The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte (8 x 10(15) bits) scale are now available in commercial clouds (e.g., Google Earth Engine and Amazon NASA NEX), and new commercial satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multi resolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. We apply remote sensing science and machine learning algorithms to detect and classify agricultural crops and then estimate crop yields.
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