Due to the technical challenges of large-scale microscopy and analysis, to date only limited knowledge has been made available about axon morphometry (diameter, shape, myelin thickness, volume fraction), thereby limiting our understanding of neuronal microstructure and slowing down research on neurodegenerative pathologies. This study addresses this knowledge gap by establishing a state-of-the-art acquisition and analysis framework for mapping axon morphometry, and providing the first comprehensive mapping of axon morphometry in the human spinal cord. We dissected, fixed and stained a human spinal cord with osmium tetroxide, and used a scanning electron microscope to image the entirety of 23 axial slices, covering C1 to L5 spinal levels. An automatic method based on deep learning was then used to segment each axon and myelin sheath to produce maps of axon morphometry. These maps were then registered to a standard spinal cord magnetic resonance imaging (MRI) template. Between 500,000 (lumbar) and 1 million (cervical) myelinated axons were segmented at each level of this human spinal cord. Morphometric features show a large disparity between tracts, but high right-left symmetry. Our results suggest a modality-based organization of the dorsal column in the human, as it has been observed in the rat. The generated axon morphometry template is publicly available at https://osf.io/8k7jr/ and could be used as a reference for quantitative MRI studies. The proposed framework for axon morphometry mapping could be extended to other parts of the central or peripheral nervous system that exhibit coherently-oriented axons.