Supporting artificial intelligence solution to parking problem

Author:琳达·达菲（Linda Duffy）

有有限数量的停车位和增加easing number of cars, frustration and inconvenience related to parking is growing. Recognising this as a problem, Manuela Rasthofer, CEO ofTerraLoupe GmbH启动了一个项目，以结合人工智能和矫正空中图像，以创建整个德国可用的停车场和空间的准确清单。

Mapping available parking

一辆汽车可以是一个充满压力和time-consumin停车g activity, and in the future, autonomously navigated vehicles will be searching for spaces without a driver to help. The need for high-definition digital maps that accurately measure and identify all types of objects, including parking spaces, is quickly becoming a reality.

TerraLoupe GmbH is a technology start-up based in Munich, Germany, that focuses on combining geodata and computer analytics in innovative ways. Starting with high-resolution orthoimagery, TerraLoupe applies machine learning algorithms to detect and measure objects in the physical world, such as buildings, roads, and trees, to create data-rich 3D models.

“为了解决越来越多的停车问题，我们想看看使用空中图像和人工智能检测和评估停车场是否可行，”says Rasthofer.“通过自动化功能和数字内容的提取，我们认为我们可以大大减少创建地图所​​需的时间，而不会牺牲准确性。”

A cost-effective method of creating digital maps is particularly interesting to Tier one automotive suppliers and original equipment manufacturers (OEMs) to support the autonomous navigation industry; however, many other industries can make use of the information as well.

HXGN内容程序提供

基于Martinsburg的15厘米GSD数据，由检测到的车道标记创建的语义车道模型

2014年，HXGN内容程序开始收集美国，欧洲部分地区和加拿大人口稠密地区的投机现成的矫正图像，以创建可供客户使用的数据库。目的是在人口较少的地区获得无云的30厘米分辨率，4波段图像以及人口大于50,000的都会区的15厘米分辨率。

Through the HxGN Content Program, TerraLoupe obtained 15-cm resolution orthoimagery of Berlin to test its internally developed object-identification algorithms. The initial work on Berlin took eight weeks to train the algorithms to accurately identify and categorise parking spaces, followed by just three days to analyse and produce maps for all of Germany.

“通过HXGN内容程序访问图像，使我们能够下载所需的地理位置，然后在新数据上训练我们的算法，”explained Rasthofer.“There are always slight differences in architecture, infrastructure, and road systems that are unique to each country. We check the confidence interval for each object and recheck low percentages. As we correct errors, the algorithms continue to learn and improve until we achieve a very high accuracy level.”

The aerial orthoimages available through the HxGN Content Program go through a rigorous QA/QC process to ensure delivery of survey-grade images.“ HXGN内容计划最适合我们客户在自动驾驶，停车援助和保险/再保险公司损失报告领域的需求，”says Rasthofer.“We also successfully deliver intelligence related to infrastructure, utilities, railways, and others for a variety of purposes.”

机器学习加快了准确的映射

Terraloupe的项目表明，可以有效地使用高分辨率的航空轨道与机器学习结合来提取数字内容。停车分析提供了有用的信息，例如停车场的位置，入口和出口以及可以适合每个批次的不同类别的汽车数量（紧凑，中型，大型）。城市规划师，送货人员，出租车司机和拥挤零售区的顾客都可以从这种改善的停车情报中受益。

“The availability of high-resolution, high-accuracy imagery determined where we started the project; however, we intend to perform this analysis on all of Europe as data becomes available through Hexagon, and we’d like to expand our services into the U.S.,”says Rasthofer.“总的来说，我们的目标是有效提取所有类型的对象并创建一个完整的数字环境。”

获得空中图像比陆地方法更快，更高效，可以更频繁地更新，这对于许多应用至关重要。亚搏苹果appHexagon的全球运营可产生图像的广泛可用性，并与数据提供商建立良好的业务合作伙伴关系，以继续满足对数字地图的不断增长的需求。