scikit-image has been used in industry, education, and academic research, in fields as disparate as biology and life sciences, materials science, remote sensing/satellite imaging, astrophysics, archaeology, and more. With a common API for 2D, 3D and higher-dimensional imaging, it is usable for a wide variety of imaging data. scikit-image is also being used for pre- and post-processing and analysis with deep learning frameworks such as PyTorch, TensorFlow, and Chainer. With its focus on a clear API for reference algorithms, scikit-image is also used in educational projects such as the SciPy Lecture Notes, the Python Data Science Handbook, and FastAI.