Satellite image deep learning

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 LinkedInBioRoberto 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. 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 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 YouTubeBio: 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. 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, 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 YouTubeBio: 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. 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, 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.BioRoberto 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. 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, 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 LinkedInJonathan 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. 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 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 YouTubeBio: 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. 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 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 YouTubeBio: 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 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, 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 YouTubeBio: 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. 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 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 YouTubeBio: 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 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, 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 LinkedInBio: 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. 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