Update README.md

README.md

@@ -1,28 +1,72 @@
----
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 937662970
-    num_examples: 231
-  download_size: 919821617
-  dataset_size: 937662970
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-license: ms-pl
-task_categories:
-- mask-generation
-tags:
-- street
-- view
-- street-view
-- '360'
-pretty_name: 360 streets view with mask
----
+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: mask
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 937662970
+    num_examples: 231
+  download_size: 919821617
+  dataset_size: 937662970
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+license: ms-pl
+task_categories:
+- mask-generation
+tags:
+- street
+- view
+- street-view
+- '360'
+pretty_name: 360° streets view with mask
+---
+# 📷 360 Clean Dataset
+
+A dataset of **360° equirectangular images** with corresponding **binary masks** that hide the typical artifacts introduced by 360° capture, such as:
+
+* 🚗 Vehicles (cars, bikes, etc.),
+* 🧍‍♂️ The person capturing the video (cyclist, pedestrian, etc.),
+* 🎥 Camera equipment or shadows appearing at the bottom of the image.
+
+## 🧾 Description
+
+Each sample in the dataset contains:
+
+* `image`: the original 360° equirectangular image (2:1 aspect ratio, typically 3040×1520),
+* `mask`: a binary mask of the same resolution, where white pixels (`255`) indicate areas to ignore (e.g. person, vehicle), and black pixels (`0`) represent the usable background.
+
+The masks were **manually created**.
+
+This dataset is particularly useful for:
+
+* 🗺️ 3D reconstruction tasks (e.g. NeRF, Gaussian Splatting),
+* 🤖 Training vision models without human-related artifacts,
+* 📍 Visual geolocation from clean, unobstructed environments.
+
+## 📁 Data Format
+
+```python
+{
+    "image": Image,  # equirectangular 360° scene
+    "mask": Image    # binary mask: 1 = ignore, 0 = keep
+}
+```
+
+Files are matched by filename: `xxx.jpg` and `xxx_mask.png`.
+
+## 🏷️ Possible Use Cases
+
+* **Object removal / Inpainting**
+* **Semantic Segmentation**
+* **Dynamic object filtering**
+* **Preprocessing for 3D or geospatial vision tasks**
+
+## 🪪 License
+
+This dataset is released under the **MIT**.