Steffen Illium, M.Sc.

Steffen Illium, M.Sc.

Lehrstuhl für Mobile und Verteilte Systeme

Ludwig-Maximilians-Universität München, Institut für Informatik

Raum

Telefon:

Fax:

Mail: steffen.illium@ifi.lmu.de

🔬 Research topics

🧑‍🏫Lehre
  • Active:
    Machine Learning:

    • Emergence in MARL
    • Self-Replicating Neural Networks
    • Deep Audio Analysis
    • Data Augmentation
    • Classification
    • Deep Learning Architecture
    • Memory Horizon in RNN
  • Past:
    • Clustering
    • Geoinformatics
    • Trajectory Analysis
  • Seminare:
    • TIMS: Trends in Mobilen und Verteilten System (Hauptseminar / Bachelor)
    • VTIMS: Vertiefende Themen in Mobilen und Verteilten Sytemen (Hauptseminar / Master)
  • Praktika:
    • Praktikum Mobile und Verteilte Systeme (MSP) (Masterpraktikum)
    • Praktikum IOS Entwicklung (Masterpraktikum)
  • Vorlesungen:
    • Betriebssysteme (Grundlagenvorlesung Bachelor)
    • Rechnerarchitektur (Grundlagenvorlesung Bachelor)
    • I-o-T / Python Crashkurs (Vorlesung Bachelor / Master)

Website and Contact

🌐 Steffenillium.de

ℹ️ Allgemeine Informationen zu Abschlussarbeiten am Lehrstuhl
http://www.mobile.ifi.lmu.de/lehre/abschlussarbeiten/

✅ Past Theses Topics

  • 🎓 MSc.:
    • Klassifizierung von Primatenlauten mit Deep Learning mithilfe binärem Vorsortierens
    • Vereinbarkeit von Risiko und Sicherheit im Bestärkenden Lernen am Beispiel Windy Bridge
    • Breaking the Security of Optical PUFs through Deep Learning Techniques
    • Audiosignalverarbeitung und Clustering von Audiosignalen in Wasserleitungen
    • Segmentierung und Klassifizierung von geometrischen Primitiven in 3D-Punktwolken mit neuronalen Netzen
    • Approximating the Wave Function Collapse-Algorithm with a Growing Neural Cellular Automata
    • Überwachtes Lernen auf synthetischen Leckgeräuschen zur Leckerkennung in Wasserverteilungsanlagen
    • Lokalisierung von Leckstellen in Wasserverteilungsanlagen mit Deep Learning via Data Augmentation
    • Analyse unüberwachten Clusterings von Isovisten mittels Attention-Mechanismus
  • 🧑‍🎓 BSc.:
    • Evaluating new Data Augmentation Methods: VoronoiPatches & PatchFocus
    • Machine Learning Attacks on Room PUFs in Facility Protection
    • Evaluation des Einflusses von Oberflächen Normalen auf Performance und Training in Pointnet++
    • Linear and non-linear machine learning attacks on physical unclonable functions
    • Lernen geeigneter Repräsentationen zur Anomalie-Erkennung in Audio-Daten durch unüberwachte Pseudo-Klassifikation
    • Explainability in Vision Transformers: Exploring Attention for the Fashion-MNIST Dataset
    • Hierarchical Transformer-based Document Encoder for Information Retrieval
    • Betrachtung der Flatland-Challenge als Market Game
    • Investigating Memory Horizons in Neural Memory Networks
    • Exploration of parameter boundaries in self-replicating artificial neural networks
    • Additional tasks for self-replicating neural networks
    • Steuerung multipler Agenten in einer Räuber-Beute Umgebung mittels Deep Reinforcement Learning auf Bilddaten
    • Verbesserung der durch einen Autoencoder erzeugten Faceembeddings mithilfe zusätzlicher Trainingsziele
    • Selbstüberwachtes Lernen von Spektrogramm-Repräsentationen zur Anomalieerkennung in Audiodaten
    • Evaluation von Data-Augmentation Techniken auf Audio-Daten
    • Reachability analysis for a hypothetical car sharing service
    • Vergleichende Analyse generativer neuronaler Netze auf Datenbasis des Wave Function Collapse-Algorithmus
    • Graph Convolutional Networks with Knowledge Graphs for Text Classification
    • Überwachtes Lernen einer Homotopie-Relation für 2D-Trajektorien

📚 Publikationen

ResearchGate | Google Scholar | Arxiv

2023

  • M. Kölle, S. Illium, M. Zorn, J. Nüßlein, P. Suchostawski, and C. Linnhoff-Popien, „Improving Primate Sounds Classification using Binary Presorting for Deep Learning.“ 2023.
    [BibTeX] 
    @inproceedings {koelle23primate,
title = {Improving Primate Sounds Classification using Binary Presorting for Deep Learning},
author = {K{\"o}lle, Michael and Illium, Steffen and Zorn, Maximilian and N{\"u}{\ss}lein, Jonas and Suchostawski, Patrick and Linnhoff-Popien, Claudia},
year = {2023},
organization = {Int. Conference on Deep Learning Theory and Application - DeLTA 2023},
publisher = {Springer CCIS Series},
}

  • M. Zorn, S. Illium, T. Phan, T. K. Kaiser, C. Linnhoff-Popien, and T. Gabor, „Social Neural Network Soups with Surprise Minimization.“ 2023, p. 65. doi:10.1162/isal_a_00671
    [BibTeX] [Download PDF] 
    @inproceedings{zorn23surprise,
author = {Zorn, Maximilian and Illium, Steffen and Phan, Thomy and Kaiser, Tanja Katharina and Linnhoff-Popien, Claudia and Gabor, Thomas},
title = {Social Neural Network Soups with Surprise Minimization},
volume = {ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference},
series = {ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference},
pages = {65},
year = {2023},
month = {07},
doi = {10.1162/isal_a_00671},
url = {https://doi.org/10.1162/isal\_a\_00671},
eprint = {https://direct.mit.edu/isal/proceedings-pdf/isal/35/65/2149250/isal\_a\_00671.pdf},
}

2022

  • S. Illium, G. Griffin, M. Kölle, M. Zorn, J. Nüßlein, and C. Linnhoff-Popien, VoronoiPatches: Evaluating A New Data Augmentation MethodarXiv, 2022. doi:10.48550/ARXIV.2212.10054
    [BibTeX] [Download PDF] 
    @misc{https://doi.org/10.48550/arxiv.2212.10054,
doi = {10.48550/ARXIV.2212.10054},
url = {https://arxiv.org/abs/2212.10054},
author = {Illium, Steffen and Griffin, Gretchen and Kölle, Michael and Zorn, Maximilian and Nüßlein, Jonas and Linnhoff-Popien, Claudia},
keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {VoronoiPatches: Evaluating A New Data Augmentation Method},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}

  • S. Illium, M. Zorn, C. Lenta, M. Kölle, C. Linnhoff-Popien, and T. Gabor, Constructing Organism Networks from Collaborative Self-ReplicatorsarXiv, 2022. doi:10.48550/ARXIV.2212.10078
    [BibTeX] [Download PDF] 
    @misc{https://doi.org/10.48550/arxiv.2212.10078,
doi = {10.48550/ARXIV.2212.10078},
url = {https://arxiv.org/abs/2212.10078},
author = {Illium, Steffen and Zorn, Maximilian and Lenta, Cristian and Kölle, Michael and Linnhoff-Popien, Claudia and Gabor, Thomas},
keywords = {Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Constructing Organism Networks from Collaborative Self-Replicators},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}

  • T. Gabor, S. Illium, M. Zorn, C. Lenta, A. Mattausch, L. Belzner, and C. Linnhoff-Popien, „Self-Replication in Neural Networks,“ Artificial Life, pp. 205-223, 2022. doi:10.1162/artl_a_00359
    [BibTeX] [Abstract] [Download PDF]

    {A key element of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. Backpropagation turns out to be the natural way to navigate the space of network weights and allows non-trivial self-replicators to arise naturally. We perform an in-depth analysis to show the self-replicators’ robustness to noise. We then introduce artificial chemistry environments consisting of several neural networks and examine their emergent behavior. In extension to this works previous version (Gabor et al., 2019), we provide an extensive analysis of the occurrence of fixpoint weight configurations within the weight space and an approximation of their respective attractor basins.}

    @article{10.1162/artl_a_00359,
author = {Gabor, Thomas and Illium, Steffen and Zorn, Maximilian and Lenta, Cristian and Mattausch, Andy and Belzner, Lenz and Linnhoff-Popien, Claudia},
title = {{Self-Replication in Neural Networks}},
journal = {Artificial Life},
pages = {205-223},
year = {2022},
month = {06},
abstract = {{A key element of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. Backpropagation turns out to be the natural way to navigate the space of network weights and allows non-trivial self-replicators to arise naturally. We perform an in-depth analysis to show the self-replicators’ robustness to noise. We then introduce artificial chemistry environments consisting of several neural networks and examine their emergent behavior. In extension to this works previous version (Gabor et al., 2019), we provide an extensive analysis of the occurrence of fixpoint weight configurations within the weight space and an approximation of their respective attractor basins.}},
issn = {1064-5462},
doi = {10.1162/artl_a_00359},
url = {https://doi.org/10.1162/artl\_a\_00359},
eprint = {https://direct.mit.edu/artl/article-pdf/doi/10.1162/artl\_a\_00359/2030914/artl\_a\_00359.pdf}
}

  • M. Friedrich, S. Illium, P. Fayolle, and C. Linnhoff-Popien, „CSG Tree Extraction from 3D Point Clouds and Meshes Using a Hybrid Approach,“ in Computer Vision, Imaging and Computer Graphics Theory and Applications, K. Bouatouch, A. A. de Sousa, M. Chessa, A. Paljic, A. Kerren, C. Hurter, G. M. Farinella, P. Radeva, and J. Braz, Eds., Cham: Springer International Publishing, 2022, p. 53–79.
    [BibTeX] 
    @incollection{friedrichSpringer2022,
author = {Friedrich, Markus and Illium, Steffen and Fayolle, Pierre-Alain and Linnhoff-Popien, Claudia},
editor = {Bouatouch, Kadi and de Sousa, A. Augusto and Chessa, Manuela and Paljic, Alexis and Kerren, Andreas and Hurter, Christophe and Farinella, Giovanni Maria and Radeva, Petia and Braz, Jose},
title = {CSG Tree Extraction from 3D Point Clouds and Meshes Using a Hybrid Approach},
booktitle = {Computer Vision, Imaging and Computer Graphics Theory and Applications},
year = {2022},
publisher = {Springer International Publishing},
address = {Cham},
pages = {53--79},
isbn = {978-3-030-94893-1}
}

  • R. Müller, S. Illium, T. Phan, T. Haider, and C. Linnhoff-Popien, „Towards Anomaly Detection in Reinforcement Learning,“ in 21st Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022 Blue Sky Ideas), 2022.
    [BibTeX] 
    @inproceedings{mueller2022ad_bluesky,
author = {Robert Müller and Steffen Illium and Thomy Phan and Tom Haider and Claudia Linnhoff-Popien},
title = {Towards Anomaly Detection in Reinforcement Learning},
booktitle = {21st Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022 Blue Sky Ideas)},
year = {2022},
organization = {International Foundation for Autonomous Agents and Multiagent Systems}
}

2021

  • T. Gabor, S. Illium, M. Zorn, and C. Linnhoff-Popien, Goals for Self-Replicating Neural Networks, 2021. doi:10.1162/isal_a_00439
    [BibTeX] [Download PDF] 
    @proceedings{10.1162/isal_a_00439,
author = {Gabor, Thomas and Illium, Steffen and Zorn, Maximilian and Linnhoff-Popien, Claudia},
title = {{Goals for Self-Replicating Neural Networks}},
volume = {ALIFE 2021: The 2021 Conference on Artificial Life},
series = {ALIFE 2021: The 2021 Conference on Artificial Life},
year = {2021},
month = {07},
doi = {10.1162/isal_a_00439},
url = {https://doi.org/10.1162/isal\_a\_00439},
note = {101}
}

  • S. Illium, R. Müller, A. Sedlmeier, and C. Popien, „Visual Transformers for Primates Classification and Covid Detection,“ in Proc. Interspeech 2021, 2021, p. 451–455. doi:10.21437/Interspeech.2021-273
    [BibTeX] 
    @inproceedings{illium21_interspeech,
author = {Steffen Illium and Robert Müller and Andreas Sedlmeier and Claudia-Linnhoff Popien},
title = {{Visual Transformers for Primates Classification and Covid Detection}},
year = 2021,
booktitle = {Proc. Interspeech 2021},
pages = {451--455},
doi = {10.21437/Interspeech.2021-273}
}

  • R. Müller, S. Illium, and C. Linnhoff-Popien, „A Deep and Recurrent Architecture for Primate Vocalization Classification,“ in Proc. Interspeech 2021, 2021, p. 461–465. doi:10.21437/Interspeech.2021-1274
    [BibTeX] 
    @inproceedings{muller21_interspeech,
author = {Robert Müller and Steffen Illium and Claudia Linnhoff-Popien},
title = {{A Deep and Recurrent Architecture for Primate Vocalization Classification}},
year = 2021,
booktitle = {Proc. Interspeech 2021},
pages = {461--465},
doi = {10.21437/Interspeech.2021-1274}
}

  • R. Müller, S. Illium, F. Ritz, T. Schröder, C. Platschek, J. Ochs, and C. Linnhoff-Popien., „Acoustic Leak Detection in Water Networks,“ in Proceedings of the 13th International Conference on Agents and Artificial Intelligence – Volume 2: ICAART,, 2021, pp. 306-313. doi:10.5220/0010295403060313
    [BibTeX] 
    @conference{icaart21acouleak,
author = {Robert Müller and Steffen Illium and Fabian Ritz and Tobias Schröder and Christian Platschek and Jörg Ochs and Claudia Linnhoff-Popien.},
title = {Acoustic Leak Detection in Water Networks},
booktitle = {Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year = {2021},
pages = {306-313},
publisher = {SciTePress},
organization = {INSTICC},
doi = {10.5220/0010295403060313},
isbn = {978-989-758-484-8}
}

  • R. Müller, F. Ritz, S. Illium, and C. Linnhoff-Popien., „Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning,“ in Proceedings of the 13th International Conference on Agents and Artificial Intelligence – Volume 2: ICAART,, 2021, pp. 49-56. doi:10.5220/0010185800490056
    [BibTeX] 
    @conference{icaart21acouano,
author = {Robert Müller and Fabian Ritz and Steffen Illium and Claudia Linnhoff-Popien.},
title = {Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning},
booktitle = {Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year = {2021},
pages = {49-56},
publisher = {SciTePress},
organization = {INSTICC},
doi = {10.5220/0010185800490056},
isbn = {978-989-758-484-8}
}

  • R. Müller, S. Illium, F. Ritz, and K. Schmid., „Analysis of Feature Representations for Anomalous Sound Detection,“ in Proceedings of the 13th International Conference on Agents and Artificial Intelligence – Volume 2: ICAART,, 2021, pp. 97-106. doi:10.5220/0010226800970106
    [BibTeX] 
    @conference{icaart21acoufeat,
author = {Robert Müller and Steffen Illium and Fabian Ritz and Kyrill Schmid.},
title = {Analysis of Feature Representations for Anomalous Sound Detection},
booktitle = {Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year = {2021},
pages = {97-106},
publisher = {SciTePress},
organization = {INSTICC},
doi = {10.5220/0010226800970106},
isbn = {978-989-758-484-8}
}

2020

  • S. Illium, R. Müller, A. Sedlmeier, and C. Linnhoff-Popien, „Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms,“ in Proc. Interspeech 2020, 2020, p. 2052–2056. doi:10.21437/Interspeech.2020-1692
    [BibTeX] [Download PDF] 
    @inproceedings{Illium2020,
author = {Steffen Illium and Robert Müller and Andreas Sedlmeier and Claudia Linnhoff-Popien},
title = {{Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms}},
year = 2020,
booktitle = {Proc. Interspeech 2020},
pages = {2052--2056},
doi = {10.21437/Interspeech.2020-1692},
url = {http://dx.doi.org/10.21437/Interspeech.2020-1692}
}

  • A. Sedlmeier, R. Müller, S. Illium, and C. Linnhoff-Popien, „Policy Entropy for Out-of-Distribution Classification,“ in Artificial Neural Networks and Machine Learning – ICANN 2020, Cham, 2020, p. 420–431.
    [BibTeX] [Abstract]

    One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained. Such situations could lead to potential safety risks when wrong predictions lead to the execution of harmful actions. In this work, we propose PEOC, a new policy entropy based out-of-distribution classifier that reliably detects unencountered states in deep reinforcement learning. It is based on using the entropy of an agent’s policy as the classification score of a one-class classifier. We evaluate our approach using a procedural environment generator. Results show that PEOC is highly competitive against state-of-the-art one-class classification algorithms on the evaluated environments. Furthermore, we present a structured process for benchmarking out-of-distribution classification in reinforcement learning.

    @inproceedings{sedlmeier2020peoc,
author = {Sedlmeier, Andreas
and Müller, Robert
and Illium, Steffen
and Linnhoff-Popien, Claudia},
editor = {Farka{\v{s}}, Igor
and Masulli, Paolo
and Wermter, Stefan},
title = {Policy Entropy for Out-of-Distribution Classification},
booktitle = {Artificial Neural Networks and Machine Learning -- ICANN 2020},
year = {2020},
publisher = {Springer International Publishing},
address = {Cham},
pages = {420--431},
abstract = {One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained. Such situations could lead to potential safety risks when wrong predictions lead to the execution of harmful actions. In this work, we propose PEOC, a new policy entropy based out-of-distribution classifier that reliably detects unencountered states in deep reinforcement learning. It is based on using the entropy of an agent's policy as the classification score of a one-class classifier. We evaluate our approach using a procedural environment generator. Results show that PEOC is highly competitive against state-of-the-art one-class classification algorithms on the evaluated environments. Furthermore, we present a structured process for benchmarking out-of-distribution classification in reinforcement learning.},
isbn = {978-3-030-61616-8}
}

  • S. Illium, P. A. Friese, R. Müller, and S. Feld, „What to do in the Meantime: A Service Coverage Analysis for Parked Autonomous Vehicles,“ AGILE: GIScience Series, vol. 1, p. 7, 2020. doi:10.5194/agile-giss-1-7-2020
    [BibTeX] [Download PDF] 
    @article{agile-giss-1-7-2020,
author = {Illium, S. and Friese, P. A. and M\"uller, R. and Feld, S.},
title = {What to do in the Meantime: A Service Coverage Analysis for Parked Autonomous Vehicles},
journal = {AGILE: GIScience Series},
volume = {1},
year = {2020},
pages = {7},
url = {https://agile-giss.copernicus.org/articles/1/7/2020/},
doi = {10.5194/agile-giss-1-7-2020}
}

  • M. Friedrich, S. Illium, P. Fayolle, and C. Linnhoff-Popien, „A Hybrid Approach for Segmenting and Fitting Solid Primitives to 3D Point Clouds,“ in Proceedings of the 15th International Conference on Computer Graphics Theory and Applications (GRAPP), 2020.
    [BibTeX] 
    @inproceedings{friedrich2020hybrid,
title = {A Hybrid Approach for Segmenting and Fitting Solid Primitives to 3D Point Clouds},
author = {Friedrich, Markus and Illium, Steffen and Fayolle, Pierre-Alain and Linnhoff-Popien, Claudia},
booktitle = {Proceedings of the 15th International Conference on Computer Graphics Theory and Applications (GRAPP)},
volume = {1},
year = {2020},
organization = {INSTICC},
publisher = {SCITEPRESS}
}

2019

  • D. Elsner, S. Langer, F. Ritz, R. Mueller, and S. Illium, „Deep Neural Baselines for Computational Paralinguistics,“ Proc. Interspeech 2019, p. 2388–2392, 2019.
    [BibTeX] 
    @article{elsner2019deep,
title = {Deep Neural Baselines for Computational Paralinguistics},
author = {Elsner, Daniel and Langer, Stefan and Ritz, Fabian and Mueller, Robert and Illium, Steffen},
journal = {Proc. Interspeech 2019},
pages = {2388--2392},
year = {2019}
}

  • R. Müller, S. Langer, F. Ritz, C. Roch, S. Illium, and C. Linnhoff-Popien, „Soccer Team Vectors,“ in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2019, p. 247–257.
    [BibTeX] 
    @inproceedings{mueller2019steve,
title = {Soccer Team Vectors},
author = {Müller, Robert and Langer, Stefan and Ritz, Fabian and Roch, Christoph and Illium, Steffen and Linnhoff-Popien, Claudia},
booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
pages = {247--257},
year = {2019},
organization = {Springer}
}

  • T. Gabor, S. Illium, A. Mattausch, L. Belzner, and C. Linnhoff-Popien, „Self-Replication in Neural Networks,“ in Conference on Artificial Life (ALIFE 2019), 2019.
    [BibTeX] 
    @inproceedings{gabor2019self-replication,
author = {Thomas Gabor and Steffen Illium and Andy Mattausch and Lenz Belzner and Claudia Linnhoff-Popien},
title = {Self-Replication in Neural Networks},
booktitle = {Conference on Artificial Life (ALIFE 2019)},
year = {2019},
owner = {tgabor}
}

2018

  • S. Illium, T. Gabor, and T. Phan, „Bayesian Variational Optimization in Sensor Networks,“ in 17. GI/ITG KuVS Fachgespräch Sensornetze 13. & 14. September 2018, Braunschweig: Technical Report, Braunschweig, 2018. doi:10.24355/dbbs.084-201809121401-1
    [BibTeX] [Download PDF] 
    @inproceedings{illium2018bayesian,
author = {Illium, Steffen and Gabor, Thomas and Phan, Thomy},
title = {Bayesian Variational Optimization in Sensor Networks},
booktitle = {17. GI/ITG KuVS Fachgespr{\"a}ch Sensornetze 13. \& 14. September 2018, Braunschweig: Technical Report},
year = {2018},
editor = {Wolf , Lars and B{\"u}sching, Felix},
address = {Braunschweig},
doi = {10.24355/dbbs.084-201809121401-1},
owner = {sillium},
url = {https://publikationsserver.tu-braunschweig.de/receive/dbbs_mods_00065986}
}

  • S. Feld, S. Illium, A. Sedlmeier, and L. Belzner, „Trajectory annotation using sequences of spatial perception,“ in 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’18), 2018, p. 1–10. doi:https://doi.org/10.1145/3274895.3274968
    [BibTeX] 
    @inproceedings{feld2018trajectory,
author = {Feld, Sebastian and Illium, Steffen and Sedlmeier, Andreas and Belzner, Lenz},
title = {Trajectory annotation using sequences of spatial perception},
booktitle = {26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’18)},
year = {2018},
pages = {1--10},
organization = {ACM},
doi = {https://doi.org/10.1145/3274895.3274968}
}