Upload dataset

README.md

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+---
+dataset_info:
+- config_name: 128x128
+  features:
+  - name: image
+    sequence:
+      sequence:
+        sequence:
+          dtype: float32
+  - name: label
+    sequence:
+      sequence:
+        dtype: uint8
+  - name: i
+    dtype: int32
+  - name: j
+    dtype: int32
+  - name: start_time
+    dtype: string
+  - name: end_time
+    dtype: string
+  - name: ind
+    dtype: int32
+  - name: size
+    dtype: int32
+  splits:
+  - name: train
+    num_bytes: 568043374
+    num_examples: 529
+  - name: test
+    num_bytes: 54764106
+    num_examples: 51
+  download_size: 0
+  dataset_size: 622807480
+- config_name: 256x256
+  features:
+  - name: image
+    sequence:
+      sequence:
+        sequence:
+          dtype: float32
+  - name: label
+    sequence:
+      sequence:
+        dtype: uint8
+  - name: i
+    dtype: int32
+  - name: j
+    dtype: int32
+  - name: start_time
+    dtype: string
+  - name: end_time
+    dtype: string
+  - name: ind
+    dtype: int32
+  - name: size
+    dtype: int32
+  splits:
+  - name: train
+    num_bytes: 5484000000  # Estimated
+    num_examples: 1713
+  - name: test
+    num_bytes: 587000000   # Estimated
+    num_examples: 183
+  download_size: 0
+  dataset_size: 6071000000  # Estimated
+task_categories:
+- image-classification
+- computer-vision
+tags:
+- satellite-imagery
+- goes-16
+- abi
+- multi-spectral
+- remote-sensing
+- weather
+- earth-observation
+---
+
+# GOES-16 ABI Satellite Image Dataset
+
+This dataset contains GOES-16 ABI (Advanced Baseline Imager) satellite images with multi-spectral imagery and corresponding labels.
+
+## Dataset Description
+
+The dataset contains training and test splits at two different resolutions (128x128 and 256x256). Each image has 16 spectral channels from the GOES-16 ABI instrument. The data is provided by NOAA and NESDIS.
+
+### Dataset Structure
+
+The dataset is organized into the following configurations:
+
+- **128x128**: Images at 128x128 pixel resolution
+  - Train: 529 examples (~568 MB)
+  - Test: 51 examples (~55 MB)
+  - Total: 580 examples (~623 MB)
+  
+- **256x256**: Images at 256x256 pixel resolution  
+  - Train: 1,713 examples (~5.5 GB estimated)
+  - Test: 183 examples (~587 MB estimated)
+  - Total: 1,896 examples (~6.1 GB estimated)
+
+### Data Fields
+
+Each example in the dataset contains:
+
+- `image`: Multi-spectral satellite image as a 3D array with shape [16, height, width]
+  - 16 spectral channels from GOES-16 ABI instrument
+  - Values are float32 type, typically in range [-3, 3]
+  - Height and width are 128 or 256 depending on configuration
+- `label`: Corresponding label/mask as a 2D array with shape [height, width]
+  - Values are uint8 type, typically binary (0 or 1)
+- `i`: Spatial coordinate i (int32)
+- `j`: Spatial coordinate j (int32)
+- `start_time`: Start time of the satellite observation (string)
+- `end_time`: End time of the satellite observation (string)
+- `ind`: Index within the original data array (int32)
+- `size`: Resolution size (128 or 256) (int32)
+
+### Data Source
+
+The satellite data originates from:
+- **Instrument**: GOES-16 Advanced Baseline Imager (ABI)
+- **Provider**: NOAA (National Oceanic and Atmospheric Administration)
+- **Data Center**: NESDIS (National Environmental Satellite, Data, and Information Service)
+
+## Usage
+
+### Basic Usage
+
+```python
+from datasets import load_dataset
+import numpy as np
+
+# Load 128x128 resolution data
+dataset = load_dataset("./goes16_dataset.py", name="128x128")
+
+# Access a sample
+sample = dataset["train"][0]
+
+# Convert to numpy arrays for processing
+image = np.array(sample["image"])  # Shape: (16, 128, 128)
+label = np.array(sample["label"])  # Shape: (128, 128)
+
+print(f"Image shape: {image.shape}")
+print(f"Label shape: {label.shape}")
+print(f"Image data type: {image.dtype}")
+print(f"Label data type: {label.dtype}")
+print(f"Image value range: [{image.min():.3f}, {image.max():.3f}]")
+print(f"Label value range: [{label.min()}, {label.max()}]")
+```
+
+### Accessing Metadata
+
+```python
+# Get observation metadata
+print(f"Spatial coordinates: i={sample['i']}, j={sample['j']}")
+print(f"Observation time: {sample['start_time']} to {sample['end_time']}")
+print(f"Resolution: {sample['size']}x{sample['size']}")
+print(f"Array index: {sample['ind']}")
+```
+
+### Working with Different Resolutions
+
+```python
+# Load different resolutions
+dataset_128 = load_dataset("./goes16_dataset.py", name="128x128")
+dataset_256 = load_dataset("./goes16_dataset.py", name="256x256")
+
+# Compare samples
+sample_128 = dataset_128["train"][0]
+sample_256 = dataset_256["train"][0]
+
+image_128 = np.array(sample_128["image"])  # Shape: (16, 128, 128)
+image_256 = np.array(sample_256["image"])  # Shape: (16, 256, 256)
+
+print(f"128x128 image shape: {image_128.shape}")
+print(f"256x256 image shape: {image_256.shape}")
+```
+
+### Data Processing Example
+
+```python
+import matplotlib.pyplot as plt
+
+# Load a sample
+sample = dataset["train"][0]
+image = np.array(sample["image"])  # Shape: (16, 128, 128)
+label = np.array(sample["label"])  # Shape: (128, 128)
+
+# Visualize a specific channel (e.g., channel 0)
+plt.figure(figsize=(12, 4))
+
+plt.subplot(1, 3, 1)
+plt.imshow(image[0], cmap='viridis')
+plt.title('Channel 0 (Raw)')
+plt.colorbar()
+
+plt.subplot(1, 3, 2)
+plt.imshow(label, cmap='gray')
+plt.title('Label/Mask')
+plt.colorbar()
+
+plt.subplot(1, 3, 3)
+# Create RGB composite (if channels support it)
+# This is an example - adjust channels based on your specific needs
+rgb_composite = np.stack([image[2], image[1], image[0]], axis=-1)
+rgb_composite = (rgb_composite - rgb_composite.min()) / (rgb_composite.max() - rgb_composite.min())
+plt.imshow(rgb_composite)
+plt.title('RGB Composite')
+
+plt.tight_layout()
+plt.show()
+```
+
+## Dataset Statistics
+
+### 128x128 Configuration
+- **Total examples**: 580 (529 train, 51 test)
+- **Dataset size**: 623 MB
+- **Image dimensions**: 16 channels × 128 × 128 pixels
+- **Data types**: float32 (images), uint8 (labels)
+
+### 256x256 Configuration  
+- **Total examples**: 1,896 (1,713 train, 183 test)
+- **Dataset size**: ~6.1 GB (estimated)
+- **Image dimensions**: 16 channels × 256 × 256 pixels
+- **Data types**: float32 (images), uint8 (labels)
+
+## Applications
+
+This dataset can be used for:
+- Satellite image analysis and classification
+- Weather pattern recognition
+- Multi-spectral image processing
+- Earth observation studies
+- Remote sensing applications
+- Computer vision research on satellite imagery
+- Semantic segmentation of satellite data
+- Time series analysis of atmospheric conditions
+
+## Data Format
+
+The actual image and label data are stored in NPZ format (`data/dataset.npz`) with corresponding metadata in CSV format (`data/metadata.csv`).

data/dataset.npz

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+size 541481677