支持ing artificial intelligence solution to parking problem

作者：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 tocombine artificial intelligence and orthorectified aerial imageryto create an accurate inventory of available parking lots and spaces throughout Germany.

映射可用的停车位

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是一家位于德国慕尼黑的技术初创企业，专注于以创新的方式将Geodata和计算机分析相结合。从高分辨率矫形器开始，Terraloupe适用机器学习算法以检测和测量物理世界中的对象, 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.”

A创建数字地图的成本效益方法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 Content Program delivers

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

In 2014, theHxGN Content Program开始收集投机现成orthorectified imageryof the US, parts of Europe, and populated areas of Canada tocreate a database available to customers。目标是获取cloud-free 30-cm resolution, 4-band imagery在人口较少的地区，以及15厘米分辨率在都会区，有population greater than 50,000。

通过HXGN内容计划，Terralupe获得了柏林15厘米分辨率的矫形器to test its internally developed object-identification algorithms. The initial work on Berlin took八周训练算法准确识别和分类停车位，其次是three days to analyse and produce maps为整个德国。

“通过HXGN内容程序访问图像allows us to下载地理位置我们需要，然后train our algorithms on the new data，”Rasthofer解释了。“每个国家 /地区独有的建筑，基础设施和道路系统总是有轻微的差异。我们检查每个对象的置信区间，并重新检查低百分比。当我们纠正错误时，算法继续学习和改进，直到达到非常高的准确性水平为止。”

The通过HXGN内容程序获得的航空矫形图进行严格的QA/QC过程，以确保提供调查级图像。“The HxGN Content Program best suits the needs of our customers in theareas of autonomous driving, parking assistance, and loss reports用于保险/再保险公司”Rasthofer说。“We also successfully deliver与基础设施，公用事业，铁路有关的情报, and others for a variety of purposes.”

机器学习expedites accurate mapping

TerraLoupe’s project shows that高分辨率的航空射击图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 ofhigh-resolution, high-accuracy imagerydetermined 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.,”Rasthofer说。“总的来说，我们的目标是有效提取所有类型的对象，并且create a complete digital environment。”

获得航空影像更快and more高效的比陆地上的方法,允许更频繁pdates, which is crucial for many applications. Hexagon’s global operations generate widespread availability of imagery and good business partnerships with data providers to continue to meet the对数字地图的需求不断增长。