Supporting artificial intelligence solution to parking problem

Author:Linda Duffy.

With a finite number of parking spaces and an increasing number of cars, frustration and inconvenience related to parking is growing. Recognising this as a problem, Manuela Rasthofer, CEO ofTerraLoupe GmbH，推出了一个项目来结合人工智能和矫正空中图像，为整个德国的可用停车场和空间创建准确的库存。

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.

“为了解决不断增长的停车问题，我们希望看到使用空中图像和人工智能检测和评估停车场是否是可行的，”says Rasthofer.“通过自动化特征和数字内容的提取，我们认为我们可以大大减少创建地图所​​花费的时间，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15厘米GSD数据在Martinsburg中的15厘米GSD数据出现的语义车道模型

2014年，HXGN内容计划开始收集美国，欧洲部分地区和加拿大人口稠密地区的投机现货地图，以为客户提供数据库。目标是获得免费的30厘米分辨率，4频段图像，超过人口较少的地区，以及15厘米的地铁地区，人口大于50,000。

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内容程序访问图像允许我们下载我们需要的地理位置，然后在新数据上培训我们的算法”，“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在自动驾驶，停车援助和保险/再保险公司的损失报告中的客户的需求，”says Rasthofer.“We also successfully deliver intelligence related to infrastructure, utilities, railways, and others for a variety of purposes.”

机器学习加快准确的映射

Terraloupe的项目表明，高分辨率的空中OrthoImages与机器学习相结合，可以有效地用于提取数字内容。停车分析提供了有用的信息，例如停车场的地点，入口和出口以及不同类别的汽车数量（紧凑，中型，大），可以适应每批。拥挤的零售领域的城市规划师，送货人，出租车司机和顾客都可以从这种改进的停车智能中受益。

“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.“整体我们的目标是有效地提取所有类型的物体并创建完整的数字环境。”

获得空中图像比陆地方法更快，更高效，允许更频繁的更新，这对许多应用来说至关重要。bob体育报道六角形的全球业务产生了与数据提供商的广泛可用性，以继续满足对数字地图不断增长的需求。