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Remote sensing datasets for land cover classification are mostly distributed in UTM projection which introduce significant geometric distortions—particularly at high latitudes—and fail to respect the spherical geometry of Earth. These distortions propagate into deep learning models trained on such data, leading to latitude-dependent biases, edge artifacts in tile-based processing, and poor generalization across geographic boundaries. While convolutional neural networks (CNNs) have achieved state-of-the-art performance on benchmark datasets like BigEarthNet, they operate on Euclidean grids and cannot naturally handle the structure of a sphere.Here we introduce a comprehensive pipeline for transforming the BigEarthNet dataset—comprising 549,488 multispectral image patches from its original UTM projection into the HEALPix (Hierarchical Equal Area isoLatitude Pixelization) representation. HEALPix, originally developed for cosmic microwave background analysis, offers equal-area partitioning of the sphere, ensuring uniform statistical treatment of pixels regardless of latitude, and provides a natural hierarchical structure for multi-resolution analysis.We implement and evaluate spherical CNNs architectures designed for data on spherical manifolds—against traditional planar CNN baselines (Unet/Resnet50) trained on the HEALPix-transformed data, benchmarking classification performance for multi-label land cover prediction using the 19-class BigEarthNet nomenclature with metrics suited to imbalanced settings (F1-macro/micro, precision, recall, average precision).This work represents the first large-scale application of HEALPix projection to Remote Sensing classification and validates the effectiveness of spherical deep learning for real-world remote sensing beyond traditional climate science domains. Our experimental design employs matched training protocols and comparable model capacities, demonstrating that spherical representations eliminate projection-induced artifacts, enable seamless cross-boundary analysis, and provide rotation equivariance that reduces the need for extensive spatial data augmentation—key advantages for global-scale Earth observation applications.