metadata
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 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.
How to Get Started with the Model
You can load the weights using PyTorch:
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()