Benedikt Blumenstiel
AI for Climate Impact, IBM Research | Based in Zurich
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
* Equal contribution
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