Hands-on TESSERA: Time-series embeddings for geospatial analysis скачать в хорошем качестве

Hands-on TESSERA: Time-series embeddings for geospatial analysis

Satellite remote sensing enables a wide range of downstream applications, including habitat mapping, carbon accounting, and strategies for conservation and sustainable land use. However, satellite time series are voluminous and often corrupted, making them challenging to use: the scientific community’s ability to extract actionable insights is often constrained by the scarcity of labelled training datasets and the computational burden of processing temporal data. The presentation will introduce TESSERA (Time-series Embeddings of Surface Spectra for Earth Representation and Analysis), an open foundation model that preserves spectral-temporal signals in 128-dimensional latent representations at 10-meter resolution globally. The model uses self-supervised learning to summarise petabytes of Earth observation data. TESSERA is shown to be label-efficient and closely matches or outperforms state-of-the-art alternatives. By preserving temporal phenological signals that are typically lost in conventional approaches, TESSERA enables new insights into ecosystem dynamics, agricultural food systems, and environmental change detection. Moreover, the open-source implementation supports reproducibility and extensibility, while the privacy-preserving design allows researchers to maintain data sovereignty. To current knowledge, TESSERA is unprecedented in its ease of use, scale, and accuracy: no other foundation model provides analysis-ready outputs, is open, and delivers global, annual coverage at 10m resolution using only spectral-temporal features at pixel level. Speakers: Srinivasan Keshav Robert Sansom Professor of Computer Science in the Department of Computer Science and Technology, University of Cambridge Zhengpeng Feng PhD Candidate, Department of Computer Science and Technology, University of Cambridge Moderators: Maria Antonia Brovelli Professor, Politecnico di Milano Andrea Manara System Analyst/Geospatial Focal Point, International Telecommunication Union (ITU) AI for Good is identifying innovative AI applications, building skills and standards, and advancing partnerships to solve global challenges. AI for Good is organized by ITU in partnership with over 50 UN partners and co-convened with the Government of Switzerland. Join the Neural Network! 👉https://aiforgood.itu.int/neural-netw... The AI for Good networking community platform powered by AI. Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to solve global challenges using AI. 🔴 Watch the latest #AIforGood videos!    / aiforgood   📩 Stay updated and join our weekly AI for Good newsletter: http://eepurl.com/gI2kJ5 🗞Check out the latest AI for Good news: https://aiforgood.itu.int/newsroom/ 📱Explore the AI for Good blog: https://aiforgood.itu.int/ai-for-good... 🌎 Connect on our social media: Website: https://aiforgood.itu.int/ X:   / aiforgood   LinkedIn Page:   / 26511907   LinkedIn Group:   / 8567748   Instagram:   / aiforgood   Facebook:   / aiforgood   Disclaimer: The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.