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Whu Building Unetplusplus Efficientnet B4

A semantic segmentation model for building detection in high-resolution aerial imagery, trained on the WHU Building Dataset.

Sourcehuggingface.co/giswqs/whu-building-unetplusplus-efficientnet-b4/tree/09df9efd323bbd3d56b98b4857129eb9b5baa2d3
KindOtherVersionRevision 09df9ef

Model source

Kind
Other
File size
80.1 MB
Version
vRevision 09df9ef
Updated
Sep 27, 2026
Downloads
740
Tasks
Image segmentation
Source
Hugging Face

Source description

WHU Building Detection — EfficientNet-B4 + UNet++

A semantic segmentation model for building detection in high-resolution aerial imagery, trained on the WHU Building Dataset.

Model Description

PropertyValue
ArchitectureUNet++
EncoderEfficientNet-B4 (ImageNet pretrained)
Frameworksegmentation-models-pytorch (SMP)
Training FrameworkPyTorch Lightning
Input3-channel RGB, 512x512 tiles
Output2-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):

MetricScore
IoU0.9054
Dice0.9503
Best Val IoU0.9434

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

pip install geoai-py timm segmentation-models-pytorch

Inference with GeoAI

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

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()

Example Notebook

See the full inference notebook with visualization and analysis:

Open In Colab

Dataset

The WHU Building Dataset consists of aerial imagery at 0.3m resolution with binary building masks:

  • Train: 5,732 tiles (512x512 RGB)
  • Val: 1,228 tiles
  • Test: 1,228 tiles

Reference

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.

License

This model is released under the Apache 2.0 License.

Links

Sources

1 source
Hugging FacePrimaryVersion 09df9efd323bbd3d56b98b4857129eb9b5baa2d3
huggingface.co/giswqs/whu-building-unetplusplus-efficientnet-b4/tree/09df9efd323bbd3d56b98b4857129eb9b5baa2d3

Verified Sep 27

Model artifacts

1 artifact

model.pth

pth · 80.1 MB · SHA-256 922af7c96c0d…0dc6 · Hugging Face

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Source excerpts

2 excerpts
ReadmeSep 27huggingface.co/giswqs/whu-building-unetplusplus-efficientnet-b4

--- 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...

NoteSep 27huggingface.co/giswqs/whu-building-unetplusplus-efficientnet-b4

Source context: 740 downloads · 5 likes · Pipeline image-segmentation · Library segmentation-models-pytorch · Repo giswqs/whu-building-unetplusplus-efficientnet-b4