"Recent advances in sensor technology have revolutionized the assessment of crop health by providing fine spatial and high temporal resolutions at affordable costs. As plant scientists gain access to increasingly larger volumes of Unmanned Aerial Systems (UAS) and satellite High Throughput Phenotyping (HTP) data, there is a growing need to extract biologically informative and quantitative phenotypic information from the vast amount of freely available geospatial data. However, the lack of specialized software packages tailored for processing such data makes it challenging to develop transdisciplinary research collaboration around these data. This workshop aims to bridge the gap between big data and agricultural research scientists by providing training on an open-source online platform for managing big UAS HTP data known as Data to Science. Additionally, attendees will be introduced to powerful Python packages, namely leafmap and Leafmap, designed for the seamless integration and analysis of UAS and satellite images in various agricultural applications. By participating in this workshop, attendees will acquire the skills necessary to efficiently search, visualize, and analyze geospatial data within a Jupyter environment, even with minimal coding experience. The workshop provides a hands-on learning experience through practical examples and interactive exercises, enabling participants to enhance their proficiency and gain valuable insights into leveraging geospatial data for agricultural research purposes.\n",
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