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Remote sensing data refers to the information collected by sensors that observe objects, surfaces, or environmental phenomena without direct physical contact, together with the digital or recorded datasets produced from those observations ^[1][2]. The data may be acquired from different observation platforms, including satellites, aircraft, unmanned aerial systems, and ground-based sensors. Depending on the sensing system, remote sensing data may record reflected, emitted, or otherwise measured energy across different portions of the electromagnetic spectrum and may be represented in forms such as digital imagery, multispectral or hyperspectral measurements, thermal observations, microwave data, and other sensor-derived measurements ^[3]. Remote sensing datasets can vary substantially in spatial, spectral, radiometric, and temporal characteristics according to the sensor, platform, observation configuration, and intended application ^[4]. Consequently, remote sensing data are not necessarily limited to raw observations: datasets may undergo preprocessing, correction, classification, transformation, and other analytical procedures before being used to derive geographic or environmental information ^[2][3]. At large scales, remote sensing data are characterized by their multi-source, multi-scale, high-dimensional, heterogeneous, and dynamic properties, creating requirements for specialized methods of storage, processing, integration, and analysis ^[4]. Contemporary remote sensing workflows increasingly organize these datasets into processing pipelines extending from sensor observations and data acquisition through preprocessing and information extraction to application-specific products and analyses ^[5].