Thus, in this contribution, we propose an automated materials characterization pipeline that is fully based on computer vision. We demonstrate this automated vision-based characterization loop using (i) a computer vision tool for scalable segmentation to arbitrarily many material samples, (ii) a tool to extract the chemical composition of all material samples, (iii) an algorithm capable of automatically computing band gap across arbitrarily many unique samples using vision-segmented hyperspectral reflectance data, and (iv) automating the stability measurement of multi-hour perovskite degradation experiments with vision for spatially non-uniform samples. The accuracy of each developed method is benchmarked against human evaluation and Xray diffraction.