  Classifying all cells in an organ is a relevant and difficult problem from
plant developmental biology. We here abstract the problem into a new benchmark
for node classification in a geo-referenced graph. Solving it requires learning
the spatial layout of the organ including symmetries. To allow the convenient
testing of new geometrical learning methods, the benchmark of Arabidopsis
thaliana ovules is made available as a PyTorch data loader, along with a large
number of precomputed features. Finally, we benchmark eight recent graph neural
network architectures, finding that DeeperGCN currently works best on this
problem.
