使用机器学习解决世界问题的整体框架

Ilke Demir
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引用次数: 0

摘要

由于缺乏适当的解决办法,数百万人无法获得基本服务。我们提出了一种自动生成算法来从卫星图像中创建街道地址。我们的寻址方案与街道拓扑结构一致,线性和分层遵循人类感知,并作为统一的地理编码系统使用。我们的算法首先使用深度学习提取路段,并将路网划分为区域。然后使用邻近计算来命名区域、街道和地址单元。我们还扩展了我们的寻址方案,以覆盖无法到达的地区,灵活应对变化,并成为统一地理数据库的先驱。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Holistic Framework for Addressing the World Using Machine Learning
Millions of people are disconnected from basic services due to lack of adequate addressing. We propose an automatic generative algorithm to create street addresses from satellite imagery. Our addressing scheme is coherent with the street topology, linear and hierarchical to follow human perception, and universal to be used as a unified geocoding system. Our algorithm starts with extracting road segments using deep learning and partitions the road network into regions. Then regions, streets, and address cells are named using proximity computations. We also extend our addressing scheme to cover inaccessible areas, to be flexible for changes, and to lead as a pioneer for a unified geodatabase.
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