We believe that the future of Earth Observation (EO) is in fusion, harmonization, and interoperability of satellite imagery. Intensified monitoring leads to better understanding of land use and reduction of maintenance costs for all Land Cover products. RapidAI4EO is an initiative that aims to establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products. The goal is to provide intensified monitoring of Land Use (LU), Land Cover (LC) changes at a much higher spatial resolution and temporal cadence than is possible today. Key objectives are to explore, evaluate, and quantify state of the art deep learning algorithms and methodologies that leverage three meter, daily time series, in conjunction with higher spectral resolution Sentinel-2 imagery. Consortium Partners: The RapidAI4EO projects brings together Planet Labs PBC, the operator of the world's largest fleet of Earth-imaging satellites and the recognized leader of the CubeSat revolution, VITO, the main production center of the Copernicus Global Land Service, Vision Impulse, a recent spin-off of German Research Center for Artificial Intelligence (DFKI, the largest research center for Artificial Intelligence in the world and one of the two European NVIDIA AI Labs), the International Institute for Applied Systems Analysis (IIASA) whose Center for Earth Observation and Citizen Science (EOCS) devises new approaches and technologies to collect data on land cover and land use, and Serco Italia, a worldwide service provider to governments, international agencies and industries, and operator of the ONDA DIAS platform. The objectives of RapidAI4EO are: 1) the creation and release of the most comprehensive spatiotemporal EO training sets ever produced for machine learning; 2) the development and implementation of novel AI solutions for continuous change detection that leverage these data sets; 3)the ability to drive frequent temporal updates of the Corine Land Cover (CLC) product; and 4)to demonstrate improved LULC mapping using harmonized Sentinel-2 and very high resolution, high cadence data streams.
To bridge the gap between community-engaged learning and research-practice partnerships, we describe our experiences in a project jointly conceptualized and implemented by undergraduate students and youth development practitioners over the course of two academic semesters. The project offered students the opportunity to apply the skills they learned through coursework in a way that also supported the needs of community practitioners, providing both groups with opportunities to learn from each other. In this paper we describe the collaborative project, our process, the challenges we faced, and the impact of the project on the student researchers and the youth development practitioners. Written by representatives of both the student researchers and the practitioner collaborators, we hope this paper will inspire others to incorporate students in research-practice partnerships and that our reflections on the strengths and challenges of this process will facilitate more effective implementation of community-engaged scholarship in the future.
Historians of economic thought often criticize David Ricardo on the grounds that he lacked factual knowledge of Britain's economy, and that he recommended irresponsible policies in reliance on the axioms of Say's Law and the Quantity Theory of Money. This article establishes that Ricardo was well informed about business conditions in Britain. His livelihood depended on being able to predict, months in advance, the state of financial markets. This, in turn, meant predicting changes in the money supply, the foreign exchange rate, Government expenditures, and general economic activity. The article also illustrates how Ricardo used this same information when assessing Britain's economic state and when making recommendations about the choice of a monetary standard.Ricardo, Empirical Economist, Knowledge,
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