Create README.md

README.md

@@ -0,0 +1,84 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- earth-observation
+- segmentation
+- unet
+- pytorch
+- remote-sensing
+- spacenet
+datasets:
+- harshinde/spacenet-rio
+metrics:
+- iou
+- accuracy
+- dice
+pipeline_tag: image-segmentation
+---
+
+# SpaceNet Rio Building Detection Model
+
+This model detects building footprints from high-resolution satellite imagery. It is a PyTorch-based U-Net model trained on the **SpaceNet Area of Interest 1 (Rio de Janeiro)** dataset for semantic segmentation (binary: background vs. building).
+
+## Model Details
+
+- **Architecture:** U-Net with residual connections, 4 encoder/decoder levels, 10% spatial dropout, and 1024-channel bottleneck.
+- **Task:** Semantic Segmentation (Building Footprint Extraction)
+- **Input:** 3-band (RGB) pan-sharpened GeoTIFFs (dynamic architecture also supports 8-band multispectral).
+- **Output:** Binary mask (0: background, 1: building).
+- **Parameters:** ~31M (Kaiming He initialized)
+- **Framework:** PyTorch
+
+## Uses
+
+### Direct Use
+This model can be used to automatically detect and extract building footprint masks from satellite imagery. It is primarily designed for high-resolution (e.g., ~50cm/pixel) RGB satellite tiles.
+
+### Out-of-Scope Use
+- General object detection (e.g., cars, roads).
+- Imagery with completely different spatial resolutions (e.g., 30m Landsat data) without fine-tuning.
+
+## Training Details
+
+### Dataset
+Trained on the [SpaceNet Rio de Janeiro](https://huggingface.co/datasets/harshinde/spacenet-rio) dataset.
+- **Total Tiles:** 6,940
+- **Split:** 7:1:2 (Train: 4,857 | Val: 693 | Test: 1,387)
+
+### Hyperparameters
+- **Epochs:** 100 (with early stopping patience of 15)
+- **Batch Size:** 16 (Train) / 4 (Val)
+- **Learning Rate:** 0.001 with 5 warmup epochs
+- **Weight Decay:** 0.0001
+- **Loss Function:** Combined Dice Loss (weight 1.0) + Cross-Entropy Loss (weight 1.0)
+- **Image Crops:** 400x400 (Train) / 480x480 (Val)
+
+### Training Metrics
+Training metrics were tracked using TensorBoard and include:
+- Training/Validation Loss
+- Mean IoU and Per-Class IoU
+- Pixel Accuracy
+
+You can view the full training logs and curves [here on TensorBoard](https://huggingface.co/harshinde/spacenet/tensorboard).
+
+## How to Get Started with the Model
+
+You can load the weights using PyTorch:
+
+```python
+import torch
+
+# Assuming the U-Net architecture is defined in your local code
+# model = UNet(in_channels=3, num_classes=2)
+
+checkpoint = torch.load("best_model.pt", map_location="cpu")
+
+# Depending on how the state dict was saved, load it into the model
+# model.load_state_dict(checkpoint['model_state_dict']) # if saved as a dictionary
+# OR
+# model.load_state_dict(checkpoint) # if saved as raw state_dict
+
+model.eval()
+```