Update README.md

README.md

@@ -45,6 +45,30 @@ TerraMesh
 └── terramesh.py
 ```
 
+Each folder includes up to 889 shard files, containing up to 10240 samples each. Samples from MajorTom-Core are stored in shards with the pattern `majortom_{split}_{id}.tar` while shards with SSL4EO-S12 samples start with `ssl4eos12_`.
+Samples are stored as Zarr Zip files which can be loaded with `zarr` (Version <= 2.18) or `xarray.load_zarr()`. Each sample location includes seven modalities that share the same shard and sample name. Note that each sample only inludes one Sentinel-1 version (S1GRD or S1RTC) because of different processing versions in the source datasets.
+Each Zarr file includes aligned metadata as demonstrated by this S1GRD example from sample `ssl4eos12_val_0080385.zarr.zip`:
+
+```
+<xarray.Dataset> Size: 283kB
+Dimensions:      (band: 2, time: 1, y: 264, x: 264)
+Coordinates:
+  * band         (band) <U2 16B "vv" "vh"
+    sample       <U9 36B "0194630_1"
+    spatial_ref  int64 8B 0
+  * time         (time) datetime64[ns] 8B 2020-05-03T02:07:17
+  * x            (x) float64 2kB 6.004e+05 6.004e+05 ... 6.03e+05 6.03e+05
+  * y            (y) float64 2kB 4.275e+06 4.275e+06 ... 4.273e+06 4.273e+06
+Data variables:
+    bands        (time, band, y, x) float16 279kB -9.461 -10.77 ... -16.67
+    center_lat   float64 8B 38.61
+    center_lon   float64 8B -121.8
+    crs          int64 8B 32610
+    file_id      (time) <U67 268B "S1A_IW_GRDH_1SDV_20201105T020809_20201105T...
+```
+
+Sentinel-2 modalities and LULC additionally provide a `cloud_mask` as additional metadata.
+
 ---
 
 ## Description 
@@ -71,12 +95,20 @@ More details in our [paper](https://arxiv.org/abs/2504.11172).
 
 ## Usage
 
+Important! The dataset was created using `zarr==2.18.0` and `numcodecs==0.15.1`. Unfortunately, Zarr 3.0 has backwards compatibility issues, and Zarr 2.18 is incompatible with NumCodecs 0.16. Therefore, we recommend installing:
+
+### Setup
+
+```
+pip install huggingface_hub webdataset torch numpy albumentations braceexpand zarr==2.18.0 numcodecs==0.15.1
+```
+
+
 ### Download
 
 You can download the dataset with the Hugging Face CLI tool. Please note that the dataset requires 16TB or storage.
 
 ```shell
-pip install huggingface_hub
 huggingface-cli download ibm-esa-geospatial/TerraMesh --repo-type dataset --local-dir data/TerraMesh
 ```
 
@@ -105,16 +137,16 @@ from torch.utils.data import DataLoader
 dataset = build_terramesh_dataset(
     path="https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh/resolve/main/",  # Streaming or local path
     modalities=["S2L2A"], 
-    split='val',
+    split="val",
     batch_size=8
 )
-# Batch keys: ['__key__', '__url__', 'image']
+# Batch keys: ["__key__", "__url__", "image"]
 
 # If you pass multiple modalities, the modalities are returned using the modality names as keys
 dataset = build_terramesh_dataset(
     path="https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh/resolve/main/",  # Streaming or local path
     modalities=["S2L2A", "S2L1C", "S2RGB", "S1GRD", "S1RTC", "DEM", "NDVI", "LULC"], 
-    split='val',
+    split="val",
     batch_size=8
 )
 
@@ -124,7 +156,7 @@ dataloader = DataLoader(dataset, batch_size=None, num_workers=4)
 # Iterate over the dataloader
 for batch in dataloader:
     print("Batch keys:", list(batch.keys()))
-    # Batch keys: ['__key__', '__url__', 'S2L2A', 'S2L1C', 'S2RGB', 'S1RTC', 'DEM', 'NDVI', 'LULC']
+    # Batch keys: ["__key__", "__url__", "S2L2A", "S2L1C", "S2RGB", "S1RTC", "DEM", "NDVI", "LULC"]
     # Because S1RTC and S1GRD are not present for all samples, each batch only includes one S1 version.
 
     print("Data shape:", batch["S2L2A"].shape)
@@ -138,6 +170,7 @@ for batch in dataloader:
 We provide some additional code for wrapping `albumentations` transform functions.
 We recommend albumentations because parameters are shared between all image modalities (e.g., same random crop). 
 However, it requires some wrapping to bring the data into the expected shape.  
+
 ```python
 import albumentations as A
 from albumentations.pytorch import ToTensorV2
@@ -164,12 +197,34 @@ val_transform = MultimodalTransforms(
 dataset = build_terramesh_dataset(
     path="https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh/resolve/main/",
     modalities=modalities,
-    split='val',
+    split="val",
     transform=val_transform,
     batch_size=8,
 )
 ```
 
+If you only use a single modality, you can directly pass a `A.Compose` instance to `build_terramesh_dataset` without the `MultimodalTransforms` wrapper. It still requires `Transpose([1, 2, 0])` as a first step. 
+
+### Returning metadata
+
+You can pass `return_metadata=True` to `build_terramesh_dataset()` to load center longitude and latitude, timestamps, and the S2 cloud mask as additional metadata.
+
+The resulting batch keys include: `["__key__", "__url__", "S2L2A", "S1RTC", ..., "center_lon", "center_lat", "cloud_mask", "time_S2L2A", "time_S1RTC", ...]`
+
+Therefore, you need to update the transforms if you use one:
+```
+        ...
+        additional_targets={m: "image" for m in modalities + ["cloud_mask"]}
+    ),
+    non_image_modalities=["__key__", "__url__", "center_lon", "center_lat"] + ["time_" + m for m in modalities]
+```
+
+Note that center points are not corrected when random crop is used. 
+The cloud mask provides the classes land (0), water (1), snow (2), thin cloud (3), thick cloud (4), and cloud shadow (5), and no data (6).
+DEM does not return a time value while LULC uses the S2 timestamp because of the augmentation usign the S2 cloud and ice mask. Time values are returned as integer values but can be converted back to datetime with 
+```python
+batch["time_S2L2A"].numpy().astype("datetime64[ns]")
+``` 
 
 If you have any issues with data loading, please create a discussion in the community tab and tag `@blumenstiel`.
 
@@ -204,4 +259,6 @@ The satellite data (S2L1C, S2L2A, S1GRD, S1RTC) is sourced from the [SSL4EO‑S1
 
 The LULC data is provided by [ESRI, Impact Observatory, and Microsoft](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02) (CC-BY-4.0).
 
+The cloud masks used for augmentating the LULC maps and provided as metadata are produced using the [SEnSeIv2](https://github.com/aliFrancis/SEnSeIv2/tree/main?tab=readme-ov-file) model.
+
 The DEM data is produced using [Copernicus WorldDEM-30](https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM) © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved