Publications

[1] G. Dax, C. O. Dumitru, G. Schwarz, and M. Datcu, “SAR Change Detection in a General Case Using Normalized Compression Distance,” in TerraSAR-X Science Team Meeting 2019, 2019. Available at: https://elib.dlr.de/130271/

URL BibTeX

@inproceedings{dlr130271,
  title = {SAR Change Detection in a General Case Using Normalized Compression Distance},
  author = {Dax, Gabriel and Dumitru, Corneliu Octavian and Schwarz, Gottfried and Datcu, Mihai},
  booktitle = {TerraSAR-X Science Team Meeting 2019},
  year = {2019},
  month = {Oktober},
  url = {https://elib.dlr.de/130271/},
  keywords = {Synthetic Aparture Radar, Normalized Compression Distance,Change Detection}
}

[2] M. Coca, M. Datcu, G. Dax, C. O. Dumitru, G. Schwarz, and W. Yao, “No Feature Data Analytics: Compression Pattern Recognition,” in Φ-week, Sept. 2019. Available at: https://elib.dlr.de/130276/

URL BibTeX

@inproceedings{dlr130276,
  year = {2019},
  booktitle = {{{\Phi}}-week},
  month = sep,
  title = {No Feature Data Analytics: Compression Pattern Recognition},
  author = {Coca, Mihai and Datcu, Mihai and Dax, Gabriel and Dumitru, Corneliu Octavian and Schwarz, Gottfried and Yao, Wei},
  url = {https://elib.dlr.de/130276/},
  keywords = {Data Analytics, No Feature, Pattern Recognition}
}

[3] C. O. Dumitru, G. Dax, G. Schwarz, C. Cazacu, M. C. Adamescu, and M. Datcu, “Accurate Monitoring of the Danube Delta Dynamics using Copernicus Data,” in SPIE Remote Sensing, 2019, pp. 1–13. Available at: https://elib.dlr.de/129121/

URL BibTeX

@inproceedings{dlr129121,
  author = {Dumitru, Corneliu Octavian and Dax, Gabriel and Schwarz, Gottfried and Cazacu, Constantin and Adamescu, Mihai Cristian and Datcu, Mihai},
  pages = {1--13},
  title = {Accurate Monitoring of the Danube Delta Dynamics using Copernicus Data},
  year = {2019},
  booktitle = {SPIE Remote Sensing},
  url = {https://elib.dlr.de/129121/},
  keywords = {Classification, change detection, Copernicus satellite data, coastline detection, Danube Delta, normalized compression distance, Sentinel-1, Sentinel-2.}
}

[4] M. Werner, G. Dax, and M. Laass, “Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research,” in INFORMATIK 2020, 2020. doi: 10.18420/inf2020_95
DOI BibTeX
```
@inproceedings{Werner2020,
  author = {Werner, Martin and Dax, Gabriel and Laass, Moritz},
  title = {Computational Challenges for Artificial Intelligence and Machine Learning in Environmental Research},
  booktitle = {INFORMATIK 2020},
  year = {2020},
  doi = {10.18420/inf2020_95}
}
```

[5] C. O. Dumitru, G. Schwarz, D. Ao, G. Dax, C. Karmakar, and M. Datcu, “Selection of Reliable Machine Learning Algorithms for Geophysical Applications,” in EGU 2020, 2020. Available at: https://elib.dlr.de/138129/

URL 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/}
}

[6] C. O. Dumitru, G. Schwarz, G. Dax, A. Vlad, D. Ao, and M. Datcu, “Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches,” in Principles of Data Science, H. R. Arabnia, K. Daimi, R. Stahlbock, C. Soviany, L. Heilig, and K. Brussau, Eds., in Transactions on Computational Science and Computational Intelligence. Springer Nature Switzerland AG, 2020, pp. 207–231. Available at: https://elib.dlr.de/138139/

URL 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}
}

[7] S. Alam, M. Ahmed, G. Dax, and M. Werner, “Change detection of Lake Starnberg, Germany using NDVI and Sentinel 2,” in Symposium für Angewandte Geoinformatik (AGIT’2021), 2021.
BibTeX
```
@inproceedings{Alam2021,
  author = {Alam, Shahidul and Ahmed, Mueed and Dax, Gabriel and Werner, Martin},
  title = {Change detection of Lake Starnberg, Germany using NDVI and Sentinel 2},
  booktitle = {Symposium f{\"u}r Angewandte Geoinformatik (AGIT'2021)},
  year = {2021}
}
```
[8] G. Dax, M. Laass, and M. Werner, “Genetic Algorithm for Improved Transfer Learning Through Bagging Color-Adjusted Models,” in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, IEEE, 2021, pp. 2612–2615. doi: 10.1109/IGARSS47720.2021.9554380
DOI BibTeX
```
@inproceedings{Dax2021,
  author = {Dax, Gabriel and Laass, Moritz and Werner, Martin},
  title = {Genetic Algorithm for Improved Transfer Learning Through Bagging Color-Adjusted Models},
  pages = {2612--2615},
  publisher = {IEEE},
  isbn = {978-1-6654-0369-6},
  booktitle = {2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},
  year = {2021},
  doi = {10.1109/IGARSS47720.2021.9554380}
}
```
[9] G. Dax and M. Werner, “Information-optimal Abstaining for Reliable Classification of Building Functions,” AGILE: GIScience Series, vol. 2, pp. 1–10, 2021, doi: 10.5194/agile-giss-2-1-2021
DOI BibTeX
```
@article{Dax2021b,
  author = {Dax, Gabriel and Werner, Martin},
  year = {2021},
  title = {Information-optimal Abstaining for Reliable Classification of Building Functions},
  pages = {1--10},
  volume = {2},
  journal = {AGILE: GIScience Series},
  doi = {10.5194/agile-giss-2-1-2021}
}
```
[10] G. Dax and M. Werner, “Trajectory Similarity using Compression,” in 2021 22nd IEEE International Conference on Mobile Data Management (MDM), IEEE, 2021, pp. 169–174. doi: 10.1109/MDM52706.2021.00035
DOI BibTeX
```
@inproceedings{Dax2021c,
  author = {Dax, Gabriel and Werner, Martin},
  title = {Trajectory Similarity using Compression},
  pages = {169--174},
  publisher = {IEEE},
  isbn = {978-1-6654-2845-3},
  booktitle = {2021 22nd IEEE International Conference on Mobile Data Management (MDM)},
  year = {2021},
  doi = {10.1109/MDM52706.2021.00035}
}
```

[11] M. Ghiglione et al., “Machine Learning Application Benchmark for In-Orbot On-Board Data Processing,” in European Workshop on On-Board Data Processing, 2021. doi: 10.5281/zenodo.5520877

DOI BibTeX

@incollection{Ghiglione2021,
  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},
  title = {Machine Learning Application Benchmark for In-Orbot On-Board Data Processing},
  booktitle = {European Workshop on On-Board Data Processing},
  year = {2021},
  doi = {10.5281/zenodo.5520877}
}

[12] S. Götzer, M. Laass, G. Dax, and M. Werner, “ObservaToriUM: A Simple Scalable Earth Observation Processing Engine,” in Symposium für Angewandte Geoinformatik (AGIT’2021), 2021.
BibTeX
```
@inproceedings{Gotzer2021,
  author = {G{\"o}tzer, Stephan and Laass, Moritz and Dax, Gabriel and Werner, Martin},
  title = {ObservaToriUM: A Simple Scalable Earth Observation Processing Engine},
  booktitle = {Symposium f{\"u}r Angewandte Geoinformatik (AGIT'2021)},
  year = {2021}
}
```

[13] A. Raoofy et al., “Benchmarking Machine Learning Inference in FPGA-based Accelerated Space Applications,” in Workshop on Benchmarking Machine Learning Workloads on Emerging Hardware, 2021.

BibTeX

@inproceedings{Raoofy2021,
  author = {Raoofy, Amir and Dax, Gabriel and Ghiglione, Max and Langer, Martin and Trinitis, Carsten and Werner, Martin and Schulz, Martin},
  title = {Benchmarking Machine Learning Inference in FPGA-based Accelerated Space Applications},
  booktitle = {Workshop on Benchmarking Machine Learning Workloads on Emerging Hardware},
  year = {2021}
}

[14] S. M. Zeya, A. Theofanidis, G. Dax, and M. Werner, “Forest and Vegetation Monitoring Using Sentinel-2 Imagery in the Northern Part of Democratic Republic of Congo,” in Proceedings of the 24th AGILE Conference on Geographic Information Science (AGILE’2021), 2021.
BibTeX
```
@inproceedings{Zeya2021,
  author = {Zeya, Syed Miftah and Theofanidis, Alexandros and Dax, Gabriel and Werner, Martin},
  title = {Forest and Vegetation Monitoring Using Sentinel-2 Imagery in the Northern Part of Democratic Republic of Congo},
  booktitle = {Proceedings of the 24th AGILE Conference on Geographic Information Science (AGILE'2021)},
  year = {2021}
}
```

[15] G. Dax and M. Werner, “The Role of Compression in Spatial Computing,” in PhD Colloquium of the DGK Section on Geoinformatics 2022, Braunschweig, 2022.

BibTeX

@inproceedings{Dax2022,
  author = {Dax, Gabriel and Werner, Martin},
  title = {The Role of Compression in Spatial Computing},
  online = {https://www.geoinfo.uni-bonn.de/DGKGeoinfo2022/pdf-dokumente/09_DGKGeoinfo2022_DAX-WERNER_paper_2684.pdf},
  keywords = {Bloom filter;Compression;Deep Learning;Spatial Computing},
  booktitle = {PhD Colloquium of the DGK Section on Geoinformatics 2022},
  year = {2022},
  address = {Braunschweig}
}

[16] D. G. Denizoglu, G. Dax, S. Nagarajan, N. Zhang, and M. Werner, “Global Active Fire Detection – Towards a SAR-enabled Multi-Sensor Global Monitoring System,” in Living Planet Symposium 2022, 2022.
BibTeX
```
@inproceedings{Denizoglu2022,
  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},
  booktitle = {Living Planet Symposium 2022},
  year = {2022}
}
```

[17] M. Ghiglione et al., “Survey of frameworks for inference of neural networks in space data systems,” in DASIA 2022, 2022.

BibTeX

@inproceedings{Ghiglione2022,
  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 systems},
  booktitle = {DASIA 2022},
  year = {2022}
}

[18] A. Raoofy, G. Dax, V. Serra, M. Ghiglione, M. Werner, and C. Trinitis, “Benchmarking and feasibility aspects of machine learning in space systems,” in Proceedings of the 19th ACM International Conference on Computing Frontiers, New York, NY, USA: ACM, 2022, pp. 225–226. doi: 10.1145/3528416.3530986

DOI BibTeX

@inproceedings{Raoofy2022,
  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},
  pages = {225--226},
  publisher = {ACM},
  isbn = {9781450393386},
  booktitle = {Proceedings of the 19th ACM International Conference on Computing Frontiers},
  year = {2022},
  address = {New York, NY, USA},
  doi = {10.1145/3528416.3530986}
}

[19] M. Laass, G. Dax, and M. Werner, “A Randomized Data Structure for Point Clouds,” in Proceedings of the 7th Annual SDSC Conference and 17th 3D GeoInfo Conference, 2022.
BibTeX
```
@inproceedings{laass2022,
  author = {Laass, Moritz and Dax, Gabriel and Werner, Martin},
  title = {A Randomized Data Structure for Point Clouds},
  year = {2022},
  booktitle = {Proceedings of the 7th Annual SDSC Conference and 17th 3D GeoInfo Conference}
}
```
[20] G. Dax, S. Nagarajan, H. Li, and M. Werner, “Compression Supports Spatial Deep Learning,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 702–713, 2023, doi: 10.1109/JSTARS.2022.3226563
DOI BibTeX
```
@article{Dax2022a,
  author = {Dax, Gabriel and Nagarajan, Srilakshmi and Li, Hao and Werner, Martin},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  title = {Compression Supports Spatial Deep Learning},
  year = {2023},
  volume = {16},
  number = {},
  pages = {702-713},
  doi = {10.1109/JSTARS.2022.3226563}
}
```

[21] H. Li et al., “Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation,” in 12th International Conference on Geographic Information Science (GIScience 2023), R. Beecham, J. A. Long, D. Smith, Q. Zhao, and S. Wise, Eds., in Leibniz International Proceedings in Informatics (LIPIcs), vol. 277. Dagstuhl, Germany: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2023, pp. 7:1–7:15. doi: 10.4230/LIPIcs.GIScience.2023.7

DOI BibTeX

@inproceedings{li_et_al:LIPIcs.GIScience.2023.7,
  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},
  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}
}

[22] A. Koch et al., “Reference Implementations for Machine Learning Application Benchmark,” in 2023 European Data Handling & Data Processing Conference (EDHPC), 2023, pp. 1–3. doi: 10.23919/EDHPC59100.2023.10396582

DOI BibTeX

@inproceedings{10396582,
  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},
  booktitle = {2023 European Data Handling & Data Processing Conference (EDHPC)},
  title = {Reference Implementations for Machine Learning Application Benchmark},
  year = {2023},
  volume = {},
  number = {},
  pages = {1-3},
  keywords = {Earth;Satellites;Publishing;Machine learning;Benchmark testing;Hardware;Telemetry;Machine learning;neural networks;benchmark;FPGA;datasets;power consumption},
  doi = {10.23919/EDHPC59100.2023.10396582}
}

[23] A. Koch et al., “Machine Learning Application Benchmark,” in Proceedings of the 20th ACM International Conference on Computing Frontiers, in CF ’23. New York, NY, USA: Association for Computing Machinery, 2023, pp. 229–235. doi: 10.1145/3587135.3592769

DOI BibTeX

@inproceedings{10.1145/3587135.3592769,
  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},
  year = {2023},
  isbn = {9798400701405},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3587135.3592769},
  booktitle = {Proceedings of the 20th ACM International Conference on Computing Frontiers},
  pages = {229–235},
  numpages = {7},
  keywords = {FPGA, Machine learning, benchmark, datasets, neural networks, power consumption},
  location = {Bologna, Italy},
  series = {CF '23}
}

[24] F. Karl, L. M. Kemeter, G. Dax, and P. Sierak, “Position: embracing negative results in machine learning,” in Proceedings of the 41st International Conference on Machine Learning, in ICML’24. JMLR.org, 2024. Available at: https://dl.acm.org/doi/10.5555/3692070.3693006

URL BibTeX

@inproceedings{10.5555/3692070.3693006,
  author = {Karl, Florian and Kemeter, Lukas Malte and Dax, Gabriel and Sierak, Paulina},
  title = {Position: embracing negative results in machine learning},
  year = {2024},
  publisher = {JMLR.org},
  booktitle = {Proceedings of the 41st International Conference on Machine Learning},
  articleno = {936},
  numpages = {10},
  location = {Vienna, Austria},
  series = {ICML'24},
  url = {https://dl.acm.org/doi/10.5555/3692070.3693006}
}