docs: add missing information to readme and changelog and mkdocs index

Author: print-sid8

README.md

@@ -30,7 +30,7 @@ own reader fetches pixels concurrently with no GDAL in the path.
 - **No STAC at training time** - query once at setup; zero API calls during training
 - **Reproducible** - same Parquet index = same records = same results
 - **Native dtypes** - uint16 stays uint16; no silent float32 promotion in the read path
-- **Shareable** - a 5 MB index captures scene selection, band metadata, and split assignments
+- **Shareable cache** - a 5 MB index captures scene selection, band metadata, and split assignments
 
 Rasteret is an **opt-in accelerator**. Your TorchGeo samplers, DataLoader,
 xarray workflows, and analysis tools stay the same - Rasteret handles the
@@ -121,6 +121,21 @@ collection = rasteret.build(
 COG headers, and caches everything as Parquet. The next run loads in
 milliseconds.
 
+### Inspect and filter
+
+```python
+collection        # Collection('s2_training', source='sentinel-2-l2a', bands=13, records=47, crs=32643)
+collection.bands  # ['B01', 'B02', ..., 'B12', 'SCL']
+len(collection)   # 47
+
+
+# Filter in memory — no network calls
+filtered = collection.subset(cloud_cover_lt=15, date_range=("2024-03-01", "2024-06-01"))
+```
+
+`subset()` accepts `cloud_cover_lt`, `date_range`, `bbox`, `geometries`, and
+`split`. For raw Arrow expressions, use `collection.where(expr)`.
+
 ### ML training (TorchGeo)
 
 ```python
@@ -157,7 +172,7 @@ ndvi = (ds.B08 - ds.B04) / (ds.B08 + ds.B04)
 | Multi-band COGs (AEF embeddings, etc.) | [AEF Embeddings guide](https://terrafloww.github.io/rasteret/how-to/aef-embeddings/) |
 | Authenticated sources (PC, requester-pays, Earthdata, etc.) | [Custom Cloud Provider](https://terrafloww.github.io/rasteret/how-to/custom-cloud-provider/) |
 | Share a Collection | `collection.export("path/")` then `rasteret.load("path/")` |
-| Filter by cloud cover, date, bbox | [`collection.subset()`](https://terrafloww.github.io/rasteret/tutorials/) |
+| Filter by cloud cover, date, bbox | [`collection.subset()`](https://terrafloww.github.io/rasteret/how-to/collection-management/) |
 
 </details>
 

docs/changelog.md

@@ -16,12 +16,13 @@
 - **Local catalog persistence**: `register_local()` persists to
   `~/.rasteret/datasets.local.json`; `export_local_descriptor()` for
   sharing catalog entries alongside Collections.
+- **Torchgeo GeoDataset**: Adapter created that use rasteret's own I/O parts to create a Torchgeo
+  GeoDataset.
 - **Native dtype preservation**: COG tiles return in their source dtype (uint16, int8,
   float32, etc.). No forced float32 conversion.
 - **Rasterio-aligned masking defaults**: AOI reads now default to `all_touched=False`
   and fill masked/outside-coverage pixels with `nodata` when present, otherwise `0`.
-  The primary read API (`read_cog`) returns a `valid_mask` so ML pipelines can avoid
-  learning from filled pixels.
+  The primary read API (`read_cog`) returns a `valid_mask`.
 - **rioxarray removed**: CRS encoding uses pyproj CF conventions directly (WKT2, PROJJSON,
   GeoTransform). The `xarray` extra no longer pulls rioxarray.
 - **Extended TIFF header parsing**: nodata, SamplesPerPixel, PlanarConfiguration,
@@ -32,14 +33,24 @@
   reproject to the most common CRS. Cross-CRS reprojection uses GDAL's
   `calculate_default_transform` for correct resolution handling.
 
+
+### Collection API
+
+- **Collection inspection**: `.bands`, `.bounds`, `.epsg`, `len()`, `__repr__()`,
+  `.describe()`, `.compare_to_catalog()` for quick metadata access without
+  materializing the full table.
+- **Filtering**: `collection.subset(cloud_cover_lt=..., date_range=..., bbox=...,
+  geometries=..., split=...)` for friendly filtering; `collection.where(expr)` for
+  raw Arrow dataset expressions. `select_split()` convenience wrapper.
+- **Sharing**: `collection.export("path/")` writes a portable copy;
+  `rasteret.load("path/")` reloads it.
+
 ### Other changes
 
 - Arrow-native geometry internals (GeoArrow replaces Shapely in hot paths).
 - obstore as base dependency for Rust-native HTTP backend.
 - CLI: `rasteret collections build|list|info|delete|import`, `rasteret build` shortcut.
 - CLI: `rasteret datasets list|info|build|register-local|export-local|unregister-local`.
-- Polished documentation, tutorials, and example scripts.
-- CI workflow fixes and public repo cleanup.
 
 ### Tested
 
@@ -49,14 +60,6 @@
   as the oracle, matching TorchGeo's own read semantics. See
   `test_dataset_pixel_comparison.py` and `test_network_smoke.py`.
 
-### Stability
-
-- STAC + COG scene workflows: **stable**
-- Multi-cloud (S3, Azure Blob, GCS): stable
-- Dataset catalog: stable
-- TorchGeo adapter: **stable** (upgraded from experimental)
-- Non-STAC / record-table ingestion (build_from_table): stable
-
 ### Breaking changes
 
 - `get_xarray()` returns data in native COG dtype instead of always float32. Code that

docs/index.md

@@ -99,6 +99,7 @@ import rasteret
 
 # 1. Build index (one-time, cached)
 collection = rasteret.build("earthsearch/sentinel-2-l2a", name="s2", bbox=(...), date_range=(...))
+collection.bands   # ['B01', 'B02', ..., 'B12', 'SCL']
 
 # 2. Filter metadata (in-memory, instant)
 sub = collection.subset(cloud_cover_lt=20, date_range=("2024-03-01", "2024-06-01"))