Gabriel Dax

E-Mail:	gabriel.dax@tum.de
Phone:	555 51
Personal Website:	g4br1el.github.io
Room:	9377.01.113
Address:	Technische Universität München Gabriel Dax Lise-Meitner-Str. 9 85521 Ottobrunn

Research Interests

(Spatial) Deep Learning
Machine Learning and Data Mining
Deep Learning on Custom Hardware
Probabilistic Data Structures
Data Compression
Geospatial Data

Projects

Commercial off-the-shelf Inference Processor ML Benchmark (MLAB): This project is funded by the European Space Agency (ESA) and is done together with the partners Airbus, TUM CAPS and OroraTech. This project is about applying and benchmarking deep learning models on FPGAs in the field of remote sensing using TensorFlow and VitisAI.

Teaching

Winter Semester 2022:

Computational Foundations 1, Exercise, Technical University of Munich
Selected Topics in Big Geospaial Data, Lecture and Seminar, Technical University of Munich

Summer Semester 2022

Computational Foundations 2, Exercise, Technical University of Munich
Big Geospaial Data, Exercise, Technical University of Munich
Selected Topics in Big Geospaial Data, Lecture and Seminar, Technical University of Munich

Winter Semester 2021

Computational Foundations 1, Exercise, Technical University of Munich
Principles of Spatial Data Mining and Machine Learning, Exercise, Technical University of Munich
Selected Topics in Big Geospaial Data, Seminar and Seminar, Technical University of Munich

Summer Semester 2021

Big Geospaial Data, Exercise, Technical University of Munich
Selected Topics in Big Geospaial Data, Seminar, Technical University of Munich

Winter Semester 2020

Principles of Spatial Data Mining and Machine Learning, Exercise, Technical University of Munich
Selected Topics in Big Geospaial Data, Seminar, Technical University of Munich

Spring Term 2020

Grundzüge der Geoinformatik (Principles of Geoinformatics), Exercise, Bundeswehr University Munich

Winter Term 2020

Big Geospatial Data, Exercise, Bundeswehr University Munich
Spatial Data Science and Moving Objects, Exercise, Bundeswehr University Munich

Publications

Li, H., Yuan, Z., Dax, G., Kong, G., Fan, H., Zipf, A., & Werner, M. (2023). Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation. In R. Beecham, J. A. Long, D. Smith, Q. Zhao, & S. Wise (Eds.), 12th International Conference on Geographic Information Science (GIScience 2023) (Vol. 277, pp. 7:1–7:15). Schloss Dagstuhl – Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.GIScience.2023.7 [PDF] [Online] [BibTeX]

@inproceedings{2023_semisupervised_Hao,
  author = {Li, Hao and Yuan, Zhendong and Dax, Gabriel and Kong, Gefei and Fan, Hongchao and Zipf, Alexander and Werner, Martin},
  title = {{Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation}},
  booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
  pages = {7:1--7:15},
  series = {Leibniz International Proceedings in Informatics (LIPIcs)},
  isbn = {978-3-95977-288-4},
  issn = {1868-8969},
  year = {2023},
  volume = {277},
  editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address = {Dagstuhl, Germany},
  url = {https://drops.dagstuhl.de/opus/volltexte/2023/18902},
  urn = {urn:nbn:de:0030-drops-189028},
  doi = {10.4230/LIPIcs.GIScience.2023.7},
  annote = {Keywords: OpenStreetMap, Street-view Images, VGI, GeoAI, 3D city model, Facade parsing}
}

Koch, A., Dax, G., Petry, M., Gomez, H., Raoofy, A., Saroliya, U., Ghiglione, M., Furano, G., Werner, M., Trinitis, C., & Langer, M. (2023). Reference Implementations for Machine Learning Application Benchmark. 2023 European Data Handling & Data Processing Conference (EDHPC), 1–3. https://doi.org/10.23919/EDHPC59100.2023.10396582 [PDF] [Online] [BibTeX]

@article{2023_MLAB2_Andreas,
  author = {Koch, Andreas and Dax, Gabriel and Petry, Michael and Gomez, Harvey and Raoofy, Amir and Saroliya, Urvij and Ghiglione, Max and Furano, Gianluca and Werner, Martin and Trinitis, Carsten and Langer, Martin},
  title = {Reference Implementations for Machine Learning Application Benchmark},
  booktitle = {2023 European Data Handling & Data Processing Conference (EDHPC)},
  isbn = {978-9-09-037924-1},
  doi = {10.23919/EDHPC59100.2023.10396582},
  url = {https://doi.org/10.23919/EDHPC59100.2023.10396582},
  year = {2023},
  month = oct,
  keywords = {Earth;Satellites;Publishing;Machine learning;Benchmark testing;Hardware;Telemetry;Machine learning;neural networks;benchmark;FPGA;datasets;power consumption},
  pages = {1-3},
  location = {Juan Les Pins, France},
  project = {mlab}
}

Koch, A., Petry, M., Ghiglione, M., Raoofy, A., Dax, G., Furano, G., Werner, M., Trinitis, C., & Langer, M. (2023). Machine Learning Application Benchmark. 20th ACM International Conference on Computing Frontiers (CF ’23), May 9–11, 2023, Bologna, Italy. https://doi.org/10.1145/3587135.3592769 [PDF] [BibTeX]
```
@article{2023_MLAB_Andreas,
  author = {Koch, Andreas and Petry, Michael and Ghiglione, Max and Raoofy, Amir and Dax, Gabriel and Furano, Gianluca and Werner, Martin and Trinitis, Carsten and Langer, Martin},
  title = {Machine Learning Application Benchmark},
  doi = {10.1145/3587135.3592769},
  isbn = {979-8-4007-0140-5/23/05},
  booktitle = {20th ACM International Conference on Computing Frontiers (CF
  '23), May 9--11, 2023, Bologna, Italy},
  publisher = {ACM},
  year = {2023},
  month = may,
  project = {mlab}
}
```
Dax, G., Nagarajan, S., Li, H., & Werner, M. (2022). Compression Supports Spatial Deep Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS). https://doi.org/10.1109/JSTARS.2022.3226563 [Online] [BibTeX]
```
@article{2022_CompInDL_Dax,
  title = {Compression Supports Spatial Deep Learning},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)},
  author = {Dax, Gabriel and Nagarajan, Srilakshmi and Li, Hao and Werner, Martin},
  url = {https://doi.org/10.1109/JSTARS.2022.3226563},
  year = {2022},
  project = {mlab}
}
```
Dax, G., & Werner, M. (2022). The Role of Compression in Spatial Computing. PhD Colloquium of the Deutsche Geodätische Kommission, Section on Geoinformatics. [PDF] [BibTeX]
```
@inproceedings{2022_phdColoquiumDGK_Dax,
  author = {Dax, Gabriel and Werner, Martin},
  title = {The Role of Compression in Spatial Computing},
  project = {mlab},
  booktitle = {PhD Colloquium of the Deutsche Geodätische Kommission, Section on Geoinformatics},
  year = {2022}
}
```
Denizoglu, D. G., Dax, G., Nagarajan, S., Zhang, N., & Werner, M. (2022). Global Active Fire Detection – Towards a SAR-enabled Multi-Sensor Global Monitoring System. Living Planet Symposium 2022. [PDF] [BibTeX]
```
@inproceedings{2022_wildfireS2_Gaye,
  author = {Denizoglu, Deniz Gaye and Dax, Gabriel and Nagarajan, Srilakshmi and Zhang, Ning and Werner, Martin},
  title = {Global Active Fire Detection – Towards a SAR-enabled Multi-Sensor Global Monitoring System},
  project = {mlab},
  booktitle = {Living Planet Symposium 2022},
  year = {2022}
}
```

Raoofy, A., Dax, G., Serra, V., Ghiglione, M., Werner, M., & Trinitis, C. (2022). Benchmarking and Feasibility Aspects of Machine Learning in Space Systems. Proceedings of the 19th ACM International Conference on Computing Frontiers (CF’22). https://doi.org/10.1145/3528416.3530986 [PDF] [Online] [BibTeX]

@inproceedings{2022_MLBenchmark_Raoofy,
  author = {Raoofy, Amir and Dax, Gabriel and Serra, Vittorio and Ghiglione, Max and Werner, Martin and Trinitis, Carsten},
  title = {Benchmarking and Feasibility Aspects of Machine Learning in Space Systems},
  year = {2022},
  isbn = {9781450393386},
  publisher = {Association for Computing Machinery (ACM)},
  address = {New York, NY, USA},
  project = {mlab},
  url = {https://doi.org/10.1145/3528416.3530986},
  doi = {10.1145/3528416.3530986},
  booktitle = {Proceedings of the 19th ACM International Conference on Computing Frontiers (CF'22)}
}

Ghiglione, M., Serra, V., Raoofy, A., Dax, G., Trinitis, C., Werner, M., Schulz, M., & Furano, G. (2022). Survey of frameworks for inference of neural networks in space data system. Data Systems in Aerospace (DASIA). Eurospace. [PDF] [BibTeX]
```
@article{2022_cnnInSpace_Ghiglione,
  author = {Ghiglione, Max and Serra, Vittorio and Raoofy, Amir and Dax, Gabriel and Trinitis, Carsten and Werner, Martin and Schulz, Martin and Furano, Gianluca},
  title = {{Survey of frameworks for inference of neural networks in space data system}},
  journal = {Data Systems in Aerospace (DASIA). Eurospace},
  project = {mlab},
  year = {2022}
}
```
Laass, M., Dax, G., & Werner, M. (2022). A Randomized Data Structure for Point Clouds. Proceedings of the 7th Annual SDSC Conference and 17th 3D GeoInfo Conference. [BibTeX]
```
@article{2022_Globimaps3d_Werner,
  title = {A Randomized Data Structure for Point Clouds},
  author = {Laass, Moritz and Dax, Gabriel and Werner, Martin},
  journal = {Proceedings of the 7th Annual SDSC Conference and 17th 3D GeoInfo Conference},
  year = {2022}
}
```
Ghiglione, M., Raoofy, A., Dax, G., Furano, G., Wiest, R., Trinitis, C., Werner, M., Schulz, M., & Langer, M. (2021). Machine Learning Application Benchmark for In-Orbit On-Board Data Processing. European Workshop on On-Board Data Processing. https://zenodo.org/record/5520877/files/05.04_OBDP2021_Ghiglione.pdf [PDF] [Online] [BibTeX]
```
@inproceedings{2021_MachineLearningApplicationBenchmark_Ghiglione,
  title = {Machine Learning Application Benchmark for In-Orbit On-Board Data Processing},
  author = {Ghiglione, Max and Raoofy, Amir and Dax, Gabriel and Furano, Gianluca and Wiest, Richard and Trinitis, Carsten and Werner, Martin and Schulz, Martin and Langer, Martin},
  booktitle = {European Workshop on On-Board Data Processing},
  year = {2021},
  project = {mlab},
  url = {https://zenodo.org/record/5520877/files/05.04_OBDP2021_Ghiglione.pdf}
}
```
Alam, S., Ahmed, M., Dax, G., & Werner, M. (2021). Change detection of Lake Starnberg, Germany using NDVI and Sentinel 2. Symposium Für Angewandte Geoinformatik (AGIT’2021). [PDF] [BibTeX]
```
@inproceedings{2021_changedetectionagit_Shahidul,
  booktitle = {Symposium für Angewandte Geoinformatik (AGIT'2021)},
  title = {Change detection of Lake Starnberg, Germany using NDVI and Sentinel 2},
  author = {Alam, Shahidul and Ahmed, Mueed and Dax, Gabriel and Werner, Martin},
  year = {2021}
}
```
Götzer, S., Laass, M., Dax, G., & Werner, M. (2021). ObservaToriUM: A Simple Scalable Earth Observation Processing Engine. Symposium Für Angewandte Geoinformatik (AGIT’2021). [PDF] [BibTeX]
```
@inproceedings{2021_observatoriumagit_Goetzer,
  booktitle = {Symposium für Angewandte Geoinformatik (AGIT'2021)},
  title = {ObservaToriUM: A Simple Scalable Earth Observation Processing Engine},
  author = {Götzer, Stephan and Laass, Moritz and Dax, Gabriel and Werner, Martin},
  year = {2021}
}
```
Dax, G., & Werner, M. (2021). Information-optimal Abstaining for Reliable Classification of Building Functions. Proceedings of the 24th AGILE Conference on Geographic Information Science (AGILE’2021). [PDF] [BibTeX]
```
@inproceedings{2021_abstain_Dax,
  booktitle = {Proceedings of the 24th AGILE Conference on Geographic Information Science (AGILE'2021)},
  title = {Information-optimal Abstaining for Reliable Classification of Building Functions},
  author = {Dax, Gabriel and Werner, Martin},
  online = {https://agile-giss.copernicus.org/articles/2/1/2021/},
  year = {2021}
}
```
Zeya, S. M., Theofanidis, A., Dax, G., & Werner, M. (2021). Forest and Vegetation Monitoring using Sentinel-2 Imagery in the Northern Part of Democratic Republic of Congo. Proceedings of the 24th AGILE Conference on Geographic Information Science (AGILE’2021). [PDF] [BibTeX]
```
@inproceedings{2021_forestmonitoringagile_Zeya,
  booktitle = {Proceedings of the 24th AGILE Conference on Geographic Information Science (AGILE'2021)},
  title = {Forest and Vegetation Monitoring using Sentinel-2 Imagery in the Northern Part of Democratic Republic of Congo},
  author = {Zeya, Syed Miftah and Theofanidis, Alexandros and Dax, Gabriel and Werner, Martin},
  year = {2021}
}
```
Dax, G., & Werner, M. (2021). Trajectory Similarity using Compression. Proceedings of the 22nd IEEE International Conference on Mobile Data Management (MDM’2021). [PDF] [BibTeX]
```
@inproceedings{2021_MDMCompressionDistance_Dax,
  booktitle = {Proceedings of the 22nd IEEE International Conference on Mobile Data Management (MDM'2021)},
  title = {Trajectory Similarity using Compression},
  author = {Dax, Gabriel and Werner, Martin},
  year = {2021}
}
```
Raoofy, A., Dax, G., Ghiglione, M., Langer, M., Trinitis, C., Werner, M., & Schulz, M. (2021). Benchmarking Machine Learning Inference in FPGA-based Accelerated Space Applications. Proceedings of the Workshop on Benchmarking Machine Learning Workloads Co-Located with IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). [PDF] [BibTeX]
```
@inproceedings{2021_fpgabenchmarkpositionpaper_Raoofy,
  booktitle = {Proceedings of the Workshop on Benchmarking Machine Learning Workloads co-located with IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)},
  title = {Benchmarking Machine Learning Inference in FPGA-based Accelerated Space Applications},
  author = {Raoofy, Amir and Dax, Gabriel and Ghiglione, Max and Langer, Martin and Trinitis, Carsten and Werner, Martin and Schulz, Martin},
  project = {mlab},
  year = {2021}
}
```
Dax, G., Laass, M., & Werner, M. (2021). Genetic Algorithm for Improved Transfer Learning Through Bagging Color-Adjusted Models. International Geoscience and Remote Sensing Symposium (IGARSS’21). [PDF] [BibTeX]
```
@inproceedings{2021_IGARSSGeneticTransferLearning_Dax,
  booktitle = {International Geoscience and Remote Sensing Symposium (IGARSS'21)},
  title = {Genetic Algorithm for Improved Transfer Learning Through Bagging Color-Adjusted Models},
  author = {Dax, Gabriel and Laass, Moritz and Werner, Martin},
  year = {2021}
}
```

Werner, M., Dax, G., & Laass, M. (2020). Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research. In R. H. Reussner, A. Koziolek, & R. Heinrich (Eds.), 50. Jahrestagung der Gesellschaft für Informatik, INFORMATIK 2020 - Back to the Future, Karlsruhe, Germany, 28. September - 2. Oktober 2020: Vol. P-307 (pp. 1009–1017). GI. https://doi.org/10.18420/inf2020_95 [Online] [BibTeX]

@inproceedings{2021_GIChallenges_Werner,
  author = {Werner, Martin and Dax, Gabriel and Laass, Moritz},
  editor = {Reussner, Ralf H. and Koziolek, Anne and Heinrich, Robert},
  title = {Computational Challenges for Artificial Intelligence and Machine Learning
                 in Environmental Research},
  booktitle = {50. Jahrestagung der Gesellschaft f{\"{u}}r Informatik, {INFORMATIK}
                 2020 - Back to the Future, Karlsruhe, Germany, 28. September - 2.
                 Oktober 2020},
  series = {{LNI}},
  volume = {{P-307}},
  pages = {1009--1017},
  publisher = {{GI}},
  year = {2020},
  url = {https://doi.org/10.18420/inf2020\_95},
  doi = {10.18420/inf2020\_95},
  timestamp = {Thu, 11 Feb 2021 09:01:11 +0100},
  biburl = {https://dblp.org/rec/conf/gi/WernerDL20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Other Publications

Dumitru, C. O., Schwarz, G., Dax, G., Vlad, A., Ao, D., & Datcu, M. (2020). Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches. In H. R. Arabnia, K. Daimi, R. Stahlbock, C. Soviany, L. Heilig, & K. Brussau (Eds.), Principles of Data Science (pp. 207–231). Springer Nature Switzerland AG. https://elib.dlr.de/138139/ [Online] [BibTeX]

@incollection{dlr138139,
  title = {Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches},
  pages = {207--231},
  publisher = {Springer Nature Switzerland AG},
  editor = {Arabnia, H. R. and Daimi, K. and Stahlbock, R. and Soviany, C. and Heilig, L. and Brussau, K.},
  journal = {Principles of Data Science},
  series = {Transactions on Computational Science and Computational Intelligence},
  author = {Dumitru, Corneliu Octavian and Schwarz, Gottfried and Dax, Gabriel and Vlad, Andrei and Ao, Dongyang and Datcu, Mihai},
  year = {2020},
  url = {https://elib.dlr.de/138139/},
  keywords = {machine learning, coastline detection, icebergs, sea-ice}
}

Dumitru, C. O., Schwarz, G., Ao, D., Dax, G., Karmakar, C., & Datcu, M. (2020). Selection of Reliable Machine Learning Algorithms for Geophysical Applications. EGU 2020. https://elib.dlr.de/138129/ [Online] [BibTeX]
```
@inproceedings{dlr138129,
  author = {Dumitru, Corneliu Octavian and Schwarz, Gottfried and Ao, Dongyang and Dax, Gabriel and Karmakar, Chandrabali and Datcu, Mihai},
  title = {Selection of Reliable Machine Learning Algorithms for Geophysical Applications},
  booktitle = {EGU 2020},
  year = {2020},
  month = {Mai},
  keywords = {Machine learning, applications},
  url = {https://elib.dlr.de/138129/}
}
```