BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-europe-2024-academic-tra ck//talk//YXRD3D BEGIN:VTIMEZONE TZID:EET BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1;UNTIL=20001231T220000Z TZNAME:EET TZOFFSETFROM:+0200 TZOFFSETTO:+0200 END:STANDARD BEGIN:STANDARD DTSTART:20021027T050000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:EET TZOFFSETFROM:+0300 TZOFFSETTO:+0200 END:STANDARD BEGIN:DAYLIGHT DTSTART:20020331T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:EEST TZOFFSETFROM:+0200 TZOFFSETTO:+0300 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-europe-2024-academic-track-YXRD3D@talks.staging.osgeo.or g DTSTART;TZID=EET:20240703T143000 DTEND;TZID=EET:20240703T150000 DESCRIPTION:Remote sensing has evolved into a fundamental tool in environme ntal science\, helping scientists monitor environmental changes\, assess v egetation health\, and manage natural resources. As Earth observation (EO) data products have become increasingly available\, a large number of spec tral indices have been developed to highlight specific surface features an d phenomena observed across diverse application domains\, including vegeta tion\, water\, urban areas\, and snow cover. Examples of such indices incl ude the normalized difference vegetation index (NDVI) (Rouse et al.\, 1974 )\, used to assess vegetation states\, and the normalized difference water index (NDWI) (McFeeters\, 1996)\, used to delineate and monitor water bod ies. The constantly increasing number of spectral indices\, driven by fact ors such as the enhancement of existing indices\, parameters optimization\ , and the introduction of new satellite missions with novel spectral bands \, has necessitated the development of comprehensive catalogs. One such ef fort is the Awesome Spectral Indices (ASI) suite (Montero et al.\, 2023)\, which provides a curated machine-readable catalog of spectral indices for multiple application domains. Additionally\, the ASI suite includes not o nly a Python library for querying and computing these indices but also an interface for the Google Earth Engine JavaScript application programming i nterface\, thereby accommodating a wide range of users and applications.\n \nDespite these valuable resources\, there is an emerging necessity for a dedicated library tailored to Julia\, a programming language renowned for its high-performance computing capabilities (Bezanson et al.\, 2017). Juli a has not only established itself as an effective tool for numerical and c omputational tasks but also offers the possibility to utilize Python withi n its environment through interoperability features. This interoperation a dds a layer of flexibility\, allowing users to access Python's extensive l ibraries and frameworks directly from Julia. However\, while multiple pack ages are available in Julia to manipulate high dimensional EO data\, most of them provide different interfaces. Furthermore\, leveraging Python's Py Call for interfacing with Zarr files and other high-dimensional data forma ts is not practical. Specifically\, the inefficiency in cross-language dat a exchange and the overhead from cross-language calls significantly hinder performance\, underlining the need for native Julia solutions optimized f or such data tasks.\n\nRecognizing the need for a streamlined approach to use spectral indices\, we introduce SpectralIndices.jl\, a Julia package d eveloped to simplify the computation of spectral indices in remote sensing applications. SpectralIndices.jl provides a user-friendly\, efficient sol ution for both beginners and researchers in the field of remote sensing. S pectralIndices.jl offers several features supporting remote sensing tasks: \n - Easy Access to Spectral Indices: The package provides instant access to a comprehensive range of spectral indices from the ASI catalog\, removi ng the need for manual searches or custom implementations. Users can effor tlessly select and compute indices suitable for their specific research ne eds.\n - High-Performance Computing: Built on Julia's strengths in numeric al computation\, SpectralIndices.jl provides rapid processing even for lar ge datasets (Bouchet-Valat et al.\, 2023). Consequently\, this makes it a time-efficient tool for handling extensive remote sensing data.\n - Versat ile Data Compatibility: SpectralIndices.jl supports a growing list of inpu t data types. Furthermore\, the addition of data types to the library does not slow down compilation through the built-in package extensions of Juli a that allow conditional compilation of dependencies.\n - User-Friendly In terface: Designed with simplicity in mind\, the package enables users to c ompute spectral indices with just a few lines of code. This ease of use lo wers the barrier to entry for those new to programming or remote sensing.\ n - Customization and Community Contribution: Users can extend the package 's capabilities by adding new indices or modifying existing ones. This ope nness aligns with the FAIR principles\, ensuring that data is findable\, a ccessible\, interoperable and reusable.\n\nBy providing a straightforward and efficient means to compute spectral indices\, the package helps users to streamline and accelerate software pipelines in Earth system research. Furthermore\, it provides a consistent and unified interface to compute in dices\, improving the reliability and accuracy of research outcomes. Wheth er tracking deforestation\, studying crop health\, or assessing water qual ity\, SpectralIndices.jl equips users with the tools needed for accurate\, timely analysis.\n\nThe introduction of SpectralIndices.jl reflects a bro ader trend in scientific computing towards adopting high-performance langu ages like Julia\, highlighting the importance of efficient data analysis t ools in addressing complex environmental challenges. This development cont ributes to the democratization of data analysis\, making advanced tools mo re accessible to a diverse range of users.\n\nThe SpectralIndices.jl packa ge is open-source and hosted on GitHub (https://github.com/awesome-spectra l-indices/SpectralIndices.jl)\, available for public access and contributi on. It is licensed under the MIT license\, which permits free use\, modifi cation\, and distribution of the software. This approach encourages commun ity contributions and fosters an environment of shared learning and improv ement\, ensuring that SpectralIndices.jl remains a cutting-edge tool for e nvironmental analysis and research. Additionally\, the code is commented a nd documented\, facilitating both contribution and adoption. The code in t he examples is run during the compilation of the online documentation\, as suring its reproducibility. Finally\, the software is tested using continu ous integration through GIthub Actions\, ensuring its correct execution in different use cases and environments. DTSTAMP:20260603T222741Z LOCATION:Omicum SUMMARY:SpectralIndices.jl: Streamlining spectral indices access and comput ation for Earth system research - Francesco Martinuzzi URL:https://talks.staging.osgeo.org/foss4g-europe-2024-academic-track/talk/ YXRD3D/ END:VEVENT END:VCALENDAR