The current trend towards the use of smart tools within universities open up new opportunities. Insight in the location and daily rhythms of users within the building form valuable input for many decision-making processes within campus management. Unlike the outdoor environment, where the position is easily obtained through omnipresent satellite-based positioning systems such as GPS, the use of these systems in indoor environments is limited. Various implementations of indoor positioning systems try to fill this gap and provide indoor positioning capabilities. However, the complex indoor environment presents its own challenges regarding positioning. The effectiveness of current implementations varies depending on techniques and methods used, while often being limited to function only within small test bed environments. Privacy, cost, scalability, ease of integration in the environment and many other decisive factors steer the choice for certain indoor positioning systems. Within this research the focus is on the development of a non-intrusive network-based indoor positioning system using Wi-Fi. Wi-Fi has clear advantages over other measuring techniques in that these systems are ubiquitous, cost-effective and their use is multi-faceted. Now over 85% of the users carry a smartphone. These off-the-shelf, unmodified smartphones and other Wi-Fi-enabled mobile devices can be used as mobile stations to indicate user locations through an iterative positioning process. Besides the mobile stations, existing Wi-Fi infrastructures and networking equipment can be re-used with minor adaptations. The following research question was addressed in this research: 'To what extent can indoor Wi-Fi positioning be used for indoor localisation in order to determine occupancy rhythms and movement patterns within and between rooms to support campus management?'. The research question was approached starting with exploring the different techniques and methods suited for indoor positioning or localisation. The case study was used to review and develop a system suitable for indoor campus environments. As the case study requires a non-intrusive and low-cost solution that functions without active user participation a signal propagation-based technique was chosen. Distances were estimated employing the signal strength metadata extracted from various 802.11 management frames originating from user mobile devices. Various influences on the Wi-Fi signals were identified and a solution to counteract those influences was designed in the form of a differential Wi-Fi system using a grid of interconnected reference stations. With the use differential Wi-Fi correction parameters the positioning improved by mitigating for temporal environmental influences that are continuously present in indoor environments. Next, based on a comparison of different methods, a multilateration method was developed as the core of the positioning system. The multilateration algorithms combined with various filtering algorithms provide for a highly maintenance-free system with minimal manual set-up required. As the environment changes new training data is automatically acquired for ready-use. All algorithms were designed to work both in (near) real-time as well as non real-time, with configurable processing delays of at least 10 seconds. The longer delays allow for more observations, while the shorter delays are better for moving users and fast updates. Accuracies of 2 meters at the lower bounds are reported, ranging up to the 10 meter mark due to Wi-Fi characteristics. Various filtering and post-processing mechanisms were designed to generate accurate mappings from mobile devices to user counts. Furthermore, in the preparation phase different geometries were tested with reference stations 10 to 20 meters apart from each other. Preferred spatial geometries were matches as closely as possible for the deployment of the system in a real-world test case scenario. The designed reference stations were deployed in the Architecture faculty building to monitor occupancy and movement patterns before, during and after a fire drill. Ground truth was acquired using BLE wrist bands and manual counting. The fire drill had different elements: counting the number of users per m2 and per room, the occupancy rhythms over time and the movement patterns. Overall during 48% of the uncontrolled testing a 100% match between BLE wrist band equipped user counts and Wi-Fi counts was achieved. In 77% of the cases there was a 1 person difference in counting, where in more than 95% of the cases the maximum difference was 4 persons. In conclusion, with this localisation system the occupancy rhythms and movement patterns of users can be measured to a large extent to aid in the various tasks of campus management, e.g. planning, and safety evacuations.

Indoor localization provides for a much researched subject, as the complexity and size of many public buildings require extensive and properly designed methods to facilitate location specific processes. Indoor localization entails finding a qualitative description of the occupied area, that is human interpretable, rather than a quantitative position in Euclidean space. In other words, the context of an indoor environment has to be understood, such that a position can be transcended to a meaningful location. A space is defined as a mathematical structure with relational properties, to which all its members adhere. As a subset of space, topological space describes the relationships between (parts of) objects that do not change under continuous transformation. It further defines metric space, as a set where distances and angles between all of its elements are defined. Indoor space is then interpreted as a structure bounded by physical or functional elements, enabling human activities. It should entail the geometric place an actor is in, the topological structure that place is a part of, and the semantics giving the place meaning. Many indoor positioning methods have been developed, which can provide an actor with a relative geometric place. Most preferred are positioning systems not relying on a contingent system, which can be performed using a hybrid fusion of sensors embedded into a mobile device. Such a system found to perform sufficiently is VI-SLAM, simultaneously building a geometric place and tracking each pose and heading relatively. Its output is a mesh model, in which a viewshed of the indoor environment is built. From a mesh model, a topological structure can be derived in the form of its dual graph. Now to finalize this representation of indoor space which can be captured using a mobile device, it has to be enriched with a meaningful context. These semantics are generally stored in BIM models. Thus to transcend the position retrieved using SLAM to a location, is has to be matched with a BIM model, so that the appertaining semantics can be connected. The method proposed provides for a possibility to perform graph-based indoor localization, by extracting a graph from both input sources and comparing them, in order to find a match. However different in nature and structure, both input sources can be converted to a graph of similar calibre, such that they can be tested for a match. All operations performed on the graphs are derived from spectral graph theory. The graph simplification and analysis is performed using the eigen spectrum of the graph Laplacian, and the match is performed by remapping the spectral graphs into a vector sub space using the eigen spectrum of the data covariance matrix. After a match between both graphs is found, the current position of the actor within the mesh model can be translated to the room found in the graph. This room is now connected to a room within the reference graph, for which the semantics are stored in the BIM. Returning these to the actor, a location description is formed.

This graduation project is part of the Shared Heritage Lab; a collaboration studio between the master tracks of Heritage&Architecture, Architectural Engineering, Landscape Architecture and Urbanism of the Faculty of Architecture and the Built Environment from the TU Delft. All students joining this graduation lab focus on the question ‘How to evolve important heritage structures and areas of Bandung, in order to realise inclusive, thriving, and healthy environments for working, living and leisure?’. Within the scope of this question the issues found in the Pasar Baru Area in Bandung are addressed in the research and the design of this graduation project. The area is located in the middle of the city centre, has always been one of the major trade hubs of Bandung and is the old Chinese and Arabic neighbourhood. During the Dutch colonialization of Indonesia, the area developed in an interesting spatial way, as the city blocks, are characterized by an ‘inner’ and an ‘outer world’. The outer world consist of the periphery of the city blocks, where the shophouses, owned by Chinese merchants, define the spatial and urban character of the area. Behind those shophouses, in the middle of the city blocks which is called the ‘urban pocket’, the local Sundanese community resided in their kampung houses. This urban development resulted in an interesting spatial, social and economical balance in this part of the city. Nowadays, however, this balance is under threat, because of commercial investments, densification and pollution. In order to stop the process of decay in the Pasar Baru area and to create a pleasant environment for working, living and leisure, the design aims to restore the former balance in the area and to create a comfortable place for all the inhabitants, traders and customers. In this design the values of the heritage, the community and the environment are taken into account. This results in an urban strategy focussed on the restoration of the balance between the ‘inner’ and the ‘outer world’ of the city block, whilst still allowing for further densification to happen in this urban context. These rules guarantee a comfortable environment to live, work and relax, for a diverse group of people and also stimulate the use of more traditional building techniques in the architecture, in order to create a new local atmosphere in the area, and to take the issues of climate change into account. This strategy is further elaborated into an architectural design for the urban pocket, which consists of an expandable kampung house based on easy-to-understand building methods using only timber and bamboo. This house allows for cheap and comfortable housing and guarantees a pleasant residential environment for all the residents of the urban pocket. In order to guide the process of densification and commercialisation in the area, the urban strategy is also focussed on the design of the architecture in the periphery of the city block. To provide space for small businesses and to reintroduce housing into the area, four existing shophouses are retrofitted into a mini-market for street vendors and small shops and on top of those a timber high-rise structure is designed to house small studio apartments. The flexibility and the climate adaptivity of the building is created by the design of several movable panels in the interior and in the façade. Felt and glass panels in the interior allow the residents to arrange their own privacy and comfort, whilst a system of louvre and woven bamboo panels in the façade allow for enough natural ventilation to create a comfortable indoor climate. The bridge between the urban pocket and the periphery is created by low-rise apartments for families, where a similar building system is applied. The use of plants and grasses in the roofs, the facades and in the public realm of the urban pocket, create a comfortable environment for all residents and finalise the strategy, where all the urban pockets together are the lungs of the Pasar Baru area, both metaphorically as physically.

The declining availability of practical hours for medical anatomical education has prompted Enatom to develop a digital anatomical platform, utilizing the open-source WebGL-based point cloud renderer Potree. This platform, which employs detailed point cloud scans of anatomical structures, aims to offer a dynamic and interactive educational experience. Although Enatom's focus is not directly on geomatics, the techniques employed in this project have strong parallels with those used in geomatics, thereby enabling a symbiotic exchange of expertise. This interdisciplinary approach enhances the development of Enatom’s digital platform, with the potential to contribute to the field of geomatics. To address existing user experience challenges, this project has added a lasso-selection tool tailored for Potree, advanced annotation capabilities, and methods for sensitive data anonymization within the point cloud. The project's outcomes will be available in an open-source format at https://github.com/GEO1101-Synthesis-Group4/Selection-Annotation-Repo. This project exemplifies the versatile application of geomatics expertise beyond its traditional scope, demonstrating its potential in enhancing diverse domains such as medical education.

This paper has its focus on the accessibility, interoperability and quality of different point cloud datasets. The main objective of the project is to research the process of harmonizing point cloud datasets of different origins and to incorporate the integrated datasets in a web viewer that allows for visualization and analysis. As a case study, the Three-Country Point (TCP) where Germany, Belgium, and the Netherlands share a border is evaluated.

Along with the rise of the smart city movement, Internet of Things is an upcoming phenomenon. Objects and devices are becoming more and more wirelessly interconnected, communicating information between themselves and to human beings. As an extension on static sensor networks that gather real-time environmental data, the feasibility of implementing a dynamic sensor network based on LoRa communication is researched. To achieve such a dynamic system, a self-developed sensor platform was constructed, based on the microcontroller LoPy. Sensors attached to it include a hygrometer, thermometer and microphone. The emphasis of the research was on localisation of the sensors, to put the gathered sensor data into geographical context. A WiFi fingerprinting radiomap was constructed based on available MAC-addresses, their signal strengths, and GPS coordinates. The GPS module was only used for composing the radiomap. When the radiomap is completed, the module can be switched off, only to be switched on for periodical updates of the radiomap. The quality of the radiomap methodology was evaluated by constructing it of measurements gathered in four days, and testing it for the remaining three days. This test gave a correctness of 50% while another 38% of measurements were localised in a neighbouring cell. The correctness can be improved by having a longer training period. The quality of the collected sensor data turned out to be dependent on the weather conditions and the placement location on the carrier vehicle. Vehicle requirements were specified as driving through the city centre and having a schedule and route producing as little noise, heat and air pollution as possible. Another topic of research was LoRa communication, which was deemed as very limited for dynamic implementations, as the sending of location-related data takes up a large part of the already limited message size. To decrypt the sent message and store it in a meaningful database, Node-RED was used. Despite visualisation of measurements showed promising results, there is margin for improvement as far as data capturing is concerned.

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.

Open dumping, open burning and burying of municipal solid waste (MSW) can be the cause environmental and public health issues. These practices are more prevalent in developing countries such as Mexico,where proper waste management systems are not present. Considering the environmental and health issues, it is therefore important to minimise the number of open dumps in Mexico. The construction ofsanitary landfills is regarded as the best alternative to open dumping since it is the a cost-effective and environmentally friendly solution. An important part of constructing sanitary landfills is the selection of potential locations for these wastefacilities where investment will be made to build them. In order to select these locations first the weakspots need to be located. Weak spots are areas that do not have enough (proper) waste managementservices. Since Mexico does not have a national solid waste information system, a method to locate theseweak spots needs to be developed. With the use of the weak spots a method can be developed to select the potential locations for sanitary landfills that also takes the social, economical and legal constraintsinto account. The following research question is formulated: What are the weak spots in the current waste infrastructure network in Mexico and, based on this, where should strategic investment be madeto improve waste disposal? By answering this question, information will be provided on the issues withthe management of waste in Mexico with a focus on the areas of the weak spots and the locations where investment can be made to develop new sanitary landfills. To detect the weak spots, a set of factors of different scenarios were developed, scored, overlaid, and visualised in maps. Regions that have the lowest score were detected as weak spots. To select the potential locations for investment in new sanitary landfills a spatial decision support system (SDSS) was developed and implemented as a QGIS plugin. The weak spots that corresponded to urban areas were used for analysis in the SDSS. This is due to the fact that it is more economically beneficial to construct sanitary landfills in urban areas. The weak spot analysis showed that the southern region of Mexico, especially the state of Oaxaca, hadthe highest deficiencies in waste infrastructure. With the output from the QGIS SDSS plugin we are able to determine potential areas for new sanitary landfills in an automated manner. This research has resulted in the visualisation of the weak spots in the Mexican waste infrastructure and the selection of potential locations where investment can be made for the construction of new sanitary landfills. The approach for locating the weak spots of the waste infrastructure can be used to find the weak spots in other types of infrastructure on a state and country scale in Mexico. The QGIS SDSS plugin could also be used to locate sanitary landfills in Mexico that violate the standards and regulations. The approach used to develop methods to detect the weak spots in the waste infrastructure and select potential locations for investment into new sanitary landfills could be used as a model for other countries to develop their specific approaches.

Indoor localisation methods are an essential part for the management of COVID-19 restrictions, social distancing, and the flow of people in the indoor environment. Moving towards an open work space in this scenario requires effective real-time localisation services and tools, along with a comprehensive understanding of the 3D indoor space. This project’s main objective is to analyse how ArcGIS Indoors can be used with location awareness methods to elaborate and develop space management tools for COVID­-19 restrictions in order to reopen the workspace for TU Delft Campus. This was accomplished by using six Arduino micro controllers, which were programmed in C++ to scan all available Wi-­Fi fingerprints in the east wing of the Faculty of Architecture and the Built Environment of TU Delft and send over the data to an ArcGIS Indoor Information Model (AIIM). The data stored on the AIIM is then accessed using the app on the user’s Android device using REST Application Programming Interface (API) where a kNN based matching algorithm then identifies the location of the user. The results show that the localisation is not consistent for rooms that are directly above each other or share common access points. However, when functioning to locate different tables inside a room, the system proved to uniquely distinguish between the specific tables. As a result, we can conclude that based on the size of the rooms, more Arduino devices should be installed to achieve an ideal accuracy. Finally, recommendations are made for the continuation of this research.

The NMCAs (National Mapping and Cadastral Agencies) of European countries have different cadastral survey accuracy standards (European Global Navigation Satellite Systems Agency, 2019). In order to meet these standards, the appropriate equipment and services should be determined. The augmentation service Galileo High Accuracy Service (HAS), that is planned for 2022, will provide high accuracy Precise Point Positioning (PPP) corrections. Unlike other high-accuracy services, the Galileo HAS will be free of charge and available worldwide, without the need to be close to a base station or to a dedicated provider network. The PPP corrections will be provided through the Galileo signal as well as through the Internet (EUSPA, 2021). Because of the potential of the Galileo HAS, for the Synthesis Project we want to get insight in the accuracy of the augmentation service. Since a big share of cadastral surveys is performed in the built environment, we also want to determine the accuracy in an urban canyon. With the found accuracy, we can possibly judge whether Galileo HAS is suitable for cadastral surveys in the Netherlands, by comparing the measured accuracy to cadastral survey accuracy standards of the Dutch Kadaster. As a final conclusion for this project, Galileo HAS is still a technique under development and the PPP-based correction methods are currently not as accurate as the RTK-based ones. Galileo HAS will present in the future ways to correct these errors.

Blind and visually impaired people currently have inconveniences locating themselves in the indoor environment. No standardized system exists for them yet. After an inventory of the requirements of blind people, different representations do qualify for providing specific information blind people need. The main research question is: "How can blind people localise themselves (near) real-time in indoor environments with the combination of 3 representations of reality, namely (1) LiDAR point cloud matching, (2) ArcGIS Indoors and (3) Audio dynamic tactile map as the user interface?". Room detection and positioning of the user within the room are obtained by LiDAR scanning and point cloud matching. The processed point cloud height raster grids are acquired and imported into the Esri ArcGIS platform. The rooms are geo-referenced, and data is enriched by contextual awareness. As a user interface for blind people this report proposes two deliverables: a dynamic tactile map and an added or stand-alone audible supported user interface. Preliminary results of the qualitative validation show positive outcomes. This report is a stepping stone for the possibility of integrating multiple into one device.