---
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 (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()
```