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Density estimation is a crucial component of many machine learning methods, and manifold learning in particular, where geometry is to be constructed from data alone. A significant practical limitation of the current density estimation literature is that methods have not been developed for manifolds with boundary, except in simple cases of linear manifolds where the location of the boundary is assumed to be known. This limitation is overcome by developing a density estimation method for manifolds with boundary that does not require any prior knowledge of the location of the boundary. To construct an appropriate estimator, statistics are introduced that provably approximate the distance and direction of the boundary, which allows us to apply a cut-and-normalize boundary correction. Then, multiple cut-and-normalize estimators are used to build a consistent kernel density estimator that has uniform bias, at interior and …