Footprints of buildings can provide cues about architectural styles and functional types. Learning such latent thematic information from geometry is relevant for various applications, such as urban planning and map generalization. A common task in this context is to cluster a set of building footprints based on their shape characteristics. In this paper, we present a novel method for this task which is based on concepts of graph similarity. We use a graph similarity measure that combines ideas from the Weisfeiler-Lehman-method and optimal transport theory. For the final clustering, we use spectral clustering. To obtain a meaningful graph representation, we propose two algorithms that transform the medial axis of a building footprint into a skeleton graph. We tested our algorithm on a data set from Boston and performed a small user study, where we also compared the results to an existing feature-based clustering method. The study gives a first hint that the results of our algorithm are in line with human similarity perception. Future work is needed to improve the stability of the proposed similarity measure and to confirm our findings with more extensive experiments.

doi:10.1145/3615900.3628790

@inproceedings{duong2023clusteringfootprint,
    abstract = {Footprints of buildings can provide cues about architectural styles and functional types. Learning such latent thematic information from geometry is relevant for various applications, such as urban planning and map generalization. A common task in this context is to cluster a set of building footprints based on their shape characteristics. In this paper, we present a novel method for this task which is based on concepts of graph similarity. We use a graph similarity measure that combines ideas from the Weisfeiler-Lehman-method and optimal transport theory. For the final clustering, we use spectral clustering. To obtain a meaningful graph representation, we propose two algorithms that transform the medial axis of a building footprint into a skeleton graph. We tested our algorithm on a data set from Boston and performed a small user study, where we also compared the results to an existing feature-based clustering method. The study gives a first hint that the results of our algorithm are in line with human similarity perception. Future work is needed to improve the stability of the proposed similarity measure and to confirm our findings with more extensive experiments.},
    address = {New York, NY, USA},
    author = {Duong, Sophie and Rottmann, Peter and Haunert, Jan-Henrik and Mutzel, Petra},
    booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Advances in Urban-AI},
    doi = {10.1145/3615900.3628790},
    isbn = {9798400703621},
    keywords = {graph kernels, medial axis, spectral clustering, building footprints, graph similarity},
    numpages = {10},
    pages = {22--31},
    publisher = {Association for Computing Machinery},
    series = {UrbanAI '23},
    title = {Clustering Building Footprint Polygons Based on Graph Similarity Measures},
    url = {https://doi.org/10.1145/3615900.3628790},
    year = {2023}
}

Building footprints are a prerequisite for many tasks such as urban mapping and planning. Such structures are mostly derived using airborne laser scanning which reveals rather roof structures than the underlying hidden footprint boundary. This paper introduces an approach to extract a 2D building boundary from a 3D point cloud stemming from either terrestrial scanning or via close-range sensing using a mobile platform, e.g. drone. To this end, a pipeline of methods including non-parametric kernel density estimation (KDE) of an underlying probability density function, a solution of the Travelling Salesperson Problem (TSP), outlier elimination and line segmentation are presented to extract the underlying building footprint. KDE turns out to be suitable to automatically determine a horizontal cut in the point cloud. An ordering of the resulting points in this cut using a shortest possible tour based on TSP allows for the application of existing line segmentation algorithms, otherwise dedicated to indoor segmentation. Outliers in the resulting segments are removed using DBSCAN. The segments are then generalized leading to the final footprint geometry. We applied our approach on real-world examples and achieved an IoU between 0.930 and 0.998 assessed by ground truth footprints from both authoritative and VGI data.

doi:10.5194/isprs-annals-X-4-W2-2022-233-2022

@article{rottmann2022AutomaticBuilding,
    abstract = {Building footprints are a prerequisite for many tasks such as urban mapping and planning. Such structures are mostly derived using
airborne laser scanning which reveals rather roof structures than the underlying hidden footprint boundary. This paper introduces an
approach to extract a 2D building boundary from a 3D point cloud stemming from either terrestrial scanning or via close-range sensing
using a mobile platform, e.g. drone. To this end, a pipeline of methods including non-parametric kernel density estimation (KDE)
of an underlying probability density function, a solution of the Travelling Salesperson Problem (TSP), outlier elimination and line
segmentation are presented to extract the underlying building footprint. KDE turns out to be suitable to automatically determine a
horizontal cut in the point cloud. An ordering of the resulting points in this cut using a shortest possible tour based on TSP allows for
the application of existing line segmentation algorithms, otherwise dedicated to indoor segmentation. Outliers in the resulting segments
are removed using DBSCAN. The segments are then generalized leading to the final footprint geometry. We applied our approach on
real-world examples and achieved an IoU between 0.930 and 0.998 assessed by ground truth footprints from both authoritative and
VGI data.},
    author = {Peter Rottmann and Jan-Henrik Haunert and Youness Dehbi},
    doi = {10.5194/isprs-annals-X-4-W2-2022-233-2022},
    journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
    pages = {233--240},
    title = {Automatic Building Footprint extraction from 3D laserscans},
    volume = {X-4/W2-2022},
    year = {2022}
}

@inproceedings{rottmann2022mosaicsets_compcart,
    address = {Bonn},
    author = {Peter Rottmann and Makus Wallinger and Annika Bonerath and Sven Gedicke and Martin Nöllenburg and Jan-Henrik Haunert},
    booktitle = {Abstracts of 1st Workshop on Computational Cartography 2022},
    month = {may},
    title = {{M}osaic{S}ets: {E}mbedding {S}et {S}ystems into {G}rid {G}raphs},
    year = {2022}
}

Visualizing sets of elements and their relations is an important research area in information visualization. In this paper, we present MosaicSets : a novel approach to create Euler-like diagrams from non-spatial set systems such that each element occupies one cell of a regular hexagonal or square grid. The main challenge is to find an assignment of the elements to the grid cells such that each set constitutes a contiguous region. As use case, we consider the research groups of a university faculty as elements, and the departments and joint research projects as sets. We aim at finding a suitable mapping between the research groups and the grid cells such that the department structure forms a base map layout. Our objectives are to optimize both the compactness of the entirety of all cells and of each set by itself. We show that computing the mapping is NP-hard. However, using integer linear programming we can solve real-world instances optimally within a few seconds. Moreover, we propose a relaxation of the contiguity requirement to visualize otherwise non-embeddable set systems. We present and discuss different rendering styles for the set overlays. Based on a case study with real-world data, our evaluation comprises quantitative measures as well as expert interviews.

doi:10.1109/TVCG.2022.3209485

@article{rottmann2022mosaicsets,
    abstract = {Visualizing sets of elements and their relations is an important research area in information visualization. In this paper, we present MosaicSets : a novel approach to create Euler-like diagrams from non-spatial set systems such that each element occupies one cell of a regular hexagonal or square grid. The main challenge is to find an assignment of the elements to the grid cells such that each set constitutes a contiguous region. As use case, we consider the research groups of a university faculty as elements, and the departments and joint research projects as sets. We aim at finding a suitable mapping between the research groups and the grid cells such that the department structure forms a base map layout. Our objectives are to optimize both the compactness of the entirety of all cells and of each set by itself. We show that computing the mapping is NP-hard. However, using integer linear programming we can solve real-world instances optimally within a few seconds. Moreover, we propose a relaxation of the contiguity requirement to visualize otherwise non-embeddable set systems. We present and discuss different rendering styles for the set overlays. Based on a case study with real-world data, our evaluation comprises quantitative measures as well as expert interviews.},
    author = {Peter Rottmann and Makus Wallinger and Annika Bonerath and Sven Gedicke and Martin Nöllenburg and Jan-Henrik Haunert},
    doi = {10.1109/TVCG.2022.3209485},
    journal = {IEEE Transactions on Visualization and Computer Graphics},
    number = {1},
    pages = {875-885},
    publisher = {IEEE},
    title = {MosaicSets: Embedding set systems into grid graphs},
    volume = {29},
    year = {2022}
}

We present a new method for the task of detecting groups of polygons in a given geographic data set and computing a representative polygon for each group. This task is relevant in map generalization where the aim is to derive a less detailed map from a given map. Following a classical approach, we define the output polygons by merging the input polygons with a set of triangles that we select from a constrained Delaunay triangulation of the input polygons' exterior. The innovation of our method is to compute the selection of triangles by solving a bicriteria optimization problem. While on the one hand we aim at minimizing the total area of the outputs polygons, we aim on the other hand at minimizing their total perimeter. We combine these two objectives in a weighted sum and study two computational problems that naturally arise. In the first problem, the parameter that balances the two objectives is fixed and the aim is to compute a single optimal solution. In the second problem, the aim is to compute a set containing an optimal solution for every possible value of the parameter. We present efficient algorithms for these problems based on computing a minimum cut in an appropriately defined graph. Moreover, we show how the result set of the second problem can be approximated with few solutions. In an experimental evaluation, we finally show that the method is able to derive settlement areas from building footprints that are similar to reference solutions.

doi:10.4230/LIPIcs.GIScience.2021.II.6

@inproceedings{rottmann2021bicriteria,
    abstract = {We present a new method for the task of detecting groups of polygons in a given geographic data set and computing a representative polygon for each group. This task is relevant in map generalization where the aim is to derive a less detailed map from a given map. Following a classical approach, we define the output polygons by merging the input polygons with a set of triangles that we select from a constrained Delaunay triangulation of the input polygons' exterior. The innovation of our method is to compute the selection of triangles by solving a bicriteria optimization problem. While on the one hand we aim at minimizing the total area of the outputs polygons, we aim on the other hand at minimizing their total perimeter. We combine these two objectives in a weighted sum and study two computational problems that naturally arise. In the first problem, the parameter that balances the two objectives is fixed and the aim is to compute a single optimal solution. In the second problem, the aim is to compute a set containing an optimal solution for every possible value of the parameter. We present efficient algorithms for these problems based on computing a minimum cut in an appropriately defined graph. Moreover, we show how the result set of the second problem can be approximated with few solutions. In an experimental evaluation, we finally show that the method is able to derive settlement areas from building footprints that are similar to reference solutions.},
    address = {Dagstuhl, Germany},
    annote = {Keywords: map generalization, aggregation, graph cuts, bicriteria optimization},
    author = {Peter Rottmann and Anne Driemel and Herman Haverkort and Heiko R{\"o}glin and Jan-Henrik Haunert},
    booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part II},
    doi = {10.4230/LIPIcs.GIScience.2021.II.6},
    editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
    isbn = {978-3-95977-208-2},
    issn = {1868-8969},
    pages = {6:1--6:16},
    publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
    series = {Leibniz International Proceedings in Informatics (LIPIcs)},
    title = {Bicriteria Aggregation of Polygons via Graph Cuts},
    url = {https://drops.dagstuhl.de/opus/volltexte/2021/14765},
    urn = {urn:nbn:de:0030-drops-147658},
    volume = {208},
    year = {2021}
}

Knowing the distance to nearby objects is crucial for autonomous cars to navigate safely in everyday traffic. In this paper, we investigate monocular depth estimation, which advanced substantially within the last years and is providing increasingly more accurate results while only requiring a single camera image as input. In line with recent work, we use an encoder-decoder structure with so-called packing layers to estimate depth values in a self-supervised fashion. We propose integrating a joint pre-training of semantic segmentation plus depth estimation on a dataset providing semantic labels. By using a separate semantic decoder that is only needed for pre-training, we can keep the network comparatively small. Our extensive experimental evaluation shows that the addition of such pre-training improves the depth estimation performance substantially. Finally, we show that we achieve competitive performance on the KITTI dataset despite using a much smaller and more efficient network.

doi:10.1109/IROS51168.2021.9636546

@inproceedings{rottmann2021iros,
    abstract = {Knowing the distance to nearby objects is crucial for autonomous cars to navigate safely in everyday traffic. In this paper, we investigate monocular depth estimation, which advanced substantially within the last years and is providing increasingly more accurate results while only requiring a single camera image as input. In line with recent work, we use an encoder-decoder structure with so-called packing layers to estimate depth values in a self-supervised fashion. We propose integrating a joint pre-training of semantic segmentation plus depth estimation on a dataset providing semantic labels. By using a separate semantic decoder that is only needed for pre-training, we can keep the network comparatively small. Our extensive experimental evaluation shows that the addition of such pre-training improves the depth estimation performance substantially. Finally, we show that we achieve competitive performance on the KITTI dataset despite using a much smaller and more efficient network.},
    author = {Peter Rottmann and Thorbjörn Posewsky and Andres Milioto and Cyrill Stachniss and Jens Behley},
    booktitle = {Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    doi = {10.1109/IROS51168.2021.9636546},
    pages = {5916-5923},
    title = {{Improving Monocular Depth Estimation by Semantic Pre-training}},
    year = {2021}
}

The automatic generation of travel-time maps is a prerequisite for many fields of application such as tourist assistance and spatial decision support systems. The task is to determine outlines of zones that are reachable from a user's location in a given amount of time. In this work, we focus on travel-time maps with a formally guaranteed separation property in the sense that a zone exactly contains the part of the road network that is reachable within a pre-defined time from a given starting point and start time. In contrast to other automated methods, our approach generates schematized travel-time maps that reduce the visual complexity by representing each zone by an octilinear polygon. We aim at octilinear polygons with a small number of bends to further optimize the legibility of the map. The reachable parts of the road network are determined by the integration of timetable information for different modes of public transportation and pedestrian walkways based on a multi-modal time-expanded network. Moreover, the generated travel-time maps visualize multiple travel times using a map overlay of different time zones. In experiments on real-world data we compare our schematic visualizations to travel-time maps created with other visualization techniques.

doi:10.1111/tgis.12821

@article{forsch2021isochrones,
    abstract = {The automatic generation of travel-time maps is a prerequisite for many fields of application such as tourist assistance and spatial decision support systems. The task is to determine outlines of zones that are reachable from a user's location in a given amount of time. In this work, we focus on travel-time maps with a formally guaranteed separation property in the sense that a zone exactly contains the part of the road network that is reachable within a pre-defined time from a given starting point and start time. In contrast to other automated methods, our approach generates schematized travel-time maps that reduce the visual complexity by representing each zone by an octilinear polygon. We aim at octilinear polygons with a small number of bends to further optimize the legibility of the map. The reachable parts of the road network are determined by the integration of timetable information for different modes of public transportation and pedestrian walkways based on a multi-modal time-expanded network. Moreover, the generated travel-time maps visualize multiple travel times using a map overlay of different time zones. In experiments on real-world data we compare our schematic visualizations to travel-time maps created with other visualization techniques.},
    author = {Forsch, Axel and Dehbi, Youness and Niedermann, Benjamin and Oehrlein, Johannes and Rottmann, Peter and Haunert, Jan-Henrik},
    doi = {10.1111/tgis.12821},
    journal = {Transactions in GIS},
    number = {6},
    pages = {3233--3256},
    title = {Multimodal Travel-Time Maps with Formally Correct and Schematic Isochrones},
    volume = {25},
    year = {2021}
}

The physical arrangement of soil compounds in microaggregates is important in many ways, e.g. by controlling soil stability and C sequestration. However, little is known about the spatial arrangement of organic and inorganic compounds in soil microaggregates, due to the lack of in-situ analyses in undisturbed material. Here we hypothesize that microaggregates are spatially organized, resulting in deterministic, predictable spatial patterns of different organic matter and mineral phases and that this organization depends on the abundance of specific phases such as on clay mineral content. We separated the water stable, occluded large and small microaggregate fractions from Ap horizons of a sequence of sandy to loamy Luvisols (19 to 35% clay, Scheyern, Germany) and subjected in total 60 individual aggregates to elemental mapping by electron probe micro analysis (EPMA), which recorded C, N, P, Al, Fe, Ca, K, Cl, and Si contents at µm scale resolution. Spatial arrangements of soil organic matter and soil minerals were extracted using cluster analyses. We found a pronounced heterogeneity in aggregate structure and composition, which was not reproducible and largely independent from clay content in soil. However, neighborhood analyses revealed close spatial correlations between organic matter debris (C:N app. 100:10) and microbial organic matter (C:N app. 10:1) indicating a spatial relationship between source and consumer. There was no systematic relationship between soil minerals and organic matter, suggesting that well-established macroscale correlations between contents of pedogenic oxides and clay minerals with soil organic matter storage do not apply to soil microaggregates.

@inproceedings{lehndorff2020spatial,
    abstract = {The physical arrangement of soil compounds in microaggregates is important in many ways, e.g. by controlling soil stability and C sequestration. However, little is known about the spatial arrangement of organic and inorganic compounds in soil microaggregates, due to the lack of in-situ analyses in undisturbed material. Here we hypothesize that microaggregates are spatially organized, resulting in deterministic, predictable spatial patterns of different organic matter and mineral phases and that this organization depends on the abundance of specific phases such as on clay mineral content. We separated the water stable, occluded large and small microaggregate fractions from Ap horizons of a sequence of sandy to loamy Luvisols (19 to 35% clay, Scheyern, Germany) and subjected in total 60 individual aggregates to elemental mapping by electron probe micro analysis (EPMA), which recorded C, N, P, Al, Fe, Ca, K, Cl, and Si contents at µm scale resolution. Spatial arrangements of soil organic matter and soil minerals were extracted using cluster analyses. We found a pronounced heterogeneity in aggregate structure and composition, which was not reproducible and largely independent from clay content in soil. However, neighborhood analyses revealed close spatial correlations between organic matter debris (C:N app. 100:10) and microbial organic matter (C:N app. 10:1) indicating a spatial relationship between source and consumer. There was no systematic relationship between soil minerals and organic matter, suggesting that well-established macroscale correlations between contents of pedogenic oxides and clay minerals with soil organic matter storage do not apply to soil microaggregates.},
    author = {Lehndorff, E. and Meyer, N. and Radionov, A. and Pl{\"u}mmer, L. and Rottmann, P. and Spiering, B. and Amelung, W. and Dultz, S.},
    booktitle = {EGU General Assembly Conference Abstracts},
    pages = {22405},
    title = {Spatial organization of soil microaggregates},
    year = {2020}
}

The mean residence time of organic matter in soil ranges from days to millenia. Although chemical and physical processes are known to control the storage of organic matter in soil, the role of spatial arrangements in soil remains largely unknown due to the lack of in-situ techniques operating on the micro-scale. We asked whether deterministic patterns in element arrangement occur in large and small soil microaggregates (250-53 and 53-20 µm), and how these contribute to the storage of organic matter. To do so, we studied surface soils with increasing clay contents (sandy to loamy Luvisols, Germany) and subjected 60 individual aggregates to elemental mapping by electron probe micro analysis (EPMA), which recorded C, N, P, Al, Fe, Ca, K, Cl, and Si contents at micrometer scale resolution. We further developed the first laser-ablation isotope ratio mass spectrometry technique in soil science detecting d13C composition on the micro-scale (LA-IRMS) and employed this to trace micro-gradients in undisturbed soil and soil microaggregate samples. We found a pronounced heterogeneity in aggregate structure and composition, which was not reproducible for different microaggregates from the same soil fraction, and which was largely independent from clay content in soil. However, neighborhood analyses revealed close spatial correlations between organic matter debris (C:N app. 100:10) and microbial organic matter (C:N app. 10:1), indicating a spatial relationship between source and consumer. There was no systematic relationship between soil minerals and organic matter, suggesting that well-established macroscale correlations between pedogenic oxides and clay with soil organic matter storage do not apply to soil microaggregates. From first applications of LA-IRMS to soil we found also a high d13C variability of up to 9 per mill along spatial gradients of less than 300 µm, suggesting the appearance of very small hotspots of isotope enrichment and organic matter turnover by metabolic processes.

@inproceedings{lehndorff2019element,
    abstract = {The mean residence time of organic matter in soil ranges from days to millenia. Although chemical and physical processes are known to control the storage of organic matter in soil, the role of spatial arrangements in soil remains largely unknown due to the lack of in-situ techniques operating on the micro-scale. We asked whether deterministic patterns in element arrangement occur in large and small soil microaggregates (250-53 and 53-20 µm), and how these contribute to the storage of organic matter. To do so, we studied surface soils with increasing clay contents (sandy to loamy Luvisols, Germany) and subjected 60 individual aggregates to elemental mapping by electron probe micro analysis (EPMA), which recorded C, N, P, Al, Fe, Ca, K, Cl, and Si contents at micrometer scale resolution. We further developed the first laser-ablation isotope ratio mass spectrometry technique in soil science detecting d13C composition on the micro-scale (LA-IRMS) and employed this to trace micro-gradients in undisturbed soil and soil microaggregate samples. We found a pronounced heterogeneity in aggregate structure and composition, which was not reproducible for different microaggregates from the same soil fraction, and which was largely independent from clay content in soil. However, neighborhood analyses revealed close spatial correlations between organic matter debris (C:N app. 100:10) and microbial organic matter (C:N app. 10:1), indicating a spatial relationship between source and consumer. There was no systematic relationship between soil minerals and organic matter, suggesting that well-established macroscale correlations between pedogenic oxides and clay with soil organic matter storage do not apply to soil microaggregates. From first applications of LA-IRMS to soil we found also a high d13C variability of up to 9 per mill along spatial gradients of less than 300 µm, suggesting the appearance of very small hotspots of isotope enrichment and organic matter turnover by metabolic processes.},
    author = {Lehndorff, E. and Rodionov, A. and Stremtan, C. and Brand, W. and Pl{\"u}mer, L. and Rottmann, P. and Spiering, B. and Amelung, W.},
    booktitle = {Geophysical Research Abstracts},
    title = {Element patterns and carbon turnover in soil microaggregates.},
    volume = {21},
    year = {2019}
}

In the last decades, there has been an increasing interest in animal protection and welfare issues. Heart rate variability (HRV) measurement with portable heart rate monitors on cows has established itself as a suitable method for assessing physiological states. However, more forward-looking technologies, already successfully applied to evaluate HRV data, are pushing the market. This study examines the validity and usability of collecting HRV data by exchanging the Polar watch V800 as a receiving unit of the data compared to a custom smartphone application on cows. Therefore, both receivers tap one signal sent by the Polar H7 transmitter simultaneously. Furthermore, there is a lack of suitable methods for the preparation and calculation of HRV parameters, especially for livestock. A method is presented for calculating more robust time domain HRV parameters via median formation. The comparisons of the respective simultaneous recordings were conducted after artifact correction for time domain HRV parameters. High correlations (r = 0.82–0.98) for cows as well as for control data set in human being (r = 0.98–0.99) were found. The utilization of smart devices and the robust method to determine time domain HRV parameters may be suitable to generate valid HRV data on cows in field-based settings.

doi:10.3390/s18082541

@article{Wierig_2018,
    abstract = {In the last decades, there has been an increasing interest in animal protection and welfare issues. Heart rate variability (HRV) measurement with portable heart rate monitors on cows has established itself as a suitable method for assessing physiological states. However, more forward-looking technologies, already successfully applied to evaluate HRV data, are pushing the market. This study examines the validity and usability of collecting HRV data by exchanging the Polar watch V800 as a receiving unit of the data compared to a custom smartphone application on cows. Therefore, both receivers tap one signal sent by the Polar H7 transmitter simultaneously. Furthermore, there is a lack of suitable methods for the preparation and calculation of HRV parameters, especially for livestock. A method is presented for calculating more robust time domain HRV parameters via median formation. The comparisons of the respective simultaneous recordings were conducted after artifact correction for time domain HRV parameters. High correlations (r = 0.82–0.98) for cows as well as for control data set in human being (r = 0.98–0.99) were found. The utilization of smart devices and the robust method to determine time domain HRV parameters may be suitable to generate valid HRV data on cows in field-based settings.},
    author = {Wierig, M. and Mandtler, L. and Rottmann, P. and Stroh, V. and Müller, U. and Büscher, W. and Plümer, L.},
    doi = {10.3390/s18082541},
    issn = {1424-8220},
    journal = {Sensors},
    month = {Aug},
    number = {8},
    pages = {2541},
    publisher = {MDPI AG},
    title = {Recording Heart Rate Variability of Dairy Cows to the Cloud—Why Smartphones Provide Smart Solutions},
    url = {http://dx.doi.org/10.3390/s18082541},
    volume = {18},
    year = {2018}
}