Update README.md

README.md

@@ -4,7 +4,7 @@ pipeline_tag: image-segmentation
 tags:
 - semantic segmentation
 - pytorch
-- SSL
+- landcover
 library_name: pytorch
 ---
 
@@ -55,7 +55,91 @@ Benchmark results on 4 datasets :
 
 <hr>
 
-## Usage
+## 🚀 Getting Started
+
+First, set up the module with [Poetry](https://python-poetry.org/).
+
+```bash
+# 1. Change directory
+cd MAESTRO
+
+# 2. Install dependencies with Poetry
+poetry install
+```
+
+Then, you can start from the following minimal examples.
+
+Intra-dataset MAESTRO on TreeSatAI-TS:
+```bash
+# pre-train, probe and finetune on TreeSatAI-TS
+poetry run python main.py \
+        model.model=mae model.model_size=medium \
+        opt_pretrain.epochs=100 opt_probe.epochs=10 opt_finetune.epochs=50 \
+        datasets.name_dataset=treesatai_ts \
+        datasets.root_dir=/path/to/dataset/dir datasets.treesatai_ts.rel_dir=TreeSatAI-TS \
+        run.exp_dir=/path/to/experiments/dir run.exp_name=mae-m_treesat
+```
+
+Intra-dataset MAESTRO on PASTIS-HD:
+```bash
+# pre-train, probe and finetune on PASTIS-HD
+poetry run python main.py \
+        model.model=mae model.model_size=medium \
+        opt_pretrain.epochs=100 opt_probe.epochs=10 opt_finetune.epochs=50 \
+        datasets.name_dataset=pastis_hd \
+        datasets.root_dir=/path/to/dataset/dir datasets.pastis_hd.rel_dir=PASTIS-HD \
+        run.exp_dir=/path/to/experiments/dir run.exp_name=mae-m_pastis
+```
+
+Intra-dataset MAESTRO on FLAIR-HUB:
+```bash
+# pre-train, probe and finetune on FLAIR-HUB
+poetry run python main.py \
+        model.model=mae model.model_size=medium \
+        opt_pretrain.epochs=100 opt_probe.epochs=15 opt_finetune.epochs=100 \
+        datasets.name_dataset=flair \
+        datasets.root_dir=/path/to/dataset/dir datasets.flair.rel_dir=FLAIR-HUB \
+        run.exp_dir=/path/to/experiments/dir run.exp_name=mae-m_flair
+```
+
+Cross-dataset MAESTRO from S2-NAIP urban to TreeSatAI-TS:
+```bash
+# pre-train on S2-NAIP urban
+poetry run python main.py \
+        model.model=mae model.model_size=medium \
+        opt_pretrain.epochs=15 opt_probe.epochs=0 opt_finetune.epochs=0 \
+        datasets.name_dataset=s2_naip \
+        datasets.root_dir=/path/to/dataset/dir datasets.s2_naip.rel_dir=s2-naip-urban \
+        run.exp_dir=/path/to/experiments/dir run.exp_name=mae-m_s2-naip && \
+# probe and finetune on TreeSatAI-TS
+poetry run python main.py \
+        model.model=mae model.model_size=medium \
+        opt_pretrain.epochs=0 opt_probe.epochs=10 opt_finetune.epochs=50 \
+        datasets.name_dataset=treesatai_ts \
+        datasets.treesatai_ts.aerial.image_size=240 datasets.treesatai_ts.aerial.patch_size.mae=16 \
+        datasets.treesatai_ts.s1_asc.name_embed=s1 datasets.treesatai_ts.s1_des.name_embed=s1 \
+        datasets.root_dir=/path/to/dataset/dir datasets.treesatai_ts.rel_dir=TreeSatAI-TS \
+        run.exp_dir=/path/to/experiments/dir run.load_name=mae-m_s2-naip run.exp_name=mae-m_s2-naip-x-treesat
+
+```
 
+<hr>
+
+## Reference
+
+If you use this code, please cite:
+
+```bibtex
+@article{labatie2025maestro,
+  title={MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data},
+  author={Labatie, Antoine and Vaccaro, Michael and Lardiere, Nina and Garioud, Anatol and Gonthier, Nicolas},
+  journal={arXiv preprint arXiv:2508.10894},
+  year={2025}
+}
+```
+
+<hr>
+
+## Acknowledgement
 
-<hr>
+The experiments in the paper were conducted using HPC/AI resources from GENCI-IDRIS (allocations A0181013803, A0161013803, and AD010114597R1).