3D-CiLBE : 3D City Long-term Biomass Estimating

This project repository is based on papers in Remote Sensing journals: "Estimating Urban Forests Biomass with LiDAR by Using Deep Learning Foundation Models" authored by Hanzhang Liu, Chao Mou*, Jiateng Yuan, Zhibo Chen, Liheng Zhong, and Xiaohui Cui.

1. Data Prepare

The relevant data used by the model is available through an open platform.

• LiDAR : https://apps.nationalmap.gov/lidar-explorer/
• Single Tree Images (Point Cloud) : https://doi.org/10.25625/FOHUJM
• OSM : https://openmaptiles.org/

2. Structure

The code repository includes the following directory structure:

3DCiLBE_method/
├── LiDAR-SAM/ # Segmentation model is used to segment LiDAR data
├── MLiDAR-CLIP/ # A model for species identification of single trees
├── St-Informer/ # Time series prediction model
└── rangeprojection/ # Module for range projection of LiDAR data

3. Model Environment

(1) LiDAR-SAM

• python>=3.8
• pytorch>=1.7
• torchvision>=0.8

(2) MLiDAR-CLIP

• pytorch=1.7.1
• cudatoolkit=11.0

(3) St-Informer

• python>=3.8
• cudatoolkit=11.0
• matplotlib == 3.1.1
• numpy == 1.19.4
• pandas == 0.25.1
• scikit_learn == 0.21.3
• torch == 1.8.0

4. Usage

To start with the training or running, use the following commands:

python train.py # Model training
python setup.py # Model running

5. CheckPoint

The model files are identified by name and only apply to the urban areas mentioned in the article.

• Model Link
• sam_vitb : The model is suitable for completing range projection of LiDAR data and segmentation. Based on OpenAI SAM
• sam_vitl : The model is suitable for completing range projection of LiDAR data and segmentation. Based on OpenAI SAM
• mlidarclip10A ：The model is used to classify two-dimensional vegetation images. Model based on CLIP RN50
• mlidarclipRN50 : The model is used to classify two-dimensional vegetation images. Model based on CLIP RN50*4
• mlidarclipvit : The model is used to classify two-dimensional vegetation images. Model based on CLIP ViT-B/32

6. Citation

@article{Liu2024EstimatingUF,
  title={Estimating Urban Forests Biomass with LiDAR by Using Deep Learning Foundation Models},
  author={Hanzhang Liu and Chao Mou and Jiateng Yuan and Zhibo Chen and Liheng Zhong and Xiao-Ting Cui},
  journal={Remote Sensing},
  year={2024},
  url={https://api.semanticscholar.org/CorpusID:269704888}
}