论文标题
Solardk：高分辨率的城市太阳能电池板图像分类和本地化数据集
SolarDK: A high-resolution urban solar panel image classification and localization dataset
论文作者
论文摘要
从空中图像中进行太阳能电池板阵列分类的研究群正在增加，但仍然没有很多公共基准数据集。本文介绍了两个新颖的基准数据集，用于在丹麦进行分类和本地化太阳能电池板阵列：一个人类注释的数据集，用于分类和细分，以及使用丹麦国家建筑注册表的自我报告的数据获得的分类数据集。我们探讨了新基准数据集上先前作品的性能，并在使用与最近的工作相似的方法进行微调模型后呈现结果。此外，我们在几种情况下为我们的数据集提供了较新的架构模型，并为我们的数据集提供基准基准。我们认为，这些数据集的发布可能会改善本地和全球地理空间域的未来研究，以识别和映射太阳能电池板阵列的空中图像。可以在https://osf.io/aj539/上访问数据。
The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data from the Danish national building registry. We explore the performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach as recent works. Furthermore, we train models of newer architectures and provide benchmark baselines to our datasets in several scenarios. We believe the release of these datasets may improve future research in both local and global geospatial domains for identifying and mapping of solar panel arrays from aerial imagery. The data is accessible at https://osf.io/aj539/.
