Benedikt Blumenstiel

About me

Hello everyone! I'm Benedikt — an open-minded researcher passionate about machine learning, innovation, and sustainability. I work in the AI for Climate Impact Team at the IBM Research Zurich lab. We build multi-modal foundation models for earth observation and work on climate-related applications using satellite data.

Looking forward to connecting with you!

Selected projects

TerraMind

TerraMind is the first multimodal any-to-any generative foundation model for Earth Observation jointly developed by IBM and ESA. TerraMind uses a dual-scale transformer-based encoder-decoder architecture, simultaneously processing pixel-level and token-level data. The model was pre-trained on 500B tokens from 9M spatio-temporally aligned multimodal samples from the TerraMesh dataset and is available on HuggingFace.

Prithvi EO 2.0

Prithvi-EO-2.0 is the second generation EO foundation model jointly developed by IBM and NASA. Prithvi-EO-2.0 is based on the ViT architecture, pretrained using a masked autoencoder (MAE) approach with two modifications: 3D positional embeddings for multi-temporal input and additional location and temporal embeddings to encode metadata. The models are pre-trained on 4.2M time series samples and released on HuggingFace.

MESS Benchmark

Holistic benchmarks allow a robust quantitative comparison of model architectures. However, such evaluation practice covering a wide range of real-world applications is lacking for multi-modal (i.e., text-to-image) zero-shot semantic segmentation tasks. Thus, our motivation to compose the Multi-domain Evaluation of Semantic Segmentation (MESS) benchmark, which consists of 22 datasets for varying application domains. More details can be found on our website.

Résumé

Publications

Jakubik, J., Yang, F., Blumenstiel, B., Scheurer, E., Sedona, R., Maurogiovanni, S. et al. (2025). TerraMind: Large-Scale Generative Multimodality for Earth Observation. arXiv preprint arXiv:2504.11171.

Blumenstiel, B., Fraccaro, P., Marsocci, V., Jakubik, J., Maurogiovanni, S., Czerkawski, M. et al. (2025). TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data. arXiv preprint arXiv:2504.11172.

Marti Escofet, F., Blumenstiel, B., Scheibenreif, L., Fraccaro, P., and Schindler, K. (2025). Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models. arXiv preprint arXiv:2504.17397.

Marimo, C. T., Blumenstiel, B., Nitsche, M., Jakubik, J., & Brunschwiler, T. (2025). Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation. arXiv preprint arXiv:2503.15969.

Gomes, C., Blumenstiel, B., Almeida, J. L. D. S., de Oliveira, P. H., Fraccaro, P., Marti Escofet, F. et al. (2025). TerraTorch: The Geospatial Foundation Models Toolkit. arXiv preprint arXiv:2503.20563.

Szwarcman, D., Roy, S., Fraccaro, P., Gíslason, Þ. E., Blumenstiel, B., Ghosal, R., ... & Moreno, J. B. (2024). Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications. arXiv preprint arXiv:2412.02732.

Blumenstiel, B., Moor, V., Kienzler, R., Brunschwiler, T. (2024). Multi-Spectral Remote Sensing Image Retrieval using Geospatial Foundation Models. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

Blumenstiel, B., Jakubik, J., Kühne, H., & Vössing, M. (2023). What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation. Advances in Neural Information Processing Systems.

Kienzler, R., Tizzei, L. P. T., Blumenstiel, B., Nagy, Z. A., Mukkavilli, S. K., Schmude, J., Freitag, M., Behrendt, M., Civitarese, D. S., Simumba, N., Kimura, D. & Hamann, H. (2023). TensorBank: Tensor Lakehouse for Foundation Model Training. IEEE International Conference on Big Data.

Jakubik, J., Hemmer, P., Voessing, M., Blumenstiel, B., Bartos, A., & Mohr, K. (2022). Designing a Human-in-the-Loop System for Object Detection in Floor Plans. Proceedings of the AAAI Conference on Artificial Intelligence.

Jakubik, J., Blumenstiel, B., Voessing, M., & Hemmer, P. (2022). Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning. International Conference on Wirtschaftsinformatik.

Work Experience

Research Software Engineer, AI for Climate Impact, IBM Research

02/2024 - present

Research on pre-training of multi-satellite and multi-modal geospatial foundation models
Developing data pipelines for a geospatial ML platform and foundation model pre-training

Intern, AI for Climate Impact, IBM Research

08/2023 - 02/2024

Deploying and applying MLOps tools and pipelines
Developing the CLAIMED Component Compiler (C3) for the CLAIMED project
Experiments with remote sensing image retrieval using geospatial foundation models

Junior Researcher, Applied AI in Services Lab, KIT

05/2020 - 04/2022

Research about human-AI collaboration and machine learning, with a focus on few-shot learning
Developed AI applications for a AI project in the AEC industry
Worked with multiple computer vision models and deployed them on high performance clusters

Design Thinking project, KIT & HUK Coburg

10/2019 - 06/2020

Used design thinking to develop user-centered innovations and prototypes for retirement saving

Intern, R&D Strategy, AUDI AG

07/2017 - 02/2018

Supported the project manager in defining a new technology-focused R&D strategy
Worked in the project management office of an R&D transformation project

Education

M.Sc. Information Systems, Karlsruhe Institute of Technology

10/2019 - 04/2023

Focus on machine learning and service innovation

Exchange, Norwegian University of Science and Technology

01/2021 - 06/2021

Courses in computer vision, natural language processing and statistics

B.Sc. Industrial Engineering, Karlsruhe Institute of Technology

10/2015 - 09/2019

Focus on digital services and business strategy

Skills

Advanced: Python, deep learning, computer vision, data processing, geospatial data, NLP, German, English, project management, PowerPoint, Design Thinking

Basics: Docker, Kubernetes, Kubeflow, OpenShift, Travis CI, R, Java

Packages: PyTorch, Lightning, TensorFlow, xarray, Numpy, Pandas, GeoPandas, TerraTorch, TorchGeo, and more