A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery
This repository is the official implementation of A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery by Aatif Jiwani, Shubhrakanti Ganguly, Chao Ding, Nan Zhou, and David Chan.
Requirements
- To install GDAL/
georaster, please follow this doc for instructions. - Install other dependencies from requirements.txt
pip install -r requirements.txt
Datasets
Downloading the Datasets
- To download the AICrowd dataset, please go here. You will have to either create an account or sign in to access the training and validation set. Please store the training/validation set inside
<root>/AICrowd/<train | val>for ease of conversion. - To download the Urban3D dataset, please run:
aws s3 cp --recursive s3://spacenet-dataset/Hosted-Datasets/Urban_3D_Challenge/01-Provisional_Train/ <root>/Urban3D/train
aws s3 cp --recursive s3://spacenet-dataset/Hosted-Datasets/Urban_3D_Challenge/02-Provisional_Test/ <root>/Urban3D/test
- To download the SpaceNet Vegas dataset, please run:
aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/SN2_buildings_train_AOI_2_Vegas.tar.gz <root>/SpaceNet/Vegas/
aws s3 cp s3://spacenet-dataset/spacenet/SN2_buildings/tarballs/AOI_2_Vegas_Test_public.tar.gz <root>/SpaceNet/Vegas/
tar xvf <root>/SpaceNet/Vegas/SN2_buildings_train_AOI_2_Vegas.tar.gz
tar xvf <root>/SpaceNet/Vegas/AOI_2_Vegas_Test_public.tar.gz
Converting the Datasets
Please use our provided dataset converters to process the datasets. For all converters, please look at the individual files for an example of how to use them.
- For AICrowd, use
datasets/converters/cocoAnnotationToMask.py. - For Urban3D, use
datasets/converters/urban3dDataConverter.py. - For SpaceNet, use
datasets/converters/spaceNetDataConverter.py
Creating the Boundary Weight Maps
In order to train with the exponentially weighted boundary loss, you will need to create the weight maps as a pre-processing step. Please use datasets/converters/weighted_boundary_processor.py and follow the example usage. The inc parameter is specified for computational reasons. Please decrease this value if you notice very high memory usage.
Note: these maps are not required for evaluation / testing.
Training and Evaluation
To train / evaluate the DeepLabV3+ models described in the paper, please use train_deeplab.sh or test_deeplab.sh for your convenience. We employ the following primary command-line arguments:
| Parameter | Default | Description (final argument) |
|---|---|---|
| --backbone | resnet |
The DeeplabV3+ backbone (final method used drn_c42) |
| --out-stride | 16 | The backbone compression facter (8) |
| --dataset | urban3d |
The dataset to train / evaluate on (other choices: spaceNet, crowdAI, combined) |
| --data-root | /data/ |
Please replace this with the root folder of the dataset samples |
| --workers | 2 | Number of workers for dataset retrieval |
| --loss-type | ce_dice |
Type of objective function. Use wce_dice for exponentially weighted boundary loss |
| --fbeta | 1 | The beta value to use with the F-Beta Measure (0.5) |
| --dropout | 0.1 0.5 |
Dropout values to use in the DeepLabV3+ (0.3 0.5) |
| --epochs | None | Number of epochs to train (60 for train, 1 for test) |
| --batch-size | None | Batch size (3/4) |
| --test-batch-size | None | Testing Batch Size (1/4) |
| --lr | 1e-4 |
Learning Rate (1e-3) |
| --weight-decay | 5e-4 |
L2 Regularization Constant (1e-4) |
| --gpu-ids | 0 |
GPU Ids (Use --no-cuda for only CPU) |
| --checkname | None | Experiment name |
| --use-wandb | False | Track experiment using WandB |
| --resume | None | Experiment name to load weights from (i.e. urban for weights/urban/checkpoint.pth.tar) |
| --evalulate | False | Enable this flag for testing |
| --best-miou | False | Enable this flag to get best results when testing |
| --incl-bounds | False | Enable this flag when training with wce_dice as a loss |
To train with the cross-task training strategy, you need to:
- Train a model using
--dataset=combineduntil the best loss has been achieved - Train a model using
--resume=<checkname>on one of the three primary datasets until the best mIoU is achieved
Pre-Trained Weights
We provide pre-trained model weights in the weights/ directory. Please use Git LFS to download these weights. These weights correspond to our best model on all three datasets.
Results
Our final model is a DeepLavV3+ module with a Dilated ResNet C42 backbone trained using the F-Beta Measure + Exponentially Weighted Cross Entropy Loss (Beta = 0.5). We employ the cross-task training strategy only for Urban3D and SpaceNet.
Our model achieves the following:
| Dataset | Avg. Precision | Avg. Recall | F1 Score | mIoU |
|---|---|---|---|---|
| Urban3D | 83.8% | 82.2% | 82.4% | 83.3% |
| SpaceNet | 91.4% | 91.8% | 91.6% | 90.2% |
| AICrowd | 96.2% | 96.3% | 96.3% | 95.4% |
Acknowledgements
We would like to thank jfzhang95 for his DeepLabV3+ model and training template. You can access this repository here
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