TY - GEN
T1 - Predicting PV Areas in Aerial Images with Deep Learning
AU - Zech, Matthias
AU - Ranalli, Joseph
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6/14
Y1 - 2020/6/14
N2 - Data on the location of distributed photovoltaic installations are valuable to a variety of research activities. We have trained and applied a Fully Convolutional Neural Network to identify PV sites from aerial images of Oldenburg, Germany acquired from Google Maps. The architecture used was U-net, which was trained on a set of manually labelled images, and verified against a test dataset. The model is able to accurately estimate location and shape of PV plants in the north European town of Oldenburg. In addition, the model is able to estimate its own uncertainty, breaking the black box assumption of Deep Learning.
AB - Data on the location of distributed photovoltaic installations are valuable to a variety of research activities. We have trained and applied a Fully Convolutional Neural Network to identify PV sites from aerial images of Oldenburg, Germany acquired from Google Maps. The architecture used was U-net, which was trained on a set of manually labelled images, and verified against a test dataset. The model is able to accurately estimate location and shape of PV plants in the north European town of Oldenburg. In addition, the model is able to estimate its own uncertainty, breaking the black box assumption of Deep Learning.
UR - http://www.scopus.com/inward/record.url?scp=85099538070&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099538070&partnerID=8YFLogxK
U2 - 10.1109/PVSC45281.2020.9300636
DO - 10.1109/PVSC45281.2020.9300636
M3 - Conference contribution
AN - SCOPUS:85099538070
T3 - Conference Record of the IEEE Photovoltaic Specialists Conference
SP - 767
EP - 774
BT - 2020 47th IEEE Photovoltaic Specialists Conference, PVSC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 47th IEEE Photovoltaic Specialists Conference, PVSC 2020
Y2 - 15 June 2020 through 21 August 2020
ER -