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

Joseph Ranalli, Matthias Zech

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2023 IEEE 50th Photovoltaic Specialists Conference, PVSC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665460590
DOIs
StatePublished - 2023
Event50th IEEE Photovoltaic Specialists Conference, PVSC 2023 - San Juan, United States
Duration: Jun 11 2023Jun 16 2023

Publication series

NameConference Record of the IEEE Photovoltaic Specialists Conference
ISSN (Print)0160-8371

Conference

Conference50th IEEE Photovoltaic Specialists Conference, PVSC 2023
Country/TerritoryUnited States
CitySan Juan
Period6/11/236/16/23

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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