Enabling Spectral Indices Computation with SpectralIndices.jl | Martinuzzi | JuliaCon 2024 скачать в хорошем качестве

Enabling Spectral Indices Computation with SpectralIndices.jl | Martinuzzi | JuliaCon 2024

Enabling Spectral Indices Computation with SpectralIndices.jl by Francesco Martinuzzi PreTalx: https://pretalx.com/juliacon2024/talk... Slides: https://pretalx.com/media/juliacon202... In this talk, we will showcase SpectralIndices.jl (https://github.com/awesome-spectral-i..., a Julia package developed to simplify the access and computation of spectral indices in remote sensing data analysis. The presentation will offer a comprehensive overview of the package's capabilities, emphasizing its application in Earth system research. Key topics to be covered include: 1. Spectral Indices: Basic introduction. 2. Package Overview: Introduction to SpectralIndices.jl, its design philosophy, and the specific challenges it addresses in remote sensing data analysis. 3. Accessing Spectral Indices: Demonstrating how the package provides streamlined access to a wide range of spectral indices, including both well-established and newly developed indices. 4. Data Compatibility and Processing: Exploring the package's compatibility with various remote sensing data types and formats. We will demonstrate how SpectralIndices.jl can be applied to different datasets. 5. User Interface and Usability: Showcasing the user-friendly interface of SpectralIndices.jl. The presentation will include live coding examples to illustrate the ease of computing spectral indices with minimal coding effort, making the tool accessible to users with and without remote sensing expertise. 6. Future Directions: Outlining the ongoing development and future plans for SpectralIndices.jl. We will discuss potential enhancements, community collaboration opportunities, and the envisioned roadmap for the package. By the end of this talk, attendees will have a deep understanding of SpectralIndices.jl's technical features, its application in remote sensing research, and how it contributes to advancing Earth system science through efficient data analysis and community-driven development. This work is done in collaboration with Miguel D. Mahecha, David Montero and Karin Mora.