![]() ![]() Recent advances in cloud-based remote sensing platforms have revoluted the routines for remote sensing big data (RSBD) analysis. We argue that blurring the dichotomy between global and local aligns with the DE vision to access the world's knowledge and explore information about the planet. ![]() These developments include enabling local EO data cubes based on public, global, and cloud-native EO data streaming and interoperability between local EO data cubes. ![]() A local EO data cube can benefit many stakeholders and players but requires several technical developments. We investigate local EO data cubes from five perspectives (science, business and industry, government and policy, education, communities and citizens) and illustrate four examples covering three continents at different geographic scales (Swiss Data Cube, semantic EO data cube for Austria, DE Africa, Virginia Data Cube). However, their alignment with the Digital Earth (DE) vision and the benefits and trade-offs in creating and maintaining them ought to be further examined. Several EO data cubes with a geographic focus ("local EO data cubes") have been implemented. EO data cubes are a leading technology for facilitating big EO data analysis and can be deployed on different spatial scales: local, national, regional, or global. The technological landscape for managing big Earth observation (EO) data ranges from global solutions on large cloud infrastructures with web-based access to self-hosted implementations. ![]() Future work should also explore new avenues for access to source data, metadata, provenance, and methods to adapt principles of R&R according to geographic variability and stakeholder requirements. Where ethically possible, future work should exemplify best practices for R&R research by publishing access to open data sets and workflows. R&R in geospatial UAS applications can be facilitated through augmented access to provenance information for authorized stakeholders, and the establishment of R&R as an important aspect of UAS and related research design. Key barriers include: (1) awareness of time and resource requirements, (2) accessibility of provenance, metadata, and version control, (3) conceptualization of geographic problems, and (4) geographic variability between study areas. We examine both the relevance of R&R as well as existing support for R&R in remote sensing and photogrammetry assisted UAS workflows. This review identifies key barriers to, and suggests best practices for, R&R in geospatial UAS workflows as well as broader remote sensing applications. To date, there have only been very limited efforts to overcome R&R-related issues in remote sensing workflows in general, let alone those tied to unmanned aircraft systems (UAS) as a disruptive technology. Trust in information technology (IT)-intensive workflows within geographic information science (GIScience), remote sensing, and photogrammetry depends on solutions to R&R challenges affecting multiple computationally driven disciplines. Multiple scientific disciplines face a so-called crisis of reproducibility and replicability (R&R) in which the validity of methodologies is questioned due to an inability to confirm experimental results. ![]()
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