# Geospatial foundation models enable data-efficient tree species mapping in temperate mountain forests

*2026-07-01 — paper*

Authors: James GC Ball, Jana Annika Wicklein, Zhengpeng Feng, Jovana Knezevic, Sadiq Jaffer, Anil Madhavapeddy, Clement Atzberger, Michele Dalponte, David Coomes


Accurate mapping of tree species from satellite data remains challenging in
heterogeneous mountain forests due to environmental gradients, mixed stands,
limited availability of high-purity training labels, and strong
illumination-angle effects.

Recent geospatial foundation models offer a new approach by learning generic,
cloud-agnostic, information-rich representations from large multi-sensor
archives suitable for a range of downstream tasks, but their ecological utility
for species-level mapping remains incompletely understood.

Here, we evaluate two geospatial foundation-model embeddings, AlphaEarth and
[Tessera](https://anil.recoil.org/projects/tessera), for tree species classification in the Trentino region of
northern Italy, using parcel-level forest inventories as reference data (18
species and species groups). We compare their performance against conventional
Sentinel-1+2 satellite composites across a series of controlled experiments
examining classification accuracy, label efficiency, classifier complexity,
robustness to label impurity, and temporal transferability

Foundation-model embeddings consistently outperform composite-based
multispectral satellite baselines (weighted F1 = 0.83 vs. 0.80; macro F1 = 0.55
vs. 0.50), reaching near-asymptotic accuracy with as few as 5% of available
training parcels and preserving ecologically meaningful structure aligned with
functional and taxonomic groupings.

However, realising this advantage requires a nonlinear classifier: a compact
neural network provides better results than classic machine learning (i.e.
Random Forest) and performs as well as deeper neural networks, while a linear
classifier on foundation-model embeddings underperforms a neural network on
conventional composites. Ancillary environmental covariates offer no additional
classification benefit when added to embedding-based models.

Classification accuracy remains robust to moderate levels of label impurity,
allowing mixed parcels to be retained in the training dataset without
substantial penalties, while training with parcel-level species proportions as
soft labels achieves higher peak performance (macro F1 = 0.586 for Tessera,
0.589 for AlphaEarth) and lower Proportion L1 error than hard labels without
requiring purity filtering, maximising the value of the full range of input
data.

However, temporal transfer across years reveals performance degradation, with
weighted F1 declining by 9% for Tessera and 15% for AlphaEarth, and
disproportionate losses for rare species. Overall, our results show that
geospatial foundation models shift a primary bottleneck in species mapping from
feature engineering toward the availability, quality, and temporal alignment of
ecological reference data, while opening new opportunities for scalable
biodiversity monitoring and the analysis of ecological change.


DOI: 10.1016/j.srs.2026.100466
Classification: full
Venue: Science of Remote Sensing
URL: https://www.sciencedirect.com/science/article/pii/S2666017226001045

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---
Canonical: https://anil.recoil.org/papers/2026-tessera-trentino
Type: paper
License: CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>
Tags: journal