This thesis explores methods to enhance the usability of vario-scale maps, focusing on user interaction, cognitive load, and overall satisfaction. Vario-scale maps, based on a continuous zooming approach introduced by Van Oosterom and Meijers, aim to reduce visual disruptions by allowing smooth generalisation of geographic data in real time. The research highlights their potential to merge precision and aesthetic clarity wxhile advancing beyond the conventional snapping behaviour inherent in multi-scale interfaces.
A mixed-method approach was adopted to investigate whether vario-scale maps could improve navigation and user satisfaction. Data were collected through a pilot web-based usability study, followed by a laboratory eye-tracking study designed to capture detailed behavioural and cognitive processes. Quantitative metrics, such as fixation counts and scanpath lengths, were analysed to determine efficiency in map-use tasks, while think-aloud protocols and questionnaires provided qualitative insights into participant preferences and frustrations.
Findings indicate that vario-scale and multi-scale maps offer similar levels of efficiency based on objective eye-tracking metrics. However, participants generally reported higher satisfaction with vario-scale maps, citing smoother transitions, less visual interruption, and a more intuitive zooming experience. The pilot study underscored the importance of user familiarity with Cartography, the careful placement of icons, and task complexity as influential factors in map usability. These insights informed the final study, which showed that vario-scale maps align more closely with user expectations for modern digital interfaces.
By emphasising controlled task design, participant comfort, and iterative refinement through pilot testing, this thesis demonstrates how vario-scale maps can be optimised for enhanced usability. The results further suggest that leveraging cartographic principles—such as appropriate symbol density-plays an essential role in maintaining clarity and facilitating user orientation. Through comprehensive analysis of both objective metrics and subjective evaluations, this thesis provides evidence that improving vario-scale usability is both feasible and beneficial. Future research with broader user demographics and extended usage contexts is recommended to validate and refine the proposed design strategies, ultimately supporting a wider adoption of vario-scale technology for interactive and dynamic mapping applications.

Geo-information has many applications in various sectors and is used by public agencies as well as the private sector. A key method for sharing and visualising geo-information is
through web viewers. Extensive infrastructure exists for creating web viewers to publish 2D geo-information without the need of coding, much of which is based on OpenLayers.

3D geo-information is becoming increasingly important for many use cases, such as urban planning, asset management, and disaster management. Consequently, there is an increasing need to publish 3D geo-information in web viewers without the need of coding. Since applications to do this for 2D geo-information are widely in use, it would be useful to be able to effectively integrate 3D functionality into an existing application.

This research investigates the needs for integrating 3D functionality into an existing OpenLayers based GIS web application that allows the user to publish geo-information in web viewers. This includes both the technical needs, e.i. how to integrate the necessary code, and the user needs, e.i. what features does the user need to make the application useful and user-friendly.

A proof of concept was developed to investigate the technical needs. The proof of concept is an extension of the existing application Tailormap. Tailormap is based on OpenLayers and allows users to publish geo-information in web viewers without the need of coding. 3D functionality is integrated into Tailormap to allow the users to publish 3D vector data, such as buildings, alongside 2D OpenLayers maps. This integration was achieved by incorporating Cesium functionality to load and visualise 3D Tiles datasets.

To ascertain the user needs, interviews were conducted with Tailormap users and the created proof of concept was tested with them to gather feedback. The interviews and testing sessions revealed that the most critical type of 3D data for publishing in a web viewer was vector data, specifically buildings. Furthermore, specific functions were identified that make the application useful for the users.

The developed technical methodology and the identified user needs are primarily applicable to integrating 3D functionality into Tailormap but can also serve as guidelines for integrating 3D functionality into other OpenLayers-based GIS web applications for publishing geo-information in web viewers.

With the proliferation of the IoT infrastructure, the trajectory data is dynamically emerging. This data originates from a variety of moving objects, containing big volumes of multi-dimensional information such as space, time, semantics etc. The underlying information can be potentially applied to create added value through scientific research, decision-making, emergency management etc.

However, due to the special properties of the trajectory data, namely high frequency, cardinality, dimensionality, heterogeneity etc., traditional data management systems face difficulties in handling such data. Even though some distributed solutions or big data solutions exist in other fields, they are not designed considering the modelling, accessing, distributing and querying characteristics of this special spatio-temporal data.

Given the spatial data management problems, a clustering/indexing solution for high dimensional Point Cloud by Space-filling curve considering the heterogeneous data spatial distribution has been developed, advocated and validated by a series of research finished at the GDMC, TU Delft.

However, it is uncertain whether the framework can be extended to other types of space-related phenomena. Furthermore, whether distributed database techniques can be utilized remains to be explored and what adjustments should be made is still unclear. To some extent, this thesis is an expanded study based on the Point Cloud research mentioned above.

To address these data management challenges, this thesis focuses on trajectory data modelling and compression, indexing and clustering, partitioning and distributing. Also, the querying strategies are studied. More specifically, the three main results of this thesis are:

Model the trajectory as the sequence split by semantic attributes and a spatio-temporal cube. This modelling takes proximity (locality) preservation and trajectory preservation into account at the same time, resulting in a balanced level of flexibility and aggregation, mitigating the storage burden by row-wise compression. For different subdivision resolutions (depth of Octree for space partitioning), the compression ratio can be up to 10.

Access the trajectory data by Space-filling Curve. The Space-filling Curve indexing method maps the 3D (high dimensional) indices to 1D (low dimensional) indices, overcoming the contradiction between high dimensionality and high cardinality. Adaptive Octree is used to mitigate the heterogeneity of the trajectory data. Based on the experiment results, the optimal tree depth is 4 or 5. The query optimization (specifically, the range merging technique) is also preliminarily explored.

Distribute the data on the distributed machines. This distributed deployment results in higher (nearly linearly in the experiment) scalability (horizontal expansion of disk, memory and CPU resources) and speed-up. The Space-filling Curve distributing strategy results in better load-balancing. However, due to the lack of flexibility of the distributed database platform used (specifically, Greenplum), the localization of data and computation (such as local aggregation) is limited.

The rise of urban digital twins emphasises the critical role of representing real-world environments for decision-making and collaboration among stakeholders. This leads to an expectation of effective management and visualisation of large-scale urban data models. 3D Tiles is an open specification designed for streaming and rendering massive heterogeneous 3D geospatial datasets. It is widely adopted in fields such as urban planning and engineering construction, where geospatial data is crucial for decision-making and collaboration.

However, as the 3D data size grows, the performance of traditional file-based solutions is facing challenges. Currently, dissemination and visualisation of 3D data are often reliant on file-based systems. Users suffer from problems such as long load times, data inconsistencies, and lack of flexibility. In this thesis, an approach for compactly storing both geometries and attributes in the Database Management System (DBMS) and efficiently serving data compliant with 3D Web standards to the client is proposed, advocating the direct serving of 3D Tiles from the database.

This study explores the effectiveness of Virtual Reality (VR) compared to the use of 2D interfaces in interpreting point cloud data, focusing on user perception, interaction and relative measurement accuracy. Visualizing point clouds is often challenging due to the limitations in translating three-dimensional data into two-dimensional screens. VR offers a potential solution to enhance depth perception and deepen user understanding. The research utilizes Omnibase, a platform developed by Geodelta, that integrates various spatial data types, including point clouds, for applications such as municipal boundary measurements.

The study involved participants that are either familiar or unfamiliar with point clouds, to evaluate VR versus Omnibase. Quantitative measurements and qualitative feedback were collected on either platform. Results indicate that while VR provides better depth perception and a more immersive experience, it presents a steeper learning curve, especially for inexperienced users, additionally, it comes with physical side effects. The measurements in Omnibase showed higher consistency, though not necessarily greater accuracy, due to depth misinterpretations.

In addition to the study, the VR testing environment was developed using Potree.

Accurate positioning has become an essential component of modern life, crucial for applications ranging from navigation and industrial operations to emergency response. The Global Navigation Satellite System (GNSS) has traditionally provided reliable positioning, but its effectiveness diminishes in environments where satellite signals are obstructed, such as dense urban areas and indoor spaces. This thesis explores the potential of Fifth-Generation (5G) wireless communication technology, specifically utilizing Received Signal Strength Indicator (RSSI) data for positioning, as an alternative to GNSS. The research investigates the effectiveness of 5G positioning through trilateration and compares it with GNSS-Real-Time Kinematic (RTK) positioning. The study aims to validate the accuracy and the reliability of 5G positioning and various real-world scenarios, focusing on challenging environments. Key aspects examined include the impact of topography on positioning accuracy and the influence of network distribution on Position Dilution of Precision (PDOP). By attaching a 5G modem to a laptop, field measurements were collected and analyzed against the "ground truth" provided by GNSS-RTK. The results demonstrate the potential of 5G RSSI-based positioning to serve as a robust positioning solution. This study’s findings hold significant relevance for the geomatics community, with implications for urban planning, infrastructure development, environmental monitoring, and disaster management. Through critical analysis and validation, this thesis contributes to the advancement of positioning technologies, highlighting the limitations of 5G trilateration using RSSI, yet proposing it as a potential complement to GNSS. The findings pave the way for future research and practical applications in enhancing precise positioning systems.

Point cloud data are gaining more importance in the Geomatics domain, with the development of modern sensing technologies like LiDAR and photogrammetry. The size of massive point clouds are growing, and the performance of traditional database solutions for its data management become insufficient. It remains a huge challenge for researchers to come up with a data management solution that handles the huge volumes of data, while providing standardized functionalities.

Space Filling Curve (SFC) has been explored as a good spaital access techniques for organizing point clouds. Existing SFC-based database solutions include PlainSFC, HistSFC, etc. However, they use a flat table to store point records and it is not compact for massive point clouds. In this thesis, a SFC-based database solution that manages point cloud in blocks is proposed. The purpose is to improve the performance of current point cloud database solutions, especially with storage space. This model organizes the point clouds based on Space Filling Curve, and innovatively splits each SFC key to a head and a tile. The points with the same SFC head are placed in the same block. SFC tails and other property dimensions are stored as arrays in other columns.

Compared with the pgPointCloud and Oracle SDO_PC, the intermediate SplitSFC prototype does not show significant advantage in storage and data retrieval efficiency so far. However, it is fair to believe that with the improvement of algorithms and implementations, it has the potential to be an approximate and efficient point cloud data management solution.

This research examines the multifaceted challenges posed by urbanization in the Netherlands, specifically focusing on the intricate relationships between modes of mobility and societal well-being. Central to this exploration is studying the modal split and understanding how shifts in this split can have profound implications for urban areas. The Dutch Ministry of Infrastructure and Water Management’s (Min IenW) Well-being Monitor is the foundation for this study, aiming to chart the interplay of living environment, safety, health, and accessibility. It illuminates the potential consequences of changing transportation choices (modal split) on broader environmental concerns such as the Urban Heat Island effect, noise disturbance, air quality, and climate resilience. While cars due to their ubiquity, cars play a central role in the research. However, the importance of Non-Motorized Transport (NMT) and Public Transport (PT) is also highlighted, given their significant roles in urban mobility and their potential to redefine the modal split. Using Rotterdam as a case study, the research delves into the spatial constraints of one of the Netherlands’ largest cities, emphasizing the need for effective urban planning that prioritizes sustainable transportation solutions and envisions future shifts in transportation preferences. The goal is to offer valuable insights that can influence urban strategies, ensuring sustainable and livable cities.

As the way we interact with maps keeps changing, so do the maps change alongside. And it can be easily pointed out how these changes come alongside a large number of advantages for the average map user, such as quick access to data or the ability to view more or less of the Earth's surface with just a mouse scroll, as well as for specialists such as cartographers or spatial data analysts, as it is now easier then ever to manipulate complex data. That being said, the challenges have also shifted, from the expertise of the map maker to the software solutions which now do all the work.

One of the many challenges imposed by the aforementioned is represented by the way the map generalization process is achieved. This graduation project serves as a continuation to the countless amount of research which has already been performed in this field, with a focus on the niche world on Vario-Scale Maps, and in particular how borders are handled in this generalization process.

There is already a large number of different solutions available, some of them being considered as standard and used by some of the biggest players in the world of geo-information. However, it seems that no single one solution is a `silver bullet`, as they all have their advantages and disadvantages, as well as cases where one generalization workflow is clearly more suited then others.

Considering the actual status quo of the industry, this thesis will take a look at some of these already available solutions on the market, both individually as well as together, and will try to answer the following research question: \emph{To what extent can multiple line-generalization algorithms be (simultaneously) introduced in the Vario-Scale structure such that they preserve the topology and enable an optimal line density (while trying to preserve the characteristics of the initial shape as well).}

To reach an answer, it is necessary to start first from the lowest level, with understanding how line generalization function in different situations, then slowly building up the structure by introducing these new concepts in the broader workflow, to see what impact it has on it as a whole.

Due to safety or preservation reasons, certain objects or areas need to be inspected regularly. Currently, the inspection of objects is mostly done in-person, which is labour-intensive and not very effective. With the use of drones, areas and objects can be inspected from new angles at a much faster rate. To effectively monitor these objects with drones, the position and location needs to be extracted from drone data in real time.
In this thesis, a case study is done on the localisation of fisher boats in restricted areas. Several components are integrated to create a prototype. The pretrained YOLOv3 detection model is trained on acquired nadir boat images, which makes it able to predict the bounding boxes of boats on images captured with drones. A positioning algorithm is constructed, which calculates the geographical coordinates from the pixel coordinates for images taken both in a nadir and an oblique angle. A real time connection is constructed between the drone and the prototype. This is done by creating a connection with Google Drive with the drone controller and the prototype. The positioned polygon bounding boxes are localised using a real time dashboard, which visualises the bounding boxes in a map with other relevant layers.
The results indicate that the performance of the components and prototype as a whole are satisfactory for this use case. To deploy this prototype in other object localisation use cases, it is recommended to train the pretrained model further, use a drone with more accurate equipment and run the prototype on the drone controller.

The kinds of physical spaces present in the real world are becoming ever more complex, and the locations defining the boundaries between these spaces are often arbitrary. Distinguishing between which spaces count as `outdoors,' and which count as `indoors,' becomes more difficult when `semi-outdoor' and `semi-indoor' spaces are considered. Integrating these different spaces within geovisualisations is difficult because data on the spaces are often collected and stored separately.
Many existing navigational applications avoid the explicit differentiation between different types of spaces, or choose to only visualise one type of space.
Additionally, these applications rarely identify which areas are visible to users at their present positions, and which areas are occluded.
This thesis explores the potential of utilising point clouds directly in geovisualisations to communicate information about the types of spaces surrounding a hypothetical user in a real-world environment.

Raw point cloud data is collected on three different transitional spaces, all of which contain an outdoor element. These point clouds are classified into four different `space-types' (outdoor, indoor, semi-indoor, and semi-outdoor), and visibility analysis is performed on them directly. The resulting information on space-type and visibility is combined within multiple different data visualisations, the concepts of which have been designed using a list of requirements based on existing literature.
The visualisations show that there is potential for direct use of point clouds in communicating information about spaces to a user, and that discerning between visible and occluded spaces, has potential value to a user orienting themselves within their environment with aid of a geovisualisation.

Over the years, the pace at which data is generated keeps on increasing. As a consequence, the data itself no longer holds the highest value, but rather the information and context the data captures are. This principle also holds in the 3D environment modelling scene, as accurately depicting an environment holds more value than the number of models there are of it.

One of the major problems in 3D environments, especially when the environment represents a building, is the presence of glass. A lot of the data captured to model these 3D environments is captured using LiDAR laser scanning. This is where glass becomes a problem as glass is almost completely transparent to laser beams at the typical wavelengths used when using LiDAR laser scanning. As a consequence, glass can lead to problems with navigational routes as it is invisible in the environment but still blocks the path. It can also create false spaces in the captured environment as it can also partially act as a mirror reflecting the laser beam and showing these reflections in space as if they were captured in a straight line.

Alternative manners for capturing and identifying glass in environments captured with laser have been created over the years, but they often need a dedicated set-up, expensive equipment or a lot of data. These solutions are not always feasible for users of point cloud data.

Therefore, in this thesis a focus is put on how can a low entry solution be created for this problem, which leads to the main research question: How can the location of glass be deduced using only information acquired from 3D point clouds and a reference position?

To answer this question, this thesis focuses on the deduction of the locations of glass windows in the provided input. To find these a projection from 3D data to 2D is performed. In 2D image space, contours are then detected that match the criteria of window frames. These contours are then used to segregate parts of the 3D point cloud that should contain the window detected in the projection. After clustering these parts and the best matching cluster is deduced to be a window.

In this thesis, it is shown that using the proposed methodology it is possible to deduce the location of glass in a LiDAR point cloud using only an additional reference position, but there are some flaws with the simplified input of the method.

In this thesis a new method to show the generalization process is proposed and assessed - parallel step assignment. It investigates the feasibility and applicability of this method with respect to three proposed generalization approaches and tries to evaluate the possibility of vario-scale map representation. First, the generalization sequence is created for each of these approaches. It is then processed with a greedy algorithm in order to decide which generalization operations can be shown at the same time. After that, two aspects are assessed: the generalization of the map quality and the assignment itself. The results show that the method is feasible for the vario-scale maps, however, the proposed generalization techniques need to be improved and none of them can be considered as suitable at its current state of development. Nevertheless, the main aspect of the interaction is significantly improved and the solution can be considered feasible. At last, based on various observations and conclusions from the project, some ideas for future work are proposed together with an evaluation of the chosen methodology with underlined drawbacks.

Navigational charts play a vital role in a ship's safety while navigating the seas, rivers or lakes. With most of the features and obstructions being out of sight -below sea-level - these charts are more critical than e.g. topographic maps. For routing but also positioning, depth information is a key aspect on these charts. This depth information is available in either depth contours, coloured depth areas and individual soundings. However with the data originating from accurate but usually erratic survey data, a visualisation of raw data is not sufficient for use in a navigational chart directly. It would not clearly convey the information to a human operator in one sight, and thus this visualisation is in need of generalisation: a simplified representation of the same data with irrelevant details being omitted. This thesis gives new insights in the generalisation process for isobaths only and proposes a new framework to deal with those. We propose a framework based on a novel auxiliary data structure to link a triangulation to the resulting isobaths: the triangle region graph. It links the position of isobaths directly to individual triangles, as well as establishes relations between the isobaths themselves. With this structure we ultimately link the survey data to the final cartographic product and thus in theory we could integrate all information across the generalisation pipeline in one and the same process. We have successfully used this framework with a basic rule-based evaluation model: we isolate conflicting isobaths, triangles and vertices based on legibility requirements and target generalisation operators on those. With this approach we can successfully maintain more of the morphology while still yielding a finely legible chart. Especially at large scale charts the results are promising: narrow channels, pits and bends remain if legibility permits. With smaller scale charts the challenge now is to generalise beyond smoothness. More radical generalisation operators are needed to omit all irrelevant details. However the overall framework using the triangle region graph as integrating mechanism has potential to do so. It is easily extensible due to its modular approach and can incorporate most depth information: from survey accuracy to size of isobaths and even golden sounding selection in the future.

This MSc thesis aims to research how to efficiently manage 4D AIS data (Longitude, Latitude, Time, and MMSI (the ID of the vessel)) to do the fast query by using the Space Filling curve in PostgreSQL. The AIS is the Automatic Identification System which is born because the frequent occurrence of maritime accidents has caused casualties and economic losses. AIS is intended to assist a vessel’s watchstanding officers and allow maritime authorities to track and monitor the vessels’ movement. For now, the AIS has been used in various kinds of fields because the AIS data is really important and useful. The AIS data contains lots of useful information such as the dynamic information (including ship location, speed, heading, and so on), the static information (including ship name, ship type...), and some other types of data. Because of the useful information that AIS includes, there a great many useful applications based on the AIS data. For example, the AIS data is used for detecting the vessels’ anomalies motions or tracking the vessels. While the studies mainly focus on the applications of the AIS data, The efficient management of the AIS data is neglected. Hence, I am going to study how to efficiently manage the multidimensional AIS data. Space Filling Curve (SFC) will be used to manage the multidimensional AIS data. The SFC is a great method for indexing the multidimensional data. The SFC can map data in multidimensional space to 1D space. There are lots of kinds of SFC, such as Morton curve, Hilbert curve, Gray curve, and so on. And the Morton curve and Hilbert curve are used in this thesis because of the property of the locality form the nD space is preserved in the location on the curve [Dai and Su, 2003] and both are so-called quadrant recursive curves [Meijers and van Oosterom, 2018] which is the very significant property. In my research, I proposed two kinds of methods to manage 4D AIS data. One is the 4D integrated approach that the 4D AIS data is encoded to SFC together. The other is the 3D integrated approach, only 3D AIS (Longitude, Latitude, and Time) data is encoded. To test the two approaches, bounding box query (to find the vessels in a given space and time range) and trajectory query (to find the position information of a specific vessel in a give time range) will be implemented in the database. To verify the usability and superiority of my approach, the benchmark is set. The comparison between the two approaches I proposed will be done. And results prove that the SFC approach I used to manage the 4D AIS data is great after comparing it with the benchmark I put forward.

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.

This research is aimed to answer the main research question: to what extent we can use LiDAR point clouds directly in the PostgreSQL by means of FDW, and thus a FDW supporting the Point Cloud Data Management System is implemented. Then, the range and performance of its functionality are evaluated. The results shows this FDW solution is feasible while the querying time is relevant to the number of returned points. The benefit and problem of this FDW are analyzed.

LiDAR technologies are used to measure point cloud data of the earth's surface. The usage of LiDAR allows for the fast collections of massive data sets. The AHN2 point cloud data set, part of Rijkswaterstaats initiative to map the surface of the Netherlands, contains 639 478 217 460 points. For efficient visualization in web viewers, these massive point clouds are stored in an octree data structure. Visualization through this method has the downside of discretely visualizing the point cloud. These discrete artefacts are referred to as density jumps, and are visible where there is a boundary between blocks retrieved from the octree. These blocks contain different densities because they are retrieved from different levels of the octree. This thesis proposes a continuous visualization method for massive point cloud data sets that aims to eliminate these density jumps. While the continuous visualization of vector data sets has been extensively researched, this is a novel field of research for point cloud data sets. This thesis explores the feasibility of a vario-scale visualization method, and aims to implement it in an existing web viewer architecture. Due to the massive nature of the AHN2 data set, cloud computing and distributed computing techniques are used to imrove the workflow. The presented methodology removes the \textit{density jumps} by determining an upper density bound for the point cloud density relative to the camera position. Circle packing theory is used to reinforce the upper bound continuously, thus removing artefacts created by discrete density jumps. A proof-of-concept for this theory is implemented in an existing point cloud web viewer architecture.

The contemporary trend shows a shift from rule-based algorithms to deep learning. In the last few years, this field has been developing rapidly and its popularity has increased to a large extent. This happened for a good reason, since deep learning was able to solve some of the hardest problems in fields like image recognition, natural language processing and speech recognition. A large proportion of its success has to be credited to the explosion of big data in the past two decades. Structured data sets are essential to deep learning systems. The rising amount of automatic identification system data is a key example in the current big data boom. The automatic identification system produces spatiotemporal movement data of vessels. It was designed as a collision avoidance system, but researchers have been looking into ways to leverage its data for other tasks. Analyzing behavior in the movement data of vessels can help policymakers and monitoring operators with decision making processes. Improving these processes lead to safer and more resilient marine environments. Unfortunately, the possibility of applying deep learning on vessel movement data is an underexposed topic. This project attempts to explore the research gap in this topic. The objective is therefore to give an overview of the possibilities, complications and opportunities given the current state of the art. Ultimately, this project may serve as a rough guide for those who wish to explore the crossings where deep learning and vessel movement data meet.

The performance of an Indoor Positioning System is highly related to the placement of the transmitting nodes that are used as references for the positioning estimations. Within this graduation project, we propose a methodology that can be used to optimize such a deployment and thus, increase the performance of an Indoor Positioning System that a) is based on Received Signal Strength Fingerprinting and b) is orientated towards providing location or zone estimations instead of exact positioning. The optimization process involves 4 fundamental components. Firstly, the modeling of the obstructions in the indoor environment and also the zone modeling. Then, the definition of the performance metric that can be used to evaluate each different deployment scenario, in which case, our proposed metric considers the separation area and distances between the zones in the RSS vector space. The third component is the radio propagation model, required for simulating the transmitted signals from each node, where a model based on the ray tracing technique is selected. Finally, the last component is the selection of the optimization function that will control and drive the whole optimization process by choosing which deployment schemes to evaluate. For that, the utilization of a Genetic Algorithm has been selected. The evaluation of our methodology showed that the most problematic regions in terms of localization accuracy are, as expected, those where different zones become adjacent. Yet, comparisons between regular node deployments and our optimized solutions indicated that, regardless the number of nodes, our optimization introduced in each case an overall localization improvement that was especially concentrated at the most problematic regions.