Emerging Flexible Designs for Geospatial Multimodal Foundation Models
Philipe Dias, Waqwoya Abebe, Abhishek Potnis, Aristeidis Tsaris, Dan Lu, Xiao Wang, Dalton Lunga
Why It Matters
What makes this one worth your time
Understanding the trade-offs in model architectures for geospatial data can help researchers and practitioners build more effective and flexible models for Earth observation tasks.
The paper offers a standardized comparison of geospatial foundation models to guide future design choices.
Summary
The paper conducts a comparative study of various foundation model architectures for geospatial multimodal reasoning, focusing on their flexibility with different spectral band configurations. It standardizes pretraining and evaluates models on the GEOBench benchmark to understand design trade-offs.
Key contributions
- Standardized comparison of foundation model architectures for geospatial data.
- Evaluation of models on the GEOBench benchmark for classification and segmentation tasks.
Notable insights
- Standardizing pretraining objectives and datasets allows for a fair comparison of model architectures.
- Evaluating models on a consistent benchmark like GEOBench provides insights into their strengths and limitations.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2606.12595v1 Announce Type: cross Abstract: Foundation models are rapidly transforming Earth observation by enabling scalable pretraining across diverse unlabeled geospatial modalities. However, their architectural diversity ranging from encoder-only to encoder-decoder and masked autoencoding paradigms makes it challenging to assess performance trade offs in a consistent manner. In this work, we present an apples-to-apples comparison of leading FM architectures designed for geospatial multimodal reasoning, with a particular focus on flexibility across varied spectral band configurations. We standardize pretraining using identical self supervised learning objectives and training datasets, and evaluate all models under consistent parameterization on the GEOBench benchmark across classification and segmentation tasks. Our results offer new insights into the design trade-offs between model flexibility, modality alignment, and downstream task performance. By highlighting architectural strengths and limitations under controlled conditions, this study provides practical guidance for building next generation geospatial foundation models capable of robust multimodal reasoning.