Gabriel Dax

E-Mail:	[email protected]
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]
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]
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, 2023, Bologna, Italy. https://doi.org/10.1145/3587135.3592769 [PDF]
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]
Dax, G., & Werner, M. (2022). The Role of Compression in Spatial Computing. PhD Colloquium of the Deutsche Geodätische Kommission, Section on Geoinformatics. [PDF]
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]
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]
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]
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.
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]
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]
Götzer, S., Laass, M., Dax, G., & Werner, M. (2021). ObservaToriUM: A Simple Scalable Earth Observation Processing Engine. Symposium Für Angewandte Geoinformatik (AGIT’2021). [PDF]
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]
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]
Dax, G., & Werner, M. (2021). Trajectory Similarity using Compression. Proceedings of the 22nd IEEE International Conference on Mobile Data Management (MDM’2021). [PDF]
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]
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]
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]

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]
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]