Datasets:
Commit
·
caa3514
1
Parent(s):
687a219
Update README
Browse files
README.md
CHANGED
|
@@ -23,7 +23,7 @@ Samples from the TerraMesh dataset with seven spatiotemporal aligned modalities.
|
|
| 23 |
|
| 24 |
## Dataset organisation
|
| 25 |
|
| 26 |
-
The archive ships two top‑level splits `train/` and `val/`, each holding one folder per modality. More details with the dataset release end of June.
|
| 27 |
|
| 28 |
---
|
| 29 |
|
|
@@ -46,6 +46,7 @@ TerraMesh was used to pre-train [TerraMind-B](https://huggingface.co/ibm-esa-geo
|
|
| 46 |
On the six evaluated segmentation tasks from PANGAEA bench, TerraMind‑B reaches an average mIoU of 66.6%, the best overall score with an average rank of 2.33. This amounts to roughly a 3pp improvement over the next‑best open model (CROMA), underscoring the benefits of pre‑training on TerraMesh.
|
| 47 |
Compared to an ablation model pre-trained only on SSL4EO-S12 locations TerraMind-B performs overall 1pp better with better global generalization on more remote tasks like CTM-SS.
|
| 48 |
More details in our [paper](https://arxiv.org/abs/2504.11172).
|
|
|
|
| 49 |
---
|
| 50 |
|
| 51 |
## Citation
|
|
|
|
| 23 |
|
| 24 |
## Dataset organisation
|
| 25 |
|
| 26 |
+
The archive ships two top‑level splits `train/` and `val/`, each holding one folder per modality. More details follow with the dataset release end of June.
|
| 27 |
|
| 28 |
---
|
| 29 |
|
|
|
|
| 46 |
On the six evaluated segmentation tasks from PANGAEA bench, TerraMind‑B reaches an average mIoU of 66.6%, the best overall score with an average rank of 2.33. This amounts to roughly a 3pp improvement over the next‑best open model (CROMA), underscoring the benefits of pre‑training on TerraMesh.
|
| 47 |
Compared to an ablation model pre-trained only on SSL4EO-S12 locations TerraMind-B performs overall 1pp better with better global generalization on more remote tasks like CTM-SS.
|
| 48 |
More details in our [paper](https://arxiv.org/abs/2504.11172).
|
| 49 |
+
|
| 50 |
---
|
| 51 |
|
| 52 |
## Citation
|