As new hardware, software and data acquisition RS technologies continue to emerge , there is a pressing need for an integrated RS data analysis environment, which facilitates the development of pipelines, methods and applications, and bolsters the use of RS in biomedical research. Yet, the full deployment of RS and its capabilities is still hindered by practical factors stemming from the restrictive, functionally disparate, and highly encapsulated nature of current commercial software for RS data analysis. RS data analysis often operates within proprietary software environments and data formats, which have induced methodological inconsistencies and reduced cross-platform and benchmarking efforts, with growing concerns around reproducibility. These restrictions have also hampered the adoption of new AI technologies into the field . As a consequence, researchers increasingly resort to developing inhouse scripts for RS analysis in Python , further adding to methodological fragmentation and lack of standardisation .