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A semantic segmentation model for building detection in high-resolution aerial imagery, trained on the WHU Building Dataset.
Fuente del modelo
Descripción de la fuente
A semantic segmentation model for building detection in high-resolution aerial imagery, trained on the WHU Building Dataset.
Fuentes
1 fuenteVerificado 27 sept
Artefactos del modelo
1 artefactoExtractos de fuentes
2 extractos| Property | Value |
|---|
| Architecture | UNet++ |
| Encoder | EfficientNet-B4 (ImageNet pretrained) |
| Framework | segmentation-models-pytorch (SMP) |
| Training Framework | PyTorch Lightning |
| Input | 3-channel RGB, 512x512 tiles |
| Output | 2-class mask (Background=0, Building=1) |
| Parameters | ~20.8M |
| Model Size | ~84 MB |
Evaluated on the WHU Building Dataset test split (1,228 tiles):
| Metric | Score |
|---|---|
| IoU | 0.9054 |
| Dice | 0.9503 |
| Best Val IoU | 0.9434 |
pip install geoai-py timm segmentation-models-pytorch
import geoai
# Run building detection on a GeoTIFF
geoai.timm_segmentation_from_hub(
input_path="input_image.tif",
output_path="building_prediction.tif",
repo_id="giswqs/whu-building-unetplusplus-efficientnet-b4",
window_size=512,
overlap=256,
batch_size=4,
)
# Vectorize to building footprints
gdf = geoai.orthogonalize(
input_path="building_prediction.tif",
output_path="building_footprints.geojson",
epsilon=2.0,
)
import json
import torch
import segmentation_models_pytorch as smp
# Load config
with open("config.json") as f:
config = json.load(f)
# Create model
model = smp.UnetPlusPlus(
encoder_name="efficientnet-b4",
encoder_weights=None,
in_channels=3,
classes=2,
)
# Load weights
state_dict = torch.load("model.pth", map_location="cpu")
model.load_state_dict(state_dict)
model.eval()
See the full inference notebook with visualization and analysis:
The WHU Building Dataset consists of aerial imagery at 0.3m resolution with binary building masks:
Ji, S., Wei, S., & Lu, M. (2019). Fully Convolutional Networks for Multisource Building Identification. IEEE Transactions on Geoscience and Remote Sensing, 57(1), 108-120.
This model is released under the Apache 2.0 License.
--- library_name: segmentation-models-pytorch license: apache-2.0 tags: - semantic-segmentation - building-detection - remote-sensing - aerial-imagery - geospatial - unet++ - efficientnet - pytorch datasets: - giswqs/WHU-Building-Dataset metrics: - iou - dice pipeline_tag: image-segmentation --- # WHU Building Detection — EfficientNet-B4 + UNet++ A semantic segmentation model for **building detection** in high-resolution aerial imagery, trained on the [WHU Building Dataset](https://study.rsgis.whu.edu.cn/pages/download/building_dataset.html). ## Model Description | Property | Value | |----------|-------| | **Architecture** | UNet++ | | **Encoder** | EfficientNet-B4 (ImageNet pretrained) | | **Framework** | [segmentation-models-pytorch](https://github.com/qubvel-org/segmentation_models.pytorch) (SMP) | | **Training Framework** | PyTorch Lightning | | **Input** | 3-channel RGB, 512x512 tiles | | **Output** | 2-class mask (Background=0, Building=1) | | **Parameters** | ~20.8M | | **Model Size** | ~84 MB | ## Performance Evaluated on the WHU Building Dataset test split (1,228 tiles): | Metric | Score | |--------|-------| | **IoU** | 0.9054 | | **Dice** | 0.9503 | | **Best Val IoU** | 0.9434 | ## Training Details - **Dataset**: WHU Building Dataset — 5,732 training tiles (512x512 RGB at 0.3m resolution) - **Validation split**: 20% of training data - **Optimizer**: AdamW (lr=1e-4, weight_decay=1e-4) - **Loss**: CrossEntropyLoss - **Epochs**: 36 (early stopping, patience=10) - **Batch size**: 16 - **GPU**: NVIDIA RTX 6000 Ada (48GB) - **Encoder weights**: ImageNet pretrained ## Quick Start ### Installation ```bash pip install geoai-py timm segmentation-models-pytorch ``` ### Inference with GeoAI ```python import geoai # Run building detection on a GeoTIFF geoai.timm_segmentation_from_hub( input_path="input_image.tif", output_path="building_prediction.tif", repo_id="giswqs/whu-building-unetplusplus-efficientnet-b4", window_size=512, overlap=256, batch_size=4, ) # Vectorize to building footprints gdf = geoai.orthogonalize( input_path="building_prediction.tif", output_path="building_footprints.geojson", epsilon=2.0, ) ``` ### Manual Loading ```python import json import torch import segmentation_models_pytorch as smp # Load config with open("config.json") as f: config = json.load(f) # Create model model = smp.UnetPlusPlus( e...
Source context: 740 downloads · 5 likes · Pipeline image-segmentation · Library segmentation-models-pytorch · Repo giswqs/whu-building-unetplusplus-efficientnet-b4