README.md

---
language:
- en
license: gemma
library_name: jax
pipeline_tag: image-text-to-text
tags:
- gemma
- earth-observation
- remote-sensing
- satellite-imagery
- lora
- jax
- tunix
base_model: google/gemma-3-4b-it
datasets:
- akshaydudhane/EarthDial-Dataset
metrics:
- exact_match
- precision
- recall
- f1
- jaccard
---

# Gemma Earth (Gemma 3 4B IT LoRA for Earth Observation)

## Model Description

Gemma Earth is a domain-adapted Earth Observation model built by fine-tuning Google Gemma 3 4B IT with LoRA adapters for satellite scene understanding.

The current release focuses on multi-label land-use and land-cover classification from the EarthDial BigEarthNet subset, with a pipeline designed to extend to additional EarthDial tasks.

This project provides an end-to-end JAX stack pipeline for dataset preparation, LoRA fine-tuning, checkpointing, evaluation, and inference serving.

Project repository: https://github.com/haruiz/gemma_earth

- Base model: google/gemma-3-4b-it
- Adaptation: LoRA
- Training stack: JAX + Flax (NNX) + Tunix + Qwix + Optax + Orbax + Grain
- Primary task: remote-sensing scene classification (multi-label)

## Intended Use

### Primary Use

- Earth Observation and remote-sensing research
- Multi-label scene classification on EarthDial/BigEarthNet-style samples
- Benchmarking and experimentation with TPU-based JAX fine-tuning workflows



## Training Data

- Dataset source: akshaydudhane/EarthDial-Dataset
- Current focus: EarthDial classification subset (BigEarthNet)
- Training setup in this project uses sampled subsets and validation splits configured via environment variables

## Training Procedure

- Hardware: Google Cloud TPU v5litepod-8
- LoRA configuration (default project setup):
  - Rank: 32
  - Alpha: 64.0
- Typical sequence length: 768
- Optimizer schedule includes warmup + decay (Optax)

## Evaluation Summary

Benchmark run size: 1500 samples

| Metric | Baseline | Fine-tuned | Delta (absolute) |
| --- | ---: | ---: | ---: |
| Exact Match | 2.53% | 22.80% | +20.27 pp |
| Sample Precision | 27.27% | 68.53% | +41.27 pp |
| Sample Recall | 10.35% | 71.86% | +61.51 pp |
| Sample F1 | 14.18% | 68.16% | +53.98 pp |
| Sample Jaccard | 10.35% | 57.47% | +47.12 pp |
| Micro Precision | 29.57% | 63.56% | +33.99 pp |
| Micro Recall | 9.25% | 67.00% | +57.75 pp |
| Micro F1 | 14.09% | 65.24% | +51.15 pp |
| Macro F1 | 6.53% | 31.50% | +24.97 pp |

The adapted model significantly improves over baseline across all reported metrics.

## How To Use

This model is intended to be used with the Gemma Earth codebase.

~~~bash
# Example: run one-image evaluation using a Hugging Face checkpoint directory
python scripts/one_example_eval.py \
  --model-checkpoint-source huggingface \
  --model-dir /path/to/hf_checkpoint_dir \
  --image-path /path/to/image.jpg
~~~

~~~bash
# Example: run benchmark evaluation with Hugging Face checkpoint source
python main.py benchmark \
  --num-examples 1500 \
  --eval-restore-policy permissive \
  --model-checkpoint-source huggingface
~~~
## Citation

If you use this model, please cite:

~~~bibtex
@misc{gemma_earth_2026,
  title={Gemma Earth: Fine-tuning Gemma for Remote Sensing Scene Classification},
  author={Henry Ruiz},
  year={2026},
  howpublished={GitHub repository},
  url={https://github.com/haruiz/gemma_earth},
}
~~~

Related references:

~~~bibtex
@misc{soni2024earthdial,
  title={EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues},
  author={Soni, Sagar and Dudhane, Akshay and Debary, Hiyam and Fiaz, Mustansar and Munir, Muhammad Akhtar and Danish, Muhammad Sohail and Fraccaro, Paolo and Watson, Campbell D and others},
  year={2024},
  eprint={2412.15190},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  doi={10.48550/arXiv.2412.15190},
  url={https://arxiv.org/abs/2412.15190}
}

@misc{sumbul2019bigearthnet,
  title={BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding},
  author={Sumbul, Gencer and Charfuelan, Marcela and Demir, Beg{"u}m and Markl, Volker},
  year={2019},
  eprint={1902.06148},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  doi={10.48550/arXiv.1902.06148},
  url={https://arxiv.org/abs/1902.06148}
}
~~~

## Model And License Notes

This model is derived from Gemma 3 4B IT. Use must comply with the Gemma license and any applicable dataset terms.