RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning
Giovanni Marchisio (1), Patrick Helber (3), Benjamin Bischke (3),, Timothy Davis (2), Caglar Senaras (2), Daniele Zanaga (4), Ruben Van De, Kerchove (4), Annett Wania (2) ((1) Planet Labs Inc., USA, (2) Planet Labs, GmbH, Germany, (3) Vision Impulse GmbH, DFKI, Germany

TL;DR
RapidAI4EO develops a comprehensive, high-resolution spatiotemporal satellite dataset to enhance land monitoring, phenology analysis, and change detection for the Copernicus ecosystem.
Contribution
It introduces the densest open satellite training datasets combining open data with Planet imagery for improved land use and cover monitoring.
Findings
Creation of high-resolution daily time series datasets
Fusion of open satellite data with Planet imagery at 500,000 locations
Open sourcing datasets for the remote sensing community
Abstract
Under the sponsorship of the European Union Horizon 2020 program, RapidAI4EO will establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products. The project aims to provide intensified monitoring of Land Use (LU), Land Cover (LC), and LU change at a much higher level of detail and temporal cadence than it is possible today. Focus is on disentangling phenology from structural change and in providing critical training data to drive advancement in the Copernicus community and ecosystem well beyond the lifetime of this project. To this end we are creating the densest spatiotemporal training sets ever by fusing open satellite data with Planet imagery at as many as 500,000 patch locations over Europe and delivering high resolution daily time series at all locations. We plan to open source these datasets for the benefit of the entire remote sensing…
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Taxonomy
Methodstravel james
