TY - GEN
T1 - Large-scale solar panel mapping from aerial images using deep convolutional networks
AU - Yuan, Jiangye
AU - Yang, Hsiu Han Lexie
AU - Omitaomu, Olufemi A.
AU - Bhaduri, Budhendra L.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - Up-to-date maps of installed solar photovoltaic panels are a critical input for policy and financial assessment of solar distributed generation. However, such maps for large areas are not available. With high coverage and low cost, aerial images enable large-scale mapping, but it is highly difficult to automatically identify solar panels from images, which are small objects with varying appearances dispersed in complex scenes. We introduce a new approach based on deep convolutional networks, which effectively learns to delineate solar panels in aerial scenes. The approach is applied to mapping solar panels in imagery covering 200 square kilometers in two cities, using only 12 square kilometers of training data that are manually labeled. Results are generated efficiently with an accuracy comparable to manual mapping, demonstrating the effectiveness and scalability of our approach.
AB - Up-to-date maps of installed solar photovoltaic panels are a critical input for policy and financial assessment of solar distributed generation. However, such maps for large areas are not available. With high coverage and low cost, aerial images enable large-scale mapping, but it is highly difficult to automatically identify solar panels from images, which are small objects with varying appearances dispersed in complex scenes. We introduce a new approach based on deep convolutional networks, which effectively learns to delineate solar panels in aerial scenes. The approach is applied to mapping solar panels in imagery covering 200 square kilometers in two cities, using only 12 square kilometers of training data that are manually labeled. Results are generated efficiently with an accuracy comparable to manual mapping, demonstrating the effectiveness and scalability of our approach.
KW - Solar PV panel
KW - convolutional network
KW - mapping
UR - https://www.scopus.com/pages/publications/85015173704
U2 - 10.1109/BigData.2016.7840915
DO - 10.1109/BigData.2016.7840915
M3 - Conference contribution
AN - SCOPUS:85015173704
T3 - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
SP - 2703
EP - 2708
BT - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
A2 - Joshi, James
A2 - Karypis, George
A2 - Liu, Ling
A2 - Hu, Xiaohua Tony
A2 - Ak, Ronay
A2 - Xia, Yinglong
A2 - Xu, Weijia
A2 - Sato, Aki-Hiro
A2 - Rachuri, Sudarsan
A2 - Ungar, Lyle
A2 - Yu, Philip S.
A2 - Govindaraju, Rama
A2 - Suzumura, Toyotaro
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Big Data, Big Data 2016
Y2 - 5 December 2016 through 8 December 2016
ER -