Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | Solar PV panel, convolutional network, mapping |
Field | DocType | Citations |
Training set,Data mining,Computer science,Distributed generation,Artificial intelligence,Photovoltaic system,Image resolution,Machine learning,Scalability | Conference | 2 |
PageRank | References | Authors |
0.42 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangye Yuan | 1 | 215 | 13.96 |
| hsiuhan lexie yang | 2 | 129 | 8.75 |
| Olufemi A. Omitaomu | 3 | 321 | 17.51 |
| B. L. Bhaduri | 4 | 138 | 22.73 |