Jorge L. Rodríguez, Kasper Johansen, Areej Alwahas, Mariana Elías-Lara, Victor Angulo-Morales, Fernando T. Maestre and Matthew F. McCabe
Climate and Livability Initiative, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Reconstruction examples generated by the shallow decoder during FLORO pretraining.
Overview
FLORO (Foundation Learning Of Remote Sensing Observations for Ecological Research) is a multimodal, multitask Vision Transformer-based foundation model designed for scalable ecological monitoring using both multispectral satellite data and elevation sources.
Built with a masked autoencoder backbone and fine-tuned on diverse ecological tasks, FLORO generalizes well across modalities, sensor types, and ecosystems.
🔍 Highlights
- Multimodal Inputs: Uses multispectral bands + digital surface models (DSM) from SRTM, UAV, or photogrammetric data.
- Self-Supervised Pretraining: Trained with adaptive masked autoencoding strategies across 400K+ image patches.
- Flexible Decoders: Task-specific decoders output vegetation structure (CHM), forage biomass, and nutrient content.
🧠 Architecture
A full Transformer-based encoder is pretrained via masked image autoencoding. Downstream decoders are optimized using supervised objectives for both segmentation and regression tasks.
