RID—Roof Information Dataset for Computer Vision-Based Photovoltaic Potential Assessment

Computer vision has great potential to accelerate the global scale of photovoltaic potential analysis by extracting detailed roof information from high-resolution aerial images, but the lack of existing deep learning datasets is a major barrier. Therefore, we present the Roof Information Dataset for...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-05, Vol.14 (10), p.2299, Article 2299
Main Authors: Krapf, Sebastian, Bogenrieder, Lukas, Netzler, Fabian, Balke, Georg, Lienkamp, Markus
Format: Article
Language:English
Subjects:
Accuracy
Annotations
Arrays
Artificial intelligence
Artificial neural networks
Computer vision
dataset
Datasets
Deep learning
Image annotation
Image resolution
Image segmentation
Information retrieval
Intersections
Machine learning
Neural networks
Photovoltaic cells
Photovoltaics
Quality assessment
Remote sensing
Renewable resources
roof information
roof segments
roof superstructures
Roofs
Semantic segmentation
Semantics
Superstructures
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Summary:Computer vision has great potential to accelerate the global scale of photovoltaic potential analysis by extracting detailed roof information from high-resolution aerial images, but the lack of existing deep learning datasets is a major barrier. Therefore, we present the Roof Information Dataset for semantic segmentation of roof segments and roof superstructures. We assessed the label quality of initial roof superstructure annotations by conducting an annotation experiment and identified annotator agreements of 0.15–0.70 mean intersection over union, depending on the class. We discuss associated the implications on the training and evaluation of two convolutional neural networks and found that the quality of the prediction behaved similarly to the annotator agreement for most classes. The class photovoltaic module was predicted to be best with a class-specific mean intersection over union of 0.69. By providing the datasets in initial and reviewed versions, we promote a data-centric approach for the semantic segmentation of roof information. Finally, we conducted a photovoltaic potential analysis case study and demonstrated the high impact of roof superstructures as well as the viability of the computer vision approach to increase accuracy. While this paper’s primary use case was roof information extraction for photovoltaic potential analysis, its implications can be transferred to other computer vision applications in remote sensing and beyond.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14102299