With the increasing demand for 3D city models in various applications, providing efficient access to 3D city models on the web has become very important and valuable. However, there is a lack of web services in which users can directly and effectively access the city objects contained in the 3D city models on the web. Another problem with web services related to 3D city models is that the size of 3D city models can become very large. As a consequence, the browser will not respond immediately until all city objects in the requested 3D city model have arrived. Addi-tionally, although file-based systems are the easiest way of data storage, they have deficiencies in web services that require scalability, efficiency, and flexibility in data access. To fix these is-sues, the goal of this research is to develop the an efficient way of disseminating 3D city models on the web, to discover the method of enabling 3D city models to be parsed incrementally and efficiently by the browser, and to explore a proper database solution for storing 3D city models to support the fast data access.

CityJSON has advantages in web applications over the CityGML data format, thus being chosen as the main datasets used in this research. Inspired by OGC API – Feature, a RESTful API for fast access to geospatial features in CityJSON has been developed. The second part of my research is related to the data streaming. CityJSONFeature has been proposed to enable CityJSON to be parsed incrementally on the web. To improve the overall data streaming perfor-mance, I proposed two methods to stream the data from the database to the RESTful API so that the RESTful API can immediately start constructing the first CityJSONFeature and sending it to the user. The third part of my research is to explore a proper database solution for CityJSON datasets to best support the fast data access in the RESTful API. In addition, some auxiliary data is added into the database to improve the RESTful API’s capabilities with efficient data filtering and streaming.

To evaluate the efficiency of the implemented methodology, a systematic benchmarking has been performed on three aspects: 1) the database schema, 2) the two streaming methods, and 3) the performance difference between the DBMS and the file system. The results show that in most use cases the DBMS can better support the RESTful API than the file system with the help of the built-in query and index mechanism. In addition, the overall streaming performance is largely improved when adding data streaming from the DBMS to the RESTful API. Lastly, based on the benchmarks, an optimal database schema has been chosen to support the RESTful API with fast data access, efficient data filtering and streaming.

With the constantly evolving range of applications for technology the quality and amount of data constantly increases as well. In this growing data environment, there is a constant search to provide more value to all data that is available for as little effort as possible. Our research tries to add such additional value by diving into the concept of classifying point cloud by using deep learning, specifically in the indoor environment. This is done by first doing a neural network comparison and then doing a case study. In the neural network comparison, a look is taken into which of the neural networks that are capable of working with point clouds is best suited for our experiments in the indoor scene, based on the training speed, accuracy, ease of use concerning training on external datasets and setting up the network and space efficiency. After the comparison, we chose to continue with the PointCNN network during the case study. The case study is performed on data the NS (Nederlandse Spoorwegen) provided to us and all test results we got from our experiments can be visualized using the web application we developed along with this project. The purpose of the case study is to add extra value to the indoor LiDAR point cloud the NS has captured from Amersfoort Station by using deep learning to automatically classify assets present in their data. The value is in purposes, such as asset management, where the data does not need possibly hundreds of man-hours to be labelled. This saves a lot of time and also money each time a scan is made. In the case study we found through 4 different experiments that unbalanced data makes for bad results, but when a scene is labelled correctly very good results can be found in a local scene.