Generalizability of Neural Network-based Identification of PV in Aerial Images

Abstract

Identification of PV panels from aerial imagery is a potential strategy for building comprehensive behind-the-meter PV datasets. Several previous studies have utilized Convolutional Neural Networks with the goal of producing tools that can perform these identification tasks. Neural Network approaches rely on labelled data for training, with several aerial imagery datasets with labelled PV already available. This study aims to investigate generalizability of models trained on one set of labelled PV data to other datasets, to further understanding of how these models can be applied. Six different PV datasets were utilized, and test data results were compared. Overall, we find that generalizability suffers when models are presented with different data than they were trained on. We describe some dataset features that led to particularly poor generalization. This study highlights the need for further research to investigate strategies for improving generalizability of trained Neural Network models.