Supporting artificial intelligence solution to parking problem

Author:琳达·达菲（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, launched a project to combine artificial intelligence and orthorectified aerial imagery to create an accurate inventory of available parking lots and spaces throughout Germany.

Mapping available parking

停车汽车可能是一项压力且耗时的活动，将来，自动导航的车辆将在没有驾驶员提供帮助的情况下寻找空间。需要准确测量和识别所有类型的物体（包括停车位）的高清数字地图的需求迅速成为现实。

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.

“To address the growing parking problem, we wanted to see if it was feasible to detect and assess parking lots using aerial imagery and artificial intelligence,”Rasthofer说。“By automating the extraction of features and digital content, we thought we could greatly reduce the time it took to create maps, without sacrificing the accuracy.”

创建数字地图的一种经济高效的方法对于一级汽车供应商和原始设备制造商（OEM）特别有趣，以支持自主导航行业。但是，许多其他行业也可以利用这些信息。

HXGN内容程序提供

Semantic lane model created out of detected lane markings based on 15 centimetres GSD data in Martinsburg

In 2014, theHXGN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。

通过HxGN内容项目,TerraLoupe获取ned 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.

“Access to imagery through the HxGN Content Program allows us to download the geographic locations we need, and then train our algorithms on the new data,”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.“The HxGN Content Program best suits the needs of our customers in the areas of autonomous driving, parking assistance, and loss reports for insurance/reinsurance companies,”Rasthofer说。“我们还成功地提供了与基础设施，公用事业，铁路和其他有关各种目的有关的情报。”

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。

“高分辨率，高准确图像的可用性决定了我们在哪里开始该项目；但是，我们打算在整个欧洲进行此分析，因为数据通过Hexagon获得，我们希望将服务扩展到美国，”亚搏苹果appRasthofer说。“Overall our goal is to efficiently extract all types of objects and create a complete digital environment.”

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。