Polarization in ferroelectric domains arises from atomic-scale structural variations that govern macroscopic functionalities. The interfaces between these domains–known as domain walls–host distinct physical responses, making their identification and control critical. Four-dimensional scanning transmission electron microscopy (4D-STEM) enables simultaneous acquisition of real- and reciprocal-space information at the atomic scale, offering a powerful platform for domain mapping. However, conventional analyses rely on computationally intensive processing and manual interpretation, which are time-consuming and prone to misalignment and diffraction artifacts. Here, we present a convolutional neural network that, with minimal training, classifies polarization directions from diffraction data and segments domains in real space. We further introduce an adaptive sampling strategy that prioritizes images from domain wall regions, reducing the number of training images required while improving accuracy and interpretability. We demonstrate this approach for domain mapping in ferroelectric boracite, Cu3B7O13Cl.