensing image analysis, specialising in small object detection for environmental monitoring. He also collaborated on European Space Agency projects aimed at extraterrestrial rover navigation and autonomy, making use of his knowledge of embedded systems. His research has led to over 80 publications, several patents, and a history of securing competitive research grants. Kraft is a member of IEEE and ACM.

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 models that generalise across vastly different agricultural systems, and why data diversity, rather than model architecture, is often the limiting factor. Hannah and Tristan explain how targeted annotation in underperforming regions can dramatically improve results, how combining global and local training data avoids catastrophic forgetting, and what they learned from large-scale model experimentation. We also dig into practical evaluation beyond standard IOU metrics, including consistency and throughput, and how small modelling choices like boundary loss weighting can have outsized impact on usability. Finally, we cover the growing tooling ecosystem, real-world user feedback, and what’s coming next, including improved models and a global map of predicted field boundaries.

* 🖥️ FTW website

* 📺 Recording of this conversation on YouTube

Bio Hannah: Hannah Kerner is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University. Her research focuses on advancing the foundations and applications of machine learning to foster a more sustainable, responsible, and fair future for all. Her lab’s research topics include machine learning for remote sensing, algorithmic bias, and machine learning theory. She translates research advances to real-world impact through her roles as the AI/Machine Learning Lead for NASA Harvest and NASA Acres, Center Faculty for the ASU Center for Global Discovery and Conservation Science (GDCS), and Research Director for Taylor Geospatial. She has been recognised by multiple research awards including NSF CAREER (2025), Schmidt Sciences AI2050 Early Career Fellowship (2025), and Forbes 30 Under 30 in Science (2021).

Bio Tristan: Tristan Grupp is an Agricultural Data Scientist in the Food, Land, and Water Program and Data Lab at the World Resources Institute. He collaborates closely with Land and Carbon Lab. His current research focuses on applying remote sensing and machine learning to monitor deforestation and natural land conversion driven by agricultural supply chains, supporting commodity traceability and corporate sustainability compliance, including under the EU Deforestation Regulation (EUDR). His work spans forest change monitoring, climate adaptation, and the intersections of food systems and natural landscapes. Beyond WRI, Grupp has contributed to research on climate change adaptation tracking in support of national adaptation planning under the UNFCCC, protected area policy evaluation in the EU, and tropical forest dynamics in the Peruvian Amazon. He has presented his work at international venues including AGU, COP, and the UN National Adaptation Planning Conference.

In this episode, I caught up with Nicolas Gonthier to learn about the FLAIR land cover mapping challenge. 

In this challenge, 20cm resolution aerial imagery was used to create high-quality annotations. This data was paired with a time series of medium-resolution Sentinel 2 images to create a rich, multidimensional dataset. Participants in the challenge were able to surpass the baseline solution by 10 points in the target metric, representing a significant step forward in land cover classification capabilities. 

The dataset is now being expanded to cover a larger area and incorporate additional imaging modalities, which have been shown to improve performance on this task. Nicolas also provided important context about the objectives of the organisation running this challenge, such as the need to balance model performance with processing costs.

* 🖥️ FLAIR website

* 🖥️ Page on the objectives of FLAIR

* 📖 The NeuRIPS paper about FLAIR

* 🤗 IGN on HuggingFace

* 🖥️ IGN datahub

* 👤 Nicolas on LinkedIn

* 📺 Video of this conversation on YouTube

Bio: Nicolas Gonthier is a R&D project manager in the innovation team at IGN the French National Institute of Geographical and Forest Information. He received a MSc. in data science from ISAE Supaero in 2017 and a Ph.D. degree in computer vision from Université Paris Saclay - Télécom Paris in 2021. His work focus on deep learning for earth observation (land cover segmentation, change detection, etc) and computer vision for geospatial data. He participate to different research and innovation projects.

In this episode, I caught up with Marc Rußwurm to learn about Meta-learning with Meteor. Our conversation starts with a discussion about meta-learning and the training of Meteor, and how this approach differs from the typical approaches taken to train foundational models. We cover the advantages and challenges of this technique, and discuss the fine-tuning of Meteor with minimal examples—as few as five—for tasks like deforestation monitoring and change detection, and consider what the future could hold for this approach. Meteor showcases the significant potential of few-shot learning for processing remote sensing imagery and proves it's possible to tackle tasks even when very few training examples are available.

* 👤 Marc on LinkedIn

* 📖 Meteor Nature paper

* 💻 Meteor code on Github

* 📺 Video of this conversation on YouTube

Bio: Marc Rußwurm is Assistant Professor of Machine Learning and Remote Sensing at Wageningen University. His background is in Geodesy and Geoinformation, and he obtained a Ph.D. in Remote Sensing Technology at TU Munich. During his Ph.D., he could visit the European Space Agency and the University of Oxford as a participant in the Frontier Development Lab in 2018, the Obelix Laboratory in Vannes, and the Lobell Lab in Stanford. As a postdoctoral researcher, he joined the Environmental Computational Science and Earth Observation Laboratory at EPFL, Switzerland. His research interests are developing modern machine learning methods for real-world remote sensing problems, such as classifying vegetation from satellite time series and detecting marine debris in the oceans. He is interested in domain shifts and transfer learning problems naturally arising from geographic data.

In this episode, I caught up with Nils Lehmann to learn about Uncertainty Quantification for Neural Networks. The conversation begins with a discussion on Bayesian neural networks and their ability to quantify the uncertainty of their predictions. Unlike regular deterministic neural networks, Bayesian neural networks offer a more principled method for providing predictions with a measure of confidence.

Nils then introduces the Pytorch Lightning UQ Box project on GitHub, a tool that enables experimentation with a variety of Uncertainty Quantification (UQ) techniques for neural networks. Model interpretability is a crucial topic, essential for providing transparency to end users of machine learning models. The video of this conversation is also available on YouTube here

* Nils’s website

* Lightning UQ box on Github

* Further reading: A survey of uncertainty in deep neural networks

Bio: Nils Lehmann is a PhD Student at the Technical University of Munich (TUM), supervised by Jonathan Bamber and Xiaoxiang Zhu, working on uncertainty quantification for sea-level rise. More broadly his interests lie in Bayesian Deep Learning, uncertainty quantification and generative modelling for Earth Observational data. He is also passionate about open-source software contributions and a maintainer of the Torchgeo package.

In this episode I caught up with Samuel Bancroft to learn about segmenting field boundaries using Segment Anything, aka SAM. SAM is a foundational model for vision released by Meta, which is capable of zero shot segmentation. However there are many open questions about how to make use of SAM with remote sensing imagery.

In this conversation, Samuel describes how he used SAM to perform segmentation of field boundaries using Sentinel 2 imagery over the UK. His best results were obtained not by fine tuning SAM, but by carefully pre-processing a time series of images into HSV colour space, and using SAM without any modifications. This is a surprising result, and using this kind of approach significantly reduces the amount of work necessary to develop useful remote sensing applications utilising SAM. You can view the recording of this conversation on YouTube here

- Samuel on LinkedIn

- https://github.com/Spiruel/UKFields

Bio: Sam Bancroft is a final year PhD student at the University of Leeds. He is assessing future food production using satellite data and machine learning. This involves exploring new self- and semi- supervised deep learning approaches that help in producing more reliable and scalable crop type maps for major crops worldwide. He is a keen supporter in democratising access to models and datasets in Earth Observation and machine learning.

In this episode I caught up with Yotam Azriel to learn about interpretable deep learning. Deep learning models are often criticised for being "black box" due to their complex architectures and large number of parameters. Model interpretability is crucial as it enables stakeholders to make informed decisions based on insights into how predictions were made. I think this is an important topic and I learned a lot about the sophisticated techniques and engineering required to develop a platform for model interpretability. You can also view the video of this recording on YouTube.

* tensorleap.ai

* Yotam on Linkedin

Bio: Yotam is an expert in machine and deep learning, with ten years of experience in these fields. He has been involved in massive military and government development projects, as well as with startups. Yotam developed and led AI projects from research to production and he also acts as a professional consultant to companies developing AI. His expertise includes image and video recognition, NLP, algo-trading, and signal analysis. Yotam is an autodidact with strong leadership qualities and great communication skills.

In this episode I caught up with Daniele Rege Cambrin, to learn about Earthquake detection with Sentinel-1 (SAR) images. Daniele has a key role in organising a new competition on this task, SMAC: Seismic Monitoring and Analysis Challenge. The topics covered include the logistics of organising this competition, and the lessons Daniele learned from organising a previous one. You can also view the recording of this discussion on YouTube.

- Daniele on LinkedIn

- Competition website

Bio: Daniele Rege Cambrin is currently pursuing his Ph.D. and his research interests lie in deep learning. He is particularly interested in finding efficient and scalable solutions in areas such as remote sensing, computer vision, and natural language processing. Additionally, he has a keen interest in game development, and worked on two machine-learning competitions related to change detection.

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.

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

* Paper

Alistair 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.

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

* SatCLIP

Bio: 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.

In this episode Robin catches up with James Gallagher to learn about the latest AI innovations reshaping image annotation. The conversation covered significant new models such as Segment Anything, GroundingDINO and RemoteCLIP, and discussed how these models can be linked together to enable new annotation capabilities. Note you can also view the video of this recording on YouTube here

* James on LinkedIn

* Autodistill on Github

* Roboflow

Bio: James is a technical marketer at Roboflow, and has written over 100 guides on computer vision, covering areas from CLIP to dataset distillation and model evaluation. He also maintains several open source software packages at Roboflow, including Autodistill, a framework for auto-labelling images. In his free time, James has a unique hobby; he maintains a website that catalogues pianos available for public use in airports around the globe at airportpianos.org

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-efficient, and deployable across diverse hardware and data regimes, and why moving beyond fixed architectures is key to achieving that. Isaac explains how RF-DETR combines strong foundation backbones like DINOv2 with efficient neural architecture search to unlock novel speed–accuracy trade-offs, including dropping decoder layers and queries after training. We also discuss the model’s strong transfer performance on domains far from COCO, the introduction of a memory-efficient instance segmentation head, and the team’s unusually rigorous benchmarking approach, before closing on the challenges of open-source research and upcoming improvements to inference and platform integration.

* 👤 Isaac on LinkedIn

* 🖥️ RF-DETR on Github

* 📖 Paper

* 📺 Video of this conversation on YouTube

Bio: Isaac Robinson is a Machine Learning Research Engineer at Roboflow. He’s worked across the field of computer vision, from real-time stereo depth estimation on household robots to biomedical research at the NIH to founding a zero shot computer vision infrastructure startup. Isaac focusses on the intersection of low latency and high performance, with the goal of helping people unlock new capabilities through vision.

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 here

Dawa 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-NeRF

Roger 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-NeRF

Yujiao Shi - Connecting Satellite Image with StreetView

Yujiao 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 Imagery

Host & Production: Mikolaj Czerkawski

https://mikonvergence.github.io

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.

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.

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

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

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.

In this video, Robin catches up with Michael Bewley to hear about the use of AI at Nearmap. Nearmap captures very high resolution aerial imagery and Michael and his team have trained a single segmentation model to identify 78 different target layers in the imagery. These layers can then be displayed on a map or accessed via an API. Please note the video is also available on YouTube

* Michael on LinkedIn

* Nearmap

* Nearmap AI docs

Bio: Michael is the Vice President of AI and Computer Vision at Nearmap. He's worked as a data scientist in a range of areas including medical devices, underwater robotics and banking. For the last six years, he's been building machine learning based products on top of Nearmap's technology stack of Australian designed aerial imaging cameras, and one of the biggest aerial capture, photogrammetry and 3D reconstruction programs in the world.

Join Robin in a career chat with Martha Morrisey, a senior machine learning engineer at Pachama, a company elevating remote sensing data and machine learning to fight climate change by monitoring carbon capture and storage projects in forests. In this episode, Martha shares her career journey and provides further insight into the role of a machine learning engineer.

* Martha on LinkedIn

* Pachama website

* Video on YouTube

Bio: Martha is a senior Machine Learning Engineer at Pachama. Prior to Pachama Martha worked at Development Seed, and Maxar. Martha has an undergraduate degree from UC Berkeley in Geography, and a master's degree in Geography from the University of Colorado, Boulder. Outside of work Martha loves spending time outside cycling, running, and attempting to take her cat on walks!

In this episode, Robin catches up with Gilberto Camara to talk about SITS. SITS is an open-source R package for land use and land cover classification of big Earth observation data using satellite image time series. Gilberto is a Senior Researcher in GIScience, Geoinformatics, Spatial Data Science and Land Use Change at Brazil’s National Institute for Space Research.

* https://github.com/e-sensing/sits

* https://gilbertocamara.org/

* Video on YouTube

Bio: Prof. Dr. Gilberto Câmara is a Brazilian researcher in Geoinformatics, GIScience, Spatial Analysis, and Land Use Modelling, who works at Brazil's National Institute for Space Research (INPE). He is internationally recognized for promoting free access for geospatial data and for setting up an efficient satellite monitoring of the Brazilian Amazon rainforest. After retiring from INPE in June 2016 after 35 years of work, he continues to conduct R&D activities at INPE as a Senior Research Fellow.

Logo animation and thumbnail credits: Mikolaj Czerkawski @mikonvergence

In this career chat, Robin catches up with Nishant Yadav to hear about his path from PhD to Applied Scientist (II) at Microsoft Azure AI working on computer vision. Nishant graduated from Northeastern University in Boston, US, with a Ph.D. in machine learning with applications in environmental and climate science. His research focused on developing deep transfer learning methods for extracting information from remotely-sensed data (e.g., satellite imagery). Nishant is an AI optimist, and his current favourite hobby is learning more about generative AI.

* https://www.linkedin.com/in/nisyad/

* 📺 Video of this chat on YouTube

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 beyond single-date imagery is essential for understanding phenology, land cover, and environmental change, and how aggregating a full year of Sentinel-1 and Sentinel-2 observations enables far richer representations of the Earth’s surface. We dive into the unique engineering challenges behind Tessera, including its unusual cost profile where inference is more expensive than training, the need to ingest petabyte-scale archives, and the design choices required to scale a pixel-based model without representation collapse. Frank walks through their self-supervised training strategy based on redundancy reduction (Barlow Twins), while Sadiq highlights how downstream evaluations—from wildfire analysis to land-cover mapping—demonstrate that the embeddings already encode meaningful temporal and semantic structure. We also discuss the practical impact for ecology and conservation, where Tessera dramatically accelerates research workflows and reduces label requirements, and look ahead to Tessera v2, which will incorporate Landsat data to extend embeddings back to the 1970s and unlock new capabilities in change detection and forecasting.

* 📺 This conversation on YouTube

* 🖥️ Tessera on Github

* 📖 Paper

* 🖥️ Franks website

* 🖥️ Sadiqs website

Slides discussed in the episode

In this episode, Robin catches up with Michail Tarasiou to discuss the new paper, ViTs for SITS: Vision Transformers for Satellite Image Time Series. This paper introduces the temporo-spatial vision transformer (TSViT) architecture. The TSViT incorporates novel design choices that make it suitable for time series tasks such as crop classification. In this work, TSViT crop classification and segmentation models are trained and evaluated on Sentinel 2 datasets and achieve state of the art (SOTA) results on these tasks by a significant margin. This is an exciting step towards high accuracy and low cost & automated crop mapping using remote sensing imagery.

Paper authors: Michail Tarasiou, Erik Chavez, Stefanos Zafeiriou

* 📖 Paper

* 💻 Code on Github

* 📘 Transformers in remote sensing blog post

* 👤 Michail on LinkedIn

In this episode of the career chat series, Robin catches up with Zhuang-Fang NaNa Yi to hear about her career path into the role of senior machine learning engineer at Regrow Ag

* https://www.linkedin.com/in/zhuang-fang-yi-phd-01178a34/

* https://www.regrow.ag/

Bio: Zhuang-Fang NaNa Yi is a senior machine learning engineer at Regrow Ag. Her day-to-day work involves building R&D and machine learning models to scale and generate accurate machine learning-derived data layers for sustainable and regenerative agriculture at Regrow. Formerly, she was a machine learning engineer & GeoAI team lead at Development Seed and a research scientist at World Agroforestry Centre. She had a Ph.D. in Ecology from the Chinese Academy of Sciences and a B.S. in Geography from Sun Yat-Sen University. Outside of work, she is an artist, and you can often find her work at local art galleries, art shows, and art centres in the DC area.

In this podcast, Robin catches up with Philip Robinson to discuss his career path, and hear how he transitioned from working in computer security research, to working on environmental and satellite imaging challenges at the Global Fishing Watch.

- https://www.linkedin.com/in/philip-robinson-2878642a/

- https://globalfishingwatch.org/

Bio: Philip Robinson is a Scientific Programmer at Global Fishing Watch. Global Fishing Watch works to increase transparency of human activity at sea, by enabling scientific research in how we manage our ocean. Philip transitioned his career from computer security research to environmental and satellite imaging work. His masters studies were in deep learning for marine acoustic anomaly detection, and he is particularly interested in environmental auditing and citizen science problems.

Logo and thumbnail credits: Mikolaj Czerkawski @mikonvergence

In this episode I caught up with Roberto del Prete to learn about his work on AutoML for in-orbit model deployment, and how it enables satellites to run highly efficient AI models under severe power and hardware constraints. Roberto explains why traditional computer-vision architectures—optimised for ImageNet or COCO—are a poor fit for narrow, mission-specific tasks like wildfire or vessel detection, and why models must be co-designed with the actual edge devices flying in space. He describes PyNAS, his neural architecture search framework, in which a genetic algorithm drives the optimisation process, evolving compact, hardware-aware neural networks and profiling them directly on representative onboard processors such as Intel Myriad and NVIDIA Jetson. We discuss the multiobjective challenge of balancing accuracy and latency, the domain gap between training data and new sensor imagery, and how lightweight models make post-launch fine-tuning and updates far more practical. Roberto also outlines the rapidly changing ecosystem of spaceborne AI hardware and why efficient optimisation will remain central to future AI-enabled satellite constellations.

* 🖥️ PyNAS on Github

* 📖 Nature paper

* 📺 Video of this conversation on YouTube

* 👤 Roberto on LinkedIn

Bio

Roberto is an Internal Research Fellow at ESA Φ-lab specialising in deep learning and edge computing for remote sensing. He focuses on improving time-critical decision-making through advanced AI solutions for space missions and Earth monitoring. He holds a Ph.D. at the University of Naples Federico II, where he also earned his Master’s and Bachelor’s degrees in Aerospace Engineering. His notable work includes the development of “FederNet,” a terrain relative navigation system. Del Prete’s professional experience includes roles as a Visiting Researcher at the European Space Agency’s Φ-Lab and SmartSat CRC in Australia. He has contributed to key projects like Kanyini Mission, and developed AI algorithms for real-time maritime monitoring and thermal anomaly detection. He co-developed the award-winning P³ANDA project, a compact AI-powered imaging system, earning the 2024 Telespazio Technology Contest prototype prize. Co-author of more than 30 scientific publications, Del Prete is dedicated to leveraging advanced technologies to address global challenges in remote sensing and AI.

In this episode I caught up with Julia Wąsala to learn about methane plume detection using AutoML, and how her research bridges atmospheric science and machine learning. Julia explains the unique challenges of working with TROPOMI data—extremely coarse spatial resolution, single-channel methane measurements, and complex auxiliary fields that sometimes create plume-like artefacts leading to false detections. She walks through how her approach generalises a traditional two-stage detection pipeline to multiple gases using AutoMergeNet, a neural architecture search framework that automatically designs multimodal CNNs tailored to different atmospheric gases. We discuss why methane matters, how model performance shifts dramatically between curated test sets and real-world global data, and the ongoing effort to understand sampling bias and improve operational precision.

* 📖 AutoMergeNet paper

* 🖥️ Code on Github

* 🖥️ Julia’s homepage

* 📺 Recording of this conversation on YouTube

Bio: Julia Wąsala is currently working toward the Ph.D. degree in automated machine learning for Earth observation with the Leiden Institute for Advanced Computer Science, Leiden University, Leiden, The Netherlands, and with Space Research Organisation Netherlands, Leiden, The Netherlands. Her research focuses on the field of automated machine learning for earth observation focuses on designing new methods and validating them in real-world applications, such as atmospheric plume detection.

In this episode, I caught up with Ibrahim Salihu Yusuf from InstaDeep’s AI for Social Good team to hear the story behind InstaGeo, an open-source geospatial machine learning framework built to make multispectral satellite data easy to use for real-world applications. Ibrahim explains how the 2019–2020 locust outbreak exposed a gap between freely available satellite imagery, existing machine learning models, and the lack of tools to turn raw data into model-ready inputs. He walks through how InstaGeo bridges this gap - fetching, processing, and preparing multispectral data; fine-tuning models such as NASA IBM’s Prithvi; and delivering end-to-end inference and visualisation in a unified app. The conversation also covers practical use cases, from locust breeding ground detection to damage assessment, air quality, and biomass estimation, as well as the team’s efforts to partner with field organisations to drive on-the-ground impact.

* 👤 Ibrahim on LinkedIn

* 🖥️ InstaGeo on Github

* 📖 Paper on InstaGeo

* 📺 Video of this conversation on YouTube

* 📺 Demo of InstaGeo on YouTube

Bio: Ibrahim is a Senior Research Engineer and Technical Lead of the AI for Social Good team at InstaDeep’s Kigali office, where he applies artificial intelligence to address real-world challenges and drive social impact across Africa and beyond. With expertise spanning geospatial machine learning, computer vision, and computational biology, he has led high-impact projects in food security, disaster response, and immunology research. He also leads the development of InstaGeo, a platform designed to democratise access to AI-powered insights from open-source satellite imagery, reflecting his commitment to using cutting-edge AI for meaningful societal benefit.

In this episode, Roberto from ESA’s Φ-lab in Frascati introduces PhiDown, a community-driven open-source tool designed to simplify data access from the Copernicus Data Space Ecosystem (CDSE). He explains why PhiDown was created, how it uses the high-speed S5 protocol for efficient downloads, and how it differs from other platforms like Google Earth Engine. The discussion highlights real-world use cases, from automating Sentinel data pipelines to building large-scale datasets for AI models. Head to YouTube on the link below to view the recording of this conversation, along with an extended demo of using PhiDown.

* 🖥️ PhiDown on Github

* 📺 Video with demo on YouTube

* 👤 Roberto on LinkedIn

🚀 Timeline

* 0:38 Motivation — PhiDown created to simplify access to Copernicus data 1:55 Key Tech — Built on S5 protocol, derived from S3, ~5–10× faster

* 2:44 Comparison — Unlike Google Earth Engine, PhiDown gives direct access to raw products such as Level-0 Sentinel imagery

* 5:01 Use cases — Automating pipelines (auto-download latest Sentinel products). Accessing low-level products for algorithm testing. Building large datasets for ML / foundation models. Research applications: wildfire detection, vessel monitoring, timeliness studies with Level-0 data

* 6:55 Development context — Roberto notes the rise of LLMs and coding agents. Tools can help, but domain expertise still required.

* 8:01 Open Source — PhiDown is on GitHub. Includes documentation + example notebooks. Community-driven project — Roberto encourages contributions, feature requests, and collaboration.

Bio

Roberto is an Internal Research Fellow at ESA Φ-lab specialising in deep learning and edge computing for remote sensing. He focuses on improving time-critical decision-making through advanced AI solutions for space missions and Earth monitoring. He holds a Ph.D. at the University of Naples Federico II, where he also earned his Master's and Bachelor's degrees in Aerospace Engineering. His notable work includes the development of "FederNet," a terrain relative navigation system. Del Prete's professional experience includes roles as a Visiting Researcher at the European Space Agency's Φ-Lab and SmartSat CRC in Australia. He has contributed to key projects like Kanyini Mission, and developed AI algorithms for real-time maritime monitoring and thermal anomaly detection. He co-developed the award-winning P³ANDA project, a compact AI-powered imaging system, earning the 2024 Telespazio Technology Contest prototype prize. Co-author of more than 30 scientific publications, Del Prete is dedicated to leveraging advanced technologies to address global challenges in remote sensing and AI.

In this episode, I caught up with Jonathan Lwowski, Connor Wallace, and Isaac Corley to explore how Zeitview built an AI-powered system to monitor solar farms at continental scale. We dive into the North American Solar Scan, which surveyed every 1MW plus site using high-resolution aerial RGB and thermal-infrared imagery, then processed it through a chained ML pipeline that detects panel-level defects and fire risks. The team discusses the challenges of normalising data across regions, why a modular cascaded model design outperforms monolithic end-to-end approaches, and how human-in-the-loop review ensures high precision. They also share insights from building a generalised ML library on top of Timm, Segmentation Models PyTorch, and TorchVision to accelerate model training and deployment, their philosophy of prioritising data quality over chasing SOTA, and how the same framework extends to wind, telecom, real estate, and other renewable assets.

* 🖥️ Zeitview website

* 📺 Video of this conversation on YouTube

* 👤 Jonathan on LinkedIn

* 👤 Conor on LinkedIn

* 👤 Isaac on LinkedIn

Jonathan bio: Jonathan Lwowski is an accomplished AI leader and Director of AI/ML at Zeitview, where he guides high-performing machine learning teams to deliver scalable, real-world solutions. With deep experience spanning start-ups and enterprise environments, Jonathan bridges cutting-edge innovation with business strategy, ensuring AI efforts are aligned, impactful, and clearly communicated. He’s passionate about unlocking AI’s potential while fostering a culture of technical excellence, collaboration, and growth.

Conor bio: Conor Wallace is a Machine Learning Scientist at Zeitview, where he develops computer vision systems - including vision-language models - for geospatial AI applications in aerial inspection and infrastructure monitoring. His work integrates visual, thermal, and spatial data to build scalable systems for analysing assets such as solar farms, wind turbines, and commercial rooftops. He is also completing a Ph.D. in Electrical Engineering, where his research focuses on agent modelling in multi-agent systems, emphasising behaviour prediction in dynamic, non-stationary environments. Conor is passionate about applying state-of-the-art machine learning to real-world challenges in remote sensing and intelligent decision-making.

Isaac bio: Isaac Corley is a Senior Machine Learning Engineer at Wherobots, where he builds scalable geospatial AI systems. He holds a Ph.D. in Electrical Engineering with a focus on computer vision for remote sensing. Isaac previously worked as a Senior ML Scientist at Zeitview and a Research Intern at Microsoft's AI for Good Lab. He is a core maintainer of TorchGeo and is passionate about advancing open-source tools that make geospatial AI more accessible and production-ready.