Update README.md

README.md

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----
-license: cc0-1.0
----
+---
+license: cc0-1.0
+task_categories:
+  - object-detection
+  - image-segmentation
+tags:
+  - landslide-detection
+  - satellite-imagery
+  - landsat
+  - earthquake
+  - remote-sensing
+  - pascal-voc
+  - yolo
+size_categories:
+  - 1K<n<10K
+---
+
+# LandsatQuake
+
+Landslide detection in Landsat satellite imagery across 16 earthquake and rainstorm events (1987–2020).
+
+## ⚠️ Important: Do NOT use `load_dataset()`
+
+HuggingFace's auto-parquet conversion does **not** work correctly for this dataset. It drops all bounding-box annotations and replaces filenames with hashes. Instead, **clone the repository directly**:
+
+```bash
+# Install git-lfs first: https://git-lfs.com
+git lfs install
+git clone https://huggingface.co/datasets/Vihaanakshaay/LandsatQuake
+cd LandsatQuake
+git lfs pull
+```
+
+## Dataset Structure
+
+```
+LandsatQuake/
+├── LandsatQuake/                        # Object detection (Pascal VOC)
+│   ├── train/                           # 834 images + 834 XMLs
+│   ├── test/                            # 105 images + 105 XMLs
+│   └── val/                             # 103 images + 103 XMLs
+├── LandsatQuake_InstanceSegmentation/   # Instance segmentation (YOLO)
+│   ├── image_patches/                   # Image files
+│   └── labels/                          # YOLO polygon TXT files
+└── LandsatQuake_Polygons.zip            # Raw polygon annotations
+```
+
+## Format Details
+
+### Images
+- **Size:** 224 × 224 pixels, RGB, JPEG
+- **Source:** Landsat satellite imagery
+- **Naming convention:** `{year} {location} {pre/post}-earthquake_patch_{x}_{y}.jpg`
+
+### Object Detection Labels (Pascal VOC XML)
+Each image has a paired `.xml` file in Pascal VOC format with bounding boxes:
+```xml
+<annotation>
+  <size><width>224</width><height>224</height><depth>3</depth></size>
+  <object>
+    <name>landslides</name>
+    <bndbox><xmin>141</xmin><ymin>201</ymin><xmax>150</xmax><ymax>210</ymax></bndbox>
+  </object>
+  <!-- ... more objects ... -->
+</annotation>
+```
+
+### Instance Segmentation Labels (YOLO TXT)
+Each `.txt` file contains normalised polygon coordinates:
+```
+0 x1 y1 x2 y2 x3 y3 ...
+```
+Where `0` = landslides class and coordinates are normalised to [0, 1].
+
+## Statistics
+
+| Split | Images | Landslide Boxes | Mean Boxes/Image |
+|-------|--------|-----------------|------------------|
+| Train | 834    | 5,626           | 6.7              |
+| Test  | 105    | 508             | 4.8              |
+| Val   | 103    | 830             | 8.1              |
+| **Total** | **1,042** | **6,964** | **6.7** |
+
+### Events Covered (16 total)
+| Event | Images |
+|-------|--------|
+| 1987 Sichuan pre-earthquake | 508 |
+| 2015 Gorkha earthquake | 205 |
+| 1999 Chamoli earthquake | 84 |
+| 2011 Sikkim earthquake | 64 |
+| 2016 Arun rainstorm | 34 |
+| 1991 Limon earthquake | 30 |
+| 2010 Haiti | 28 |
+| Others (9 events) | 89 |
+
+## Quick Start (Python)
+
+```python
+import os
+import xml.etree.ElementTree as ET
+from PIL import Image
+
+def parse_voc_xml(xml_path):
+    """Parse Pascal VOC XML and return list of (class, xmin, ymin, xmax, ymax)."""
+    tree = ET.parse(xml_path)
+    boxes = []
+    for obj in tree.getroot().findall("object"):
+        cls = obj.find("name").text
+        bb = obj.find("bndbox")
+        boxes.append((
+            cls,
+            int(bb.find("xmin").text), int(bb.find("ymin").text),
+            int(bb.find("xmax").text), int(bb.find("ymax").text),
+        ))
+    return boxes
+
+# Example: load a training image with its annotations
+img = Image.open("LandsatQuake/train/1987 Sichuan pre-earthquake_patch_2464_11872.jpg")
+boxes = parse_voc_xml("LandsatQuake/train/1987 Sichuan pre-earthquake_patch_2464_11872.xml")
+print(f"Image size: {img.size}, Landslide boxes: {len(boxes)}")
+```