Using Daytime Satellite Images and Deep Learning – Lecture by Łukasz Janisiów
On behalf of the QFRG and DSLab research centres, we cordially invite you to attend the seminar „Predicting Socio-Economic Development in Poland in 2020–2024 Using Satellite Imagery”, which will take place on 24 November at 17:00. Łukasz Janisiów will discuss how to use daytime satellite images, deep learning and transfer learning to extract features of areas from images and predict economic well-being on a local level country-wide. (The full abstract of the presentation is provided below.)
There is also an option to join the seminar online. Those interested in remote participation are kindly asked to contact us at Lq5uFr'HxlCK7O9{NXh-JA]#[9d!c2Vm$5d.-DE1*09^67* — we will be pleased to provide the link to the event. Please note that joining the meeting remotely implies consent to being recorded. Please turn off cameras and microphones during the presentation and send the questions to the speaker in the chat.
Please arrive (or log in) by 16:50. After the lecture, we warmly encourage you to take part in the discussion.
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Access to up-to-date statistical data is crucial for understanding and responding to socio-economic trends. However, in many parts of the world, official statistics are often delayed, and in some regions, reliable data may be scarce or entirely unavailable. This makes it difficult for researchers, policymakers, and organizations to make informed and timely decisions. Traditionally, nighttime lights satellite imagery has been used as a proxy for economic activity, but this approach lacks precision. Recent advances in high-resolution satellite imagery, combined with increased computational power, offer new opportunities for near real-time socio-economic monitoring. Compared to traditional statistical data, high-resolution imagery provides two key advantages: it is available almost in real time, and predictions can be made for any area, even if it does not align with official administrative boundaries. In this interdisciplinary study, we bridge economics, machine learning, and remote sensing to predict socio-economic development in Poland between 2020 and 2024. We use two approaches for feature extraction from high-resolution satellite imagery, introduce a novel dataset designed to suport transfer learning in computer vision, and develop an end-to-end pipeline for predicting regional socio-economic indicators. Our work demonstrates the potential of satellite-based methods to complement, and in some cases substitute, traditional data sources, enabling more timely and granular insights into socio-economic dynamics.