HVL-ML entry in the 2022 NORA MapAI challenge

Team

Satheshkumar Kaliyugarasan and Alexander S. Lundervold

Department of Computer Science, Electrical Engineering and Mathematical Sciences, Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway.

Introduction

The figure show's one of our model's predictions on validation data from an external dataset used for pretraining.

Citing

S. Kaliyugarasan, and A. S. Lundervold. LAB-Net: Lidar and aerial image-based building segmentation using U-Nets. Nordic Machine Intelligence (2023).

BibTeX entry:

@article{kaliyugarasan2022lab,
  title={{LAB-Net}: Lidar and aerial image-based building segmentation using {U-Nets}},
  author={Kaliyugarasan, Satheshkumar and Lundervold, Alexander Selvikv{\aa}g},
  journal={Nordic Machine Intelligence},
  volume={2},
  number={3},
  year={2023}
}

Our approach

See also our team's code in the competiton repo: https://github.com/Sjyhne/MapAI-Competition (team_hvlml).

Details TBA

Data preparation

Modelling

Deployment

Results

Add results on validation data

Setup

If you only want to produce predictions on new data, then you can install our inference environment by following the instructions below. However, if you're going to re-run or modify the training process, please install the libraries in our more extensive training environment.

Click for installation instructions

Inference environment

Clone the repo:

git clone https://github.com/skaliy/MapAI_challenge

Create conda environment:

cd MapAI_challenge
conda env update -f environment-inference.yml

Training environment

Clone the repo and its submodules:

git clone --recurse-submodules https://github.com/skaliy/MapAI_challenge

Create conda environment:

cd MapAI_challenge
conda env update -f environment-training.yml
conda activate mapai

Install PyTorch:

Follow the instructions at https://pytorch.org/get-started/locally/. E.g.,

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

Install fastai, 🤗 datasets, and kornia:

conda install -c fastchan fastai
pip install datasets
pip install kornia

Install the data augmentation submodule:

pip install -e 'semantic_segmentation_augmentations[dev]'

Table of contents

Note that the TOC is a bit outdated.

Data preparation

Notebook	Description
00a_mapai_prepare_data.ipynb	Loads the MapAI data and computes and stores information about which images the "ground truth" masks indicate have buildings and to what extent.
00b_inria_prepare_data.ipynb	Loads and extracts images patches from the INRIA dataset used for pretraining

Data cleaning

Notebook	Description
01a_classifier.ipynb	Trains a building detection classifier used to discover mislabeled data.
01b_inspect_diff.ipynb	Our filtering process to find mislabeled images is only partially automatic. This notebook contains code for a manual step investigating possible mislabels.
01c_manual_find_error.ipynb	Code for a manual step investigating possible mislabels.
01d_segmentation_cleaning.ipynb	We repeat the above filtering process to discover even more mislabeled data, but this time using a segmentation model.
01e_segmentation-pretraining-cleaning.ipynb	Use the pretrained segmentation model to filter mislabeled data

Segmentation

Notebook	Description
02a_segmentation-pretraining.ipynb	Pretrains our segmentation models on the INRIA dataset described above.
02b_segmentation-pretraining-evaluate.ipynb	Evaluate the pretrained model. Visualize predicted results.
02d_segmentation-aerial.ipynb	Fine-tunes the above model on the MapAI data
02g_segmentation-aerial-lidar.ipynb	Trains a segmentation model on the lidar data
02j_segmentation_aerial-lidar_create_visualizations_get_info_ensemble.ipynb

Inference on new data

Notebook	Description
03a_inference_aerial.ipynb	Inference on new aerial images using our top-performing model ensemble.
03b_inference_lidar.ipynb	Inference on new lidar data using our top-performing model ensemble.