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A deep learning approach for rooftop geocoding

机译：屋顶地理编码的深度学习方法

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摘要

Geocoding has become a routine task for many research investigations to conduct spatial analysis. However, the output quality of geocoding systems is found to impact the conclusions of subsequent studies that employ this workflow. The published development of geocoding systems has been limited to the same set of interpolation methods and reference data sets for quite some time. We introduce a novel geocoding approach utilizing object detection on remotely sensed imagery based on a deep learning framework to generate rooftop geocoding output. This allows geocoding systems to use and output exact building locations without employing typical geocoding interpolation methods or being completely limited by the availability of reference data sets. The utility of the proposed approach is demonstrated over a sample of 22,481 addresses resulting in significant spatial error reduction and match rates comparable to typical geocoding methods. For different land-use types, our approach performs better on low-density residential and commercial addresses than on high-density residential addresses. With appropriate model setup and training, the proposed approach can be extended to search different object locations and to generate new address and point-of-interest reference data sets.
机译：地理编码已成为许多研究调查进行空间分析的常规任务。然而，发现地理编码系统的输出质量会影响采用此工作流程的后续研究的结论。发布地理编码系统的开发仅限于相同的插值方法和相当长的数据集。我们在基于深度学习框架的远程感测图像上利用对象检测来介绍一种新的地理编码方法，以产生屋顶地理编码输出。这允许地理编码系统使用和输出精确的构建位置而不采用典型的地理编码插值方法或者完全受到参考数据集的可用性的限制。在22,481个地址的样本上证明了所提出的方法的效用，导致显着的空间误差减少和与典型地理编码方法相当的匹配率。对于不同的土地使用类型，我们的方法在低密度住宅和商业地址上表现出比高密度住宅地址更好。通过适当的模型设置和培训，可以扩展所提出的方法以搜索不同的对象位置，并生成新地址和兴趣点参考数据集。

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