Recent developments in consumer-grade panoramic cameras led to new possibilities in creating virtual tours, e.g., through museums or real estate. Most current approaches for the creation of these tours are based on manual generation of navigable links between the images. We consider cases where large numbers of georeferenced images are taken by multiple users and introduce an approach for automatically generating a graph structure that links the images in a reasonable network while sorting out redundant data. Our approach does not expect a trajectory of temporally related points but merely an incoherent point cloud, e.g., from crowdsourcing of many different contributors. We expand our methodology by a visualization approach offering in-image navigation, realized through interactive links pointing towards neighbored images. Our method delivers a virtual tour with adjustable density on and apart from road networks. We compared the output of our algorithm to human-made virtual tour graphs and were able to verify a high similarity, which approves the hypothesis that automatically generated virtual tours can be intuitive for users.

doi:10.1007/s41651-025-00228-1

@article{baltzer2025graph,
    abstract = {Recent developments in consumer-grade panoramic cameras led to new possibilities in creating virtual tours, e.g., through museums or real estate. Most current approaches for the creation of these tours are based on manual generation of navigable links between the images. We consider cases where large numbers of georeferenced images are taken by multiple users and introduce an approach for automatically generating a graph structure that links the images in a reasonable network while sorting out redundant data. Our approach does not expect a trajectory of temporally related points but merely an incoherent point cloud, e.g., from crowdsourcing of many different contributors. We expand our methodology by a visualization approach offering in-image navigation, realized through interactive links pointing towards neighbored images. Our method delivers a virtual tour with adjustable density on and apart from road networks. We compared the output of our algorithm to human-made virtual tour graphs and were able to verify a high similarity, which approves the hypothesis that automatically generated virtual tours can be intuitive for users.},
    author = {Baltzer, Dorian and Naumann, Alexander and Rosenberg, Stephan and Haunert, Jan-Henrik},
    doi = {10.1007/s41651-025-00228-1},
    journal = {Journal of Geovisualization and Spatial Analysis},
    pages = {},
    publisher = {Springer Nature},
    title = {Graph Construction and Interactive Visualization for Virtual Tours Based on Redundant Panoramic Image Collections},
    url = {hhttps://link.springer.com/article/10.1007/s41651-025-00228-1},
    volume = {9},
    year = {2025}
}

Stop-and-go laser scanning is becoming increasingly prevalent in a variety of applications, e.g., the survey of the built environment. For this, a surveyor needs to select a set of standpoints as well as the route between them. This choice, however, has a high impact on both the economic efficiency of the respective survey as well as the completeness, accuracy, and subsequent registrability of the resulting point cloud. Assuming a set of building footprints as input, this article proposes a one-step optimization method to find the minimal number of selected standpoints based on scanner-related constraints. At the same time, we incorporate the length of the shortest route connecting the standpoints in the objective function. A local search method to speed up the time for solving the corresponding Mixed-Integer Linear Program (MILP) is additionally presented. The results for different scenarios show constantly shorter routes in comparison to existing approaches while still maintaining the minimal number of standpoints. Moreover, in our formulation we aim to minimize the effects of inaccuracies in the software-based registration. Inspired by the ideas of network survivability, we to this end propose a novel definition of connectivity tailored for laser scanning networks. On this basis, we enforce redundancy for the registration network of the survey. To prove the applicability of our formulation, we applied it to a large real-world scenario. This paves the way for the future use of fully automatic autonomous systems to provide a complete and high-quality model of the underlying building scenery.

doi:https://doi.org/10.1016/j.isprsjprs.2025.03.017

@article{knechtel2025scanPlanningNetwRedundancy,
    abstract = {Stop-and-go laser scanning is becoming increasingly prevalent in a variety of applications, e.g., the survey of the built environment. For this, a surveyor needs to select a set of standpoints as well as the route between them. This choice, however, has a high impact on both the economic efficiency of the respective survey as well as the completeness, accuracy, and subsequent registrability of the resulting point cloud. Assuming a set of building footprints as input, this article proposes a one-step optimization method to find the minimal number of selected standpoints based on scanner-related constraints. At the same time, we incorporate the length of the shortest route connecting the standpoints in the objective function. A local search method to speed up the time for solving the corresponding Mixed-Integer Linear Program (MILP) is additionally presented. The results for different scenarios show constantly shorter routes in comparison to existing approaches while still maintaining the minimal number of standpoints. Moreover, in our formulation we aim to minimize the effects of inaccuracies in the software-based registration. Inspired by the ideas of network survivability, we to this end propose a novel definition of connectivity tailored for laser scanning networks. On this basis, we enforce redundancy for the registration network of the survey. To prove the applicability of our formulation, we applied it to a large real-world scenario. This paves the way for the future use of fully automatic autonomous systems to provide a complete and high-quality model of the underlying building scenery.},
    author = {Knechtel, Julius and Dehbi, Youness and Klingbeil, Lasse and Haunert, Jan-Henrik},
    doi = {https://doi.org/10.1016/j.isprsjprs.2025.03.017},
    issn = {0924-2716},
    journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
    pages = {59-74},
    title = {Simultaneous Planning of Standpoints and Routing for Laser Scanning of Buildings with Network Redundancy},
    url = {https://www.sciencedirect.com/science/article/pii/S0924271625001157},
    volume = {224},
    year = {2025}
}

Historical maps represent an invaluable memory which should be preserved. Such kind of maps are, however, mostly scanned and stored as raster graphics which do not contain semantic information in a machine-readable form. To achieve a machine-readable state, an often expensive human intervention is needed in a fully manual or semi-automatic fashion. An automatic interpretation and a feature extraction is then inevitable for a map digitization and vectorization. Automatic approaches showed more and more convincing and promising results on challenging map corpora avoiding human interaction. This paper deals with the semantic segmentation of historical maps based on Graph Convolutional Networks (GCNs) to capture long-range dependencies between image features. This allows for an extension of the receptive field of Convolutional Neural Networks (CNNs) restricted on local dependencies. A Self-Constructing Graph (SCG) module has been applied to automatically induce the structure of the GCN. We performed experiments revealing promising results where our approach achieved an Mean Intersection over Union (mIoU) of 0.68, outperforming a state-of-the-art CNN dedicated to the semantic segmentation of historical maps.

doi:10.1080/15230406.2025.2468304

@article{arzoumanidis2025semSegHistMaps,
    abstract = {Historical maps represent an invaluable memory which should be preserved. Such kind of maps are, however, mostly scanned and stored as raster graphics which do not contain semantic information in a machine-readable form. To achieve a machine-readable state, an often expensive human intervention is needed in a fully manual or semi-automatic fashion. An automatic interpretation and a feature extraction is then inevitable for a map digitization and vectorization. Automatic approaches showed more and more convincing and promising results on challenging map corpora avoiding human interaction. This paper deals with the semantic segmentation of historical maps based on Graph Convolutional Networks (GCNs) to capture long-range dependencies between image features. This allows for an extension of the receptive field of Convolutional Neural Networks (CNNs) restricted on local dependencies. A Self-Constructing Graph (SCG) module has been applied to automatically induce the structure of the GCN. We performed experiments revealing promising results where our approach achieved an Mean Intersection over Union (mIoU) of 0.68, outperforming a state-of-the-art CNN dedicated to the semantic segmentation of historical maps.},
    author = {Lukas Arzoumanidis and Julius Knechtel and Jan-Henrik Haunert and Youness Dehbi},
    doi = {10.1080/15230406.2025.2468304},
    journal = {Cartography and Geographic Information Science},
    number = {0},
    pages = {},
    title = {Semantic segmentation of historical maps using Self-Constructing Graph Convolutional Networks},
    url = {https://www.tandfonline.com/doi/full/10.1080/15230406.2025.2468304},
    volume = {},
    year = {2025}
}

@inproceedings{bonerath2025bubblecharts,
    author = {Bonerath, Annika and Evans, William and Haunert, Jan-Henrik and Kirkpatrick, David and Kobourov, Stephen},
    booktitle = {Proceedings of the 40th European Workshop on Computational Geometry (EuroCG'24)},
    note = {Preprint. Accepted for publiaction.},
    title = {{Disc Placement for Dynamic Bubble Charts}},
    year = {2025}
}

doi:10.48565/bonndoc-515

@phdthesis{bonerath2025thesis,
    author = {Bonerath, Annika },
    doi = {10.48565/bonndoc-515},
    school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
    title = {{G}eometric {A}lgorithms for the {V}isual {E}xploration of {S}patiotemporal {D}ata},
    type = {Dissertation},
    url = {https://hdl.handle.net/20.500.11811/12847},
    year = {2025}
}

User interfaces for inspecting spatio-temporal events often allow their users to filter the events by specifying a time window with a time slider. We consider the case that filtered events are visualized on a map using textual or iconic labels. However, to ensure a clear visualization, not all filtered events are annotated with a label. We present algorithms for setting up a data structure that encodes for every possible time window the set of displayed labels. Our algorithms ensure that the displayed labels never overlap and guarantee the stability of the labeling during certain basic interactions with the time slider. Assuming that the labels have different priorities (weights), we aim to maximize the weight of the displayed labels integrated over all possible time windows. As basic interactions, we consider moving the entire time window, symmetrically scaling it, and dragging one of its endpoints. We consider two stability requirements: (1) during a basic interaction, a label should appear and disappear at most once; (2) if a label is displayed for a time window $Q$, then it is also displayed for all the time windows contained in $Q$ and that contain its timestamp. We prove that finding an optimal solution is NP-hard and propose efficient constant-factor approximation algorithms for unit-square and unit- disk labels, as well as a fast greedy heuristic for arbitrarily shaped labels. In experiments on real-world data, we compare the non-exact algorithms with an exact approach through integer linear programming.

doi:10.1109/TVCG.2025.3527582

@article{bonerath2025twl,
    abstract = { User interfaces for inspecting spatio-temporal events often allow their users to filter the events by specifying a time window with a time slider. We consider the case that filtered events are visualized on a map using textual or iconic labels. However, to ensure a clear visualization, not all filtered events are annotated with a label. We present algorithms for setting up a data structure that encodes for every possible time window the set of displayed labels. Our algorithms ensure that the displayed labels never overlap and guarantee the stability of the labeling during certain basic interactions with the time slider. Assuming that the labels have different priorities (weights), we aim to maximize the weight of the displayed labels integrated over all possible time windows. As basic interactions, we consider moving the entire time window, symmetrically scaling it, and dragging one of its endpoints. We consider two stability requirements: (1) during a basic interaction, a label should appear and disappear at most once; (2) if a label is displayed for a time window $Q$, then it is also displayed for all the time windows contained in $Q$ and that contain its timestamp. We prove that finding an optimal solution is NP-hard and propose efficient constant-factor approximation algorithms for unit-square and unit- disk labels, as well as a fast greedy heuristic for arbitrarily shaped labels. In experiments on real-world data, we compare the non-exact algorithms with an exact approach through integer linear programming. },
    address = {Los Alamitos, CA, USA},
    author = {Bonerath, Annika and Driemel, Anne and Haunert, Jan-Henrik and Haverkort, Herman and Langetepe, Elmar and Niedermann, Benjamin},
    doi = {10.1109/TVCG.2025.3527582},
    issn = {1941-0506},
    journal = { IEEE Transactions on Visualization \& Computer Graphics },
    keywords = {Labeling;Data visualization;Annotations;Tornadoes;Heuristic algorithms;Data structures;Spatial databases;Reproducibility of results;Earthquakes;Approximation algorithms},
    number = {01},
    pages = {1-15},
    publisher = {IEEE Computer Society},
    title = {{ Algorithms for Consistent Dynamic Labeling of Maps With a Time-Slider Interface }},
    url = {https://doi.ieeecomputersociety.org/10.1109/TVCG.2025.3527582},
    volume = {},
    year = {2025}
}

Routing through a dynamic environment is mostly carried out by using maps that integrate information about time-dependent parameters, such as traffic conditions and spatial constraints, which is a challenging and cumbersome task. We address the complex scenario where a user has to plan a route on a network that is dynamic with respect to edges that change their congestion through time. We perform an experimental user study where we compare interactive and non-interactive interfaces, the complexity levels of the map structures (number of nodes and edges) and of the paths (number of nodes that need to be visited), and the effects of familiarity with the map. The results of our study indicate that an interactive interface is more beneficial than a non-interactive interface for more complex paths, while a non-interactive interface is more beneficial than an interactive interface for less complex paths. In detail, while the number of nodes and edges of the network had no effect on the performance, we observed that (not surprisingly) the more complex the path, the longer the processing time and the lower the correctness. We tested the familiarity with a test–retest design, where we organized a second session of tests, labeled T2, after the first session T1. We observed a familiarization effect in T2, that is, the participants’ performance improved for the networks known from T1.

doi:10.2352/J.Percept.Imaging.2025.8.000401

@article{bonerath2025userstudy,
    abstract = {Routing through a dynamic environment is mostly carried out by using maps that integrate information about time-dependent parameters, such as traffic conditions and spatial constraints, which is a challenging and cumbersome task. We address the complex scenario where a user has to plan a route on a network that is dynamic with respect to edges that change their congestion through time. We perform an experimental user study where we compare interactive and non-interactive interfaces, the complexity levels of the map structures (number of nodes and edges) and of the paths (number of nodes that need to be visited), and the effects of familiarity with the map. The results of our study indicate that an interactive interface is more beneficial than a non-interactive interface for more complex paths, while a non-interactive interface is more beneficial than an interactive interface for less complex paths. In detail, while the number of nodes and edges of the network had no effect on the performance, we observed that (not surprisingly) the more complex the path, the longer the processing time and the lower the correctness. We tested the familiarity with a test–retest design, where we organized a second session of tests, labeled T2, after the first session T1. We observed a familiarization effect in T2, that is, the participants’ performance improved for the networks known from T1.},
    author = {Annika Bonerath and Claus-Christian Carbon and Silvia Miksch and Maurizio Patrignani and Alessandra Tappini},
    doi = {10.2352/J.Percept.Imaging.2025.8.000401},
    journal = {Journal of Perceptual Imaging},
    keywords = {spatio-temporal networks, route planning, user study, complexity, navigation},
    number = {0},
    pages = {1--1},
    title = {Time-Dependent Route Planning on Maps: A User Study},
    url = {https://library.imaging.org/jpi/articles/8/0/000401},
    volume = {8},
    year = {2025}
}

Livestock production requires a thorough understanding of animal welfare to increase productivity and ensure appropriate housing conditions. The expanding availability of consumer-grade virtual and augmented reality devices opens new possibilities for precision livestock farming (PLF), where sensor technology traditionally monitors real-time animal data. In poultry farming, monitoring each bird individually is often not economically feasible due to the large flock sizes. To address this issue, we propose a novel method to evaluate housing conditions by focusing on the visual and temperature preferences of domestic chickens, considering these factors within a broader environmental context. Chickens perceive light at a wider range of wavelengths than humans, which significantly influences their behavior. Additionally, temperature variations, such as heat leaks and accumulations, can contribute to stress and negative behaviors in the flock. We developed a device comprising smart glasses equipped with specialized cameras to capture thermal infrared, ultraviolet, and visible RGB (red, green, blue) light, alongside real-time user position tracking. Points of interest (POIs) can be added to the logged tracking data along with captured content. The data collected by the glasses can be used to create virtual tours embedded in a 3D model of the barn, providing a comprehensive view of on-site conditions. We also introduce a streamlined pipeline for building these virtual tours using the Unity game engine, making the content accessible for agricultural education. This approach enables users to remotely gain insights into the housing conditions of poultry without needing a physical visit, enhancing both learning and engagement in animal welfare practices.

doi:10.1016/j.atech.2025.100786

@article{baltzer2025smart,
    abstract = {Livestock production requires a thorough understanding of animal welfare to increase productivity and ensure appropriate housing conditions. The expanding availability of consumer-grade virtual and augmented reality devices opens new possibilities for precision livestock farming (PLF), where sensor technology traditionally monitors real-time animal data. In poultry farming, monitoring each bird individually is often not economically feasible due to the large flock sizes. To address this issue, we propose a novel method to evaluate housing conditions by focusing on the visual and temperature preferences of domestic chickens, considering these factors within a broader environmental context. Chickens perceive light at a wider range of wavelengths than humans, which significantly influences their behavior. Additionally, temperature variations, such as heat leaks and accumulations, can contribute to stress and negative behaviors in the flock. We developed a device comprising smart glasses equipped with specialized cameras to capture thermal infrared, ultraviolet, and visible RGB (red, green, blue) light, alongside real-time user position tracking. Points of interest (POIs) can be added to the logged tracking data along with captured content. The data collected by the glasses can be used to create virtual tours embedded in a 3D model of the barn, providing a comprehensive view of on-site conditions.
We also introduce a streamlined pipeline for building these virtual tours using the Unity game engine, making the content accessible for agricultural education. This approach enables users to remotely gain insights into the housing conditions of poultry without needing a physical visit, enhancing both learning and engagement in animal welfare practices.},
    author = {Baltzer, Dorian and Douglas, Shannon and Haunert, Jan-Henrik and Dehbi, Youness and Tiemann, Inga},
    doi = {10.1016/j.atech.2025.100786},
    journal = {Smart Agricultural Technology},
    pages = {100786},
    publisher = {Elsevier},
    title = {Smart Glasses in the Chicken Barn: Enhancing Animal Welfare Through Mixed Reality},
    url = {https://www.sciencedirect.com/science/article/pii/S2772375525000206?via%3Dihub},
    volume = {10},
    year = {2025}
}

An important task of pattern recognition and map generalization is to partition a set of disjoint polygons into groups and to aggregate the polygons within each group to a representative output polygon. We introduce a new method for this task called bicriteria shapes. Following a classical approach, we define the output polygons by merging the input polygons with a set of triangles that we select from a conforming Delaunay triangulation of the input polygons’ exterior. The innovation is that we control the selection of triangles with a bicriteria optimization model that is efficiently solved via graph cuts. In particular, we minimize a weighted sum that combines the total area of the output polygons and their total perimeter. In a basic problem, we ask for a single solution that is optimal for a preset parameter value. In a second problem, we ask for a set containing an optimal solution for every possible value of the parameter. We discuss how this set can be approximated with few solutions and show that it is hierarchically nested. Hence, the output is a hierarchical clustering that corresponds to multiple levels of detail. An evaluation with building footprints as input and a comparison with α-shapes that are based on the same underlying triangulation conclude the article. An advantage of bicriteria shapes compared to α-shapes is that the sequence of solutions for decreasing values of the parameter is monotone with respect to the total perimeter of the output polygons, resulting in a monotonically decreasing visual complexity.

doi:10.1145/3705001

@article{rottmann2025bicriteriashapes,
    abstract = {An important task of pattern recognition and map generalization is to partition a set of disjoint polygons into groups and to aggregate the polygons within each group to a representative output polygon. We introduce a new method for this task called bicriteria shapes. Following a classical approach, we define the output polygons by merging the input polygons with a set of triangles that we select from a conforming Delaunay triangulation of the input polygons’ exterior. The innovation is that we control the selection of triangles with a bicriteria optimization model that is efficiently solved via graph cuts. In particular, we minimize a weighted sum that combines the total area of the output polygons and their total perimeter. In a basic problem, we ask for a single solution that is optimal for a preset parameter value. In a second problem, we ask for a set containing an optimal solution for every possible value of the parameter. We discuss how this set can be approximated with few solutions and show that it is hierarchically nested. Hence, the output is a hierarchical clustering that corresponds to multiple levels of detail. An evaluation with building footprints as input and a comparison with α-shapes that are based on the same underlying triangulation conclude the article. An advantage of bicriteria shapes compared to α-shapes is that the sequence of solutions for decreasing values of the parameter is monotone with respect to the total perimeter of the output polygons, resulting in a monotonically decreasing visual complexity.},
    address = {New York, NY, USA},
    articleno = {3},
    author = {Rottmann, Peter and Driemel, Anne and Haverkort, Herman and R\"{o}glin, Heiko and Haunert, Jan-Henrik},
    doi = {10.1145/3705001},
    issn = {2374-0353},
    issue_date = {March 2025},
    journal = {ACM Transactions on Spatial Algorithms and Systems},
    keywords = {Map generalization, aggregation, graph cuts, bicriteria optimization},
    month = {Feb},
    number = {1},
    numpages = {23},
    pages = {1--23},
    publisher = {Association for Computing Machinery},
    title = {Bicriteria Shapes: Hierarchical Grouping and Aggregation of Polygons with an Efficient Graph-Cut Approach},
    volume = {11},
    year = {2025}
}