To foster our connections within the NFDI (Nationale Forschungsdateninfrastruktur) we took part in the “Data Challenge: Mobile Phone Data” by Berd@NFDI at Ludwig Maximilian Unviersity of Munich (LMU).

During this workshop we had the opportunity to discuss with practicioners from fields like economics and social sciences on how they use mobility data, and more explicitly mobile phone data to answer reasearch questions in their applciation domain. Interestingly, questions like privacy-restrictions and the evaluation of large datasets were the main concerns there and align exactly with issues identified in our research (Mokbel et al., 2024). Further, we especially contributed with discussing our idea on trajectory anonymization (compare (Walther et al., 2024)) and developed ideas for future coollaborations with other institutes.

Resources

1. Mokbel, M., Sakr, M., Xiong, L., Züfle, A., Almeida, J., Anderson, T., Aref, W., Andrienko, G., Andrienko, N., Cao, Y., Chawla, S., Cheng, R., Chrysanthis, P., Fei, X., Ghinita, G., Graser, A., Gunopulos, D., Jensen, C. S., KIM, J.-S., … Zimányi, E. (2024). Mobility Data Science: Perspectives and Challenges. ACM Trans. Spatial Algorithms Syst. https://doi.org/10.1145/3652158 [Online]
2. Walther, P., Luo, X., & Werner, M. (2024). TraBiMap: Reducing Privacy Concerns in Trajectory Analysis with Randomized Data Representations. 2nd ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies. https://doi.org/10.1145/3681768.3698496 [PDF] [Online]

On November 06, we have an open course offered to students from Aerospace and Geodesy, and more than 10 students joined both in-present and online. The topic is “Introduction Course to Python”, where basic Python programming language concepts (like syntax, variables, data types, operators, control flow, etc) were covered. Besides, we also introduced basic concepts of Spatial Data mining and Machine Learning with selected examples.

This EduHub course is offered regularly with free registration for both Bachelor and Master students to gain a first impression about Python programming.

For information about the future EduHub course, please refer to: Research Seminar

Link

• NFDI4Earth in TUM BGD
• NFDI4Earth - Educational Portal

End of October 2024, we were again invited to the respected ACM MULTIMEDIA conference to present our approach to estimating the location of a drone using only camera information.

We have been winning this challenge in 2023, already, and, thus, we are proud to win the second time in a row. Among all submissions, our current submission was ranked #1 with best location accuracy. Congratulations to the team and we are proud having demonstrated that TUM Aerospace and Geodesy is able to localize their drones precisely.

Details about the method of this year can be found in the upcoming publication.

On October 16, the room was packed with colleagues and students when Yomna Eid (ifgi, Uni Münster) presented us her latest research on how EO data down-sampling impact the quality and accuracy of analysis results, as well as lessons learned from the Spatial Data Science across Languages (SDSL) workshop. The talk was very interesting and the follow-up discussion was live and inspiring.

This guest talk is embedded into the ongoing EduHub activity and kicks off the Research Seminar series for Winter Term 2024/2025.

For information about the EduHub and upcoming events, please refer to: Research Seminar

Resources

• Poster of Yomna’s talk: Trends in Earth Observation and Spatial Data Science Languages

We are very happy to be such well represented at this year’s 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2024) and associated workshops in Atlanta, USA.

Research Papers

We are excited to present the following scientific papers:

1. Calculating Upstream Relation in Spatial Networks Under Path Constraints (Main Track)
   Authors: Wejdene Mansour, Martin Werner

2. SRL: Towards a General-Purpose Framework for Spatial Representation Learning (Vision Track) Authors: Gengchen Mai, Xiaobai Yao, Yiqun Xie, Jinmeng Rao, Hao Li, Qing Zhu, Ziyuan Li, Ni Lao

3. PC-LMT: The Point Cloud Log Merge Tree for the Helena Point Cloud Database (BigSpatial’24 Workshop)
   Authors: Balthasar Teuscher and Martin Werner

4. Random Affine Transformation Feature Representation Learning for Fast Polygon Retrieval (GeoSearch’24 Workshop)
   Authors: Zhangyu Wang, Xingyi Du, Hao Li and Martin Werner

5. TraBiMap: Reducing Privacy Concerns in Trajectory Analysis with Randomized Data Representations (GeoPrivacy’24 Workshop)
   Authors: Paul Walther, Xuanshu Luo and Martin Werner

Posters

Apart from this we also have a poster in the TSAS poster track:

1. Mobility Data Science: Perspectives and Challenges Authors: Mohamed Mokbel, Mahmoud Sakr, Li Xiong, Andreas Züfle, Jussara Almeida, Taylor Anderson, Walid Aref, Gennady Andrienko, Natalia Andrienko, Yang Cao, Sanjay Chawla, Reynold Cheng, Panos Chrysanthis, Xiqi Fei, Gabriel Ghinita, Anita Graser, Dimitrios Gunopulos, Christian S. Jensen, Joon-Seok Kim, Kyoung-Sook Kim, Peer Kröger, John Krumm, Johannes Lauer, Amr Magdy, Mario Nascimento, Siva Ravada, Matthias Renz, Dimitris Sacharidis, Flora Salim, Mohamed Sarwat, Maxime Schoemans, Cyrus Shahabi, Bettina Speckmann, Egemen Tanin, Xu Teng, Yannis Theodoridis, Kristian Torp, Goce Trajcevski, Marc van Kreveld, Carola Wenk, Martin Werner, Raymond Wong, Song Wu, Jianqiu Xu, Moustafa Youssef, Demetris Zeinalipour, Mengxuan Zhang, and Esteban Zimányi.

Workshops

We are delighted to co-host the:

3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data Workshop Co-Chairs from our group: Hao Li, Martin Werner Description: Engage with experts in the efficient searching and mining of large geospatial data collections, contributing to the development of cutting-edge solutions.

Additionally Martin Werner serves as a Poster/Demo Chair at the main conference.

For further information about our participation and research reach out to us on the conference. We look forward to sharing insights, learning, and contributing to the vibrant academic community at SIGSPATIAL ‘24. For detailed information on all our published papers refer to the publications page.

Our bachelor student, Gabor Fogarasi, deployed an FPGA-accelerated bird detector at Airport Oberpfaffenhofen for bird strike prevention based on his engineering project in WiSe 23/24, as part of Brandenburg / Bayern Action for AI Hardware (BB-KI Chips).

The underlying detection model was pre-trained by quantization-aware training to reduce the model size and increase inference speed using low-precision model parameters. Experiments show 50 times faster detection speed without significantly compromising accuracy than the uncompressed model. The massive parallelism of FPGA further ensures the efficiency of online inference.

Birds are monitored by an IP camera connected to the FPGA board running the detection model in the backend at approx. 2 FPS for 4K inputs. A 3D-printed detector container is also designed for essential waterproofing and heat sinking. The gif shows some birds detected at the scene.

Our group just had a workshop in Raitenhaslach, which took place from April 8th to 9th. During these two days, we joined forces with Katharina Anders’ Group for “Remote Sensing Applications” to work on two topics:

Assess the working progress of all different projects and dissertations and develop new ideas for our research. For the first, all Ph.D. students presented their current status and results, while for the second part, we chose the interactive format of research speed dating. Both parts allowed learning about the different focus topics of all the group members and develop new ideas for innovative research approaches in the future.

Apart from the research-oriented program points, this event also left plenty of opportunities to connect with the other BGD fellows and grow together as a team. We walked together to the Marienberg church and enjoyed the summer-like weather in April. We are already looking forward to the next workshop!

In a significant achievement, we, the Professorship for Big Geospatial Data Management, have clinched the 2nd place in the 12th SIGSPATIAL Cup competition (GISCUP 2023). Our winning paper, “Exploring GeoAI Methods for Supraglacial Lake Mapping on Greenland Ice Sheet,” stands as a collaborative effort by our dedicated research team – Xuanshu Luo, Paul Walther, Wejdene Mansour, Balthasar Teuscher, Johann Maximilian Zollner, Hao Li, and Martin Werner (Luo et al., 2023).

GISCUP, conducted alongside the 2023 ACM SIGSPATIAL conference, is an annual contest hosted by SIGSPATIAL to foster innovation in geospatial research. The official announcement of the winners took place at the ACM SIGSPATIAL conference in November 2023.

The focus of this year’s challenge was on the auto-identification of supraglacial lakes on the Greenland ice sheet from satellite imagery. Our goal was to develop an automated system capable of tagging these lakes as polygons from a single image, aiding in the tracking of their behavior across multiple summer melt seasons. Our winning paper showcases the practical application of Computer Vision Models to address this specific challenge. Additionally we investigated the potentials of the new category of large foundation models, namely the Segment Anything Model (SAM), in this field of research.

The SIGSPATIAL Cup win also brings with it a Travel Grant to the 2023 ACM SIGSPATIAL conference, providing us with the opportunity to present our work and engage with experts in the geospatial community. Such, our success in the GISCUP reinforces our standing as contributors to practical and innovative geospatial research.

If you are interested in our research, also take a look at the corresponding Github Repository.

Resources

1. Luo, X., Walther, P., Mansour, W., Teuscher, B., Zollner, J. M., Li, H., & Werner, M. (2023). Exploring GeoAI Methods for Supraglacial Lake Mapping on Greenland Ice Sheet. The 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’23), November 13–16, 2023, Hamburg, Germany. https://doi.org/10.1145/3589132.3629971 [PDF] [Online]

Our professorship is thrilled to announce its active participation in the 31st International Conference on Advances in Geographic Information Systems (SIGSPATIAL ‘23), taking place in Hamburg, Germany. We are co-organizing the conference, are co-chairing multiple workshops, and we are presenting quite a few research results from colleagues of the chair and our collaborators.

Research Papers

We are excited to present the following scientific papers:

1. Rethink Geographical Generalizability with Unsupervised Self-Attention Model Ensemble: A Case Study of OpenStreetMap Missing Building Detection in Africa (full paper) Authors: Hao Li, Jiapan Wang, Johann Maximilian Zollner, Gengchen Mai, Ni Lao, Martin Werner

2. Bavaria Buildings - A Novel Dataset for Building Footprint Extraction, Instance Segmentation, and Data Quality Estimation (Data and Resources Paper) Authors: Martin Werner, Hao Li, Johann Maximilian Zollner, Balthasar Teuscher, Fabian Deuser

3. Exploring GeoAI Methods for Supraglacial Lake Mapping on Greenland Ice Sheet (GIS cup) Authors: Xuanshu Luo, Paul Walther, Wejdene Mansour, Balthasar Teuscher, Johann Maximilian Zollner, Hao Li, Martin Werner

4. Signal Separation in Global, Temporal Gravity Data (GeoAI workshop paper) Authors: Betty Heller-Kaikov, Roland Pail and Martin Werner

5. Towards GeoAI as a Containerized Microservice (SRC paper) Authors: Jiapan Wang

Workshops

We are delighted to co-host two workshops:

1. 11th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial 2023) Workshop Co-Chairs from our group: Martin Werner Description: Join us to explore the challenges and opportunities of processing and analyzing big geospatial data, fostering collaboration and knowledge exchange.

2. 2nd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data Workshop Co-Chairs from our group: Hao Li, Martin Werner Description: Engage with experts in the efficient searching and mining of large geospatial data collections, contributing to the development of cutting-edge solutions.

For further information about our participation and research reach out to us on the conference. We look forward to sharing insights, learning, and contributing to the vibrant academic community at SIGSPATIAL ‘23. For detailed information on all our published papers refer to the publications page.

Our PhD student Michael Petry has prepared a four minute thesis presentation on his Phd project Onboard Machine Learning for Physical Layer Signal Processing on Telecom-Satellites for the Four-Minute-Thesis (4MT) Competition hosted by IEEE Globecom.

In this challenge, PhD students are invited to submit a max. 4 minute long video explaining their PhD topic to a non-technical audience. In this video, Michael is explaining how he tries to implement software-defined radio systems using mainly (or only?) neural networks thus enabling AI specific hardware systems such as Xilinx Versal FPGAs to take over more aspects of the communication system.

In cooperation with the Chair of Soil Science at the Technical University of Munich, the Professorship Big Geospatial Data Management hosted a hackathon as part of the project SoilCarbonHack.

We gathered soil-driven and data-driven minds to work together on microscale NanoSIMS images and improve our understanding of soil carbon storage.

We had new ideas, engaging discussions, and could benefit from each others field knowledge during this interdisciplinary meeting.

If you are interested in the hands-on tutorials, we provide the related Jupyter Notebooks and an example NanoSIMS image on our hackathon page.

Stay tuned for upcoming events and papers from SoilCarbonHack!

For information on the project and upcoming events please refer to the project page.

Hao Li was presenting results of our efficient point cloud query paper (Teuscher et al., 2024) led by Balthasar Teuscher at the 18th 3DGeoInfo 2023 conference in Munich.

In this paper, we propose an efficient in-memory point cloud processing solution and implementation demonstrating that the inherent technical identity of the memory location of a point (e.g., a memory pointer) is both sufficient and elegant to avoid gridding as long as the point cloud fits into the main memory of the computing system. During the conference, we have collected a handful of nice comments and suggestions for the participants, which will be integrated in the future development. This paper is a nice joint effort with TUM colleagues from Chair of Engineering Geodesy (Prof. Holst) and Professorship for Remote Sensing Applications (Prof. Anders).

Resources

• Github Repository

1. Teuscher, B., Geißendörfer, O., Luo, X., Li, H., Anders, K., Holst, C., & Werner, M. (2024). Efficient In-Memory Point Cloud Query Processing. In T. H. Kolbe, A. Donaubauer, & C. Beil (Eds.), Recent Advances in 3D Geoinformation Science (pp. 267–286). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43699-4_16 [PDF] [Online]

Carla Rieger was a speaker in this year’s CERN Openlab summer student programme, where she presented on Quantum Machine Learning and Quantum Optimization. Furthermore, her talk was showcasing successful CERN use-cases to illustrate current practical applications of quantum algorithms in the field.

More details, including the lecture slides and a recording of the talk, can be found here.

The professorship “Big Geospatial Data Management” is seeking to fill a research assistant (E13) position to support research activities around geospatial artificial intelligence and spatial data management with a view toward modern didactics, starting as soon as possible.

TUM Portal Link

PDF Version

Register

The registration for the hackathon as part of the project SoilCarbonHack is now open!

To participate, please fill in the form here.

Hackathon

Detailed information on the project and event can be found here.

We are proud to announce that a team led by Fabian Deuser won the first place at the cross-view geolocalization competition.

In this challenge, the organizers presented a novel, challenging cross-view geo-localization dataset, called University160k. The motivation was to provide a comparably large satellite-view dataset for geolocalization to increase the number of similar features in different images. With a strategy of using pseudolabels to get a good alignment of latent space features and the localization problem, we were able to outperform all other submissions in the challenge. A paper on this topic is accepted and will be presented in the workshop.

Congratulations to our fresh (first month?) PhD student Fabian Deuser, who was the lead in all of this work.

This challenge has triggered a new line of research in our group as we believe that the true geolocalization problem is even harder than already depicted in the enlarged University160k dataset. On the other hand, the localization problem is typically a local problem as a coarse location might already be known in most applications. Furthermore, we will extend this activity to the indoor space, where even a limited-scalability reliable indoor geolocalization from images would be very helpful.

So stay tuned for the workshop presentation, the paper, and our follow-up work maybe including additional geolocalization challenges.

In cooperation with the Chair of Soil Science at the Technical University of Munich, the Professorship Big Geospatial Data Management is hosting a hackathon as part of the project SoilCarbonHack.
Please refer to the project page for detailed information on the project and events.

When?

12.10.2023 - 13.10.2023

Where?

Room 0120 in 0501 Institutsbau
Technical University of Munich
Arcisstraße 21
80333 München

Contact

Johann Maximilian Zollner
[email protected]
Professorship of Big Geospatial Data Management
Lise-Meitner-Str. 9
85521 Ottobrunn

Yahan Hu
[email protected]
Chair of Soil Science
Emil-Ramann-Straße 2
85354 Freising

On July 13, the room was packed with colleagues and students when Wenwen Li revealed to us her experience from adapting vision foundation models to geospatial images. It was very interesting for all of us and as the discussion grew into a long session shows how important this topic is.

In order to sustain the exchange and to turn the frontal oneway communication of such a presentation format into a more valuable interactive exchange, we organize a follow-up and lessons learnt session in three months.

Ressources

• Archiv Preprint on Permafrost Feature Example
• Google Scholar Page

A novel teaching aid in our lab. The Xilinx AI System KV260 provides all you need for embedded and mobile AI applications. Full FPGA board with a small form factor. Easy to use as it runs a Ubuntu Linux.

Students will learn more about mobile AI with this excellent board.

Hao Li was presenting aspects of our AtlasHDF infrastructure (Werner & Li, 2022) which was explored for surface water segmentation in the context of NFDI4Earth.

In a nutshell, we propose a shift in geospatial big data from Geo to NoGeo quite in the same way as the big data community sacrificed the SQL language in big data towards NoSQL infrastructures. OGC standards and their libraries are valuable assets for data management, interpretation, and preservation. But they have never been designed for computation. While our approach is less powerful in spatial operations at the moment, we have zero dependencies beyond HDF5. But this is mature, stable, and - most importantly - an integral part of both tensorflow and pytorch. The philosophy of this approach is simplicity: our data can be used by every deep learning scientist out of the box. And the best: supercomputers read it in parallel…

Ressources

• Github Repository
• Github Mirror at End of Project

1. Werner, M., & Li, H. (2022). AtlasHDF: An Efficient Big Data Framework for GeoAI. 1–7. https://doi.org/10.1145/3557917.3567615 [PDF] [Online]

The professorship “Big Geospatial Data Management” is looking for academic staff to support developments around artificial intelligence for spatial data management with a view towards hardware design, edge computing, autonomous flight of drones and modern didactics, starting as soon as possible.

TUM Portal Link

PDF Version

The new semester sees the arrival of the very first students at the TU Munich to study our newly established Bachelor Aerospace. We participate in this study program with the two computer science lectures (“Computational Foundations I”, “Computational Foundations II”) and teach the basics of algorithms, programming, and technical programming, and computer engineering, so that our students can develop mission critical software. In times of digitalization, there is probably no subject in which the long-term theoretical foundation (computer science as the foundation of all data science) is as important as actual practical skills (software as a universal tool). Accordingly, we adopt the didactic approach 4CID, which emphasizes learning through practical tasks in in a procedural way (step by step: increasing complexity with decreasing assistance) and thematic embedding (application examples). (examples of application) is in the spotlight.

We are looking forward to this new lecture series, which will be in parts also be used in the next semester in the course of geodesy and geoinformatics.

This year, we again participated in the MINTEntdeckerinnen program of the TU Munich. Six young women between the ages of 15 and 18 were our guests during the school vacations to gain experience in our field.

This year, we selected “autonomous aerial drones” as the topic and learned and tried out all the necessary elements needed to simulate autonomous drones and develop the necessary software.

We learned about MATLAB and SimuLink, got to grips with the Unity game engine, implemented a simulation of drone flight dynamics in MATLAB and connected it to the game engine. In the end we were able towe were able to plan trajectories, fly off, and solve a safe-and-rescue scenario.

One or two drones crashed in the simulation or flew through houses, but in the end all six students solved the problem independently and practically.

At the annual CODE conference at Universität der Bundeswehr München, our submission “CyberSuche” won 2nd prize.

We are happy to announce the following guest lecture:

Friday, July 31st,2020 13:00 – 15:00 CET.

In order to attend, you need to register for this lecture.

Registration Information

• https://tum-conf.zoom.us/meeting/register/tJUvdeCvpjkuE9EF1htysTrW095SR8Jdtxe1

Speaker

Martin Szugat

Abstract

Companies are drowning in data, but are thirsty for information. Although e.g. ESA’s Copernicus Open Data Strategy has exploded the availability & quality of geospatial data, only a small number of companies are using it. Because most companies only see their own data instead of seeing the opportunities and looking for new data. A data strategy is the business plan for data & analytics and data thinking is the method to develop this business plan. In this lecture you will get to know some useful tools to design data products yourself.

Vita

Martin Szugat is the founder and managing director of Datentreiber, a data strategy consulting firm. For his projects e.g. for Roche, ProSiebenSat1 and many more, he applies design thinking to data science and has developed a method and open source tools for data strategy design. Besides he is the program director of the Predictive Analytics & Deep Learning World conferences in Europe. He studied bioinformatics at LMU & TUM. When he has free time, he devotes himself to AI and Space Data and tweets about it on http://acceleran.do. The presentation is mandatory for students of the lecture Big Geospatial Data and open for all interested guests.

Today, I am starting my professorship for big geospatial data management. In these days, a virus is affecting all of us and in this situation it is unfortunately not possible to do a large party. But we will have this party as soon as we can meet in my Big Geoespatial Data Management lab.

For now, we concentrate on excellent teaching and research in times of home-office and online courses. .

Valentines day is a very dominant hashtag. The following shows a very short excerpt of the Twitter 1% public stream. Look at how Valentines day dominates hashtag distribution for this day.