Authors: Li, Hao, Deuser, Fabian, Yin, Wenping, Luo, Xuanshu, Walther, Paul, Mai, Gengchen, Huang, Wei and Werner, Martin

CVDisaster is a novel mapping framework that can addressing geolocalization and damage perception estimation using cross-view Street-View Imagery (SVI) and Very High-Resolution satellite imagery at the same time.

CVDisaster consists of two cross-view models:

• CVDisaster-Geoloc is a cross-view geolocalization model based on a contrastive learning objective with a Siamese ConvNeXt image encoder (Sample4Geo );
• CVDisaster-Est is a cross-view classification model based on a Couple Global Context Vision Transformer (based on CGCViT).

Before training, one need to download the CVIAN dataset from: doi:10.14459/2024mp1749324

1. Install the requirement.txt

   pip install -r ./geolocalization/requirement.txt

2. Put the CVIAN dataset into ./geolocalization. You should have:

    geolocalization
    ├── 0_prepare_splits_and_data.py
    ├── 1_train_cvdisaster.py
    ├── 2_eval_cvdisaster.py
    ├── requirement.txt
    ├── ...
    ├── CVIAN
    │   ├── 00_SVI
    │   │   ├── 0_MinorDamage
    │   │   ├── 1_ModerateDamage
    │   │   └── 2_SevereDamage
    │   ├── 01_Satellite
    │   │   ├── 0_MinorDamage
    │   │   ├── 1_ModerateDamage
    │   │   └── 2_SevereDamage
    │   └── 02_Position
    │       ├── CVIAN_position.geojson
    │       └── CVIAN_position_shapefile
    └── ...
   

3. Generate the splits and pre-process the images:

   python ./geolocalization/0_prepare_splits_and_data.py

4. Training the model:

   python ./geolocalization/1_train_cvdisaster.py

   Herein, we use a pre-trained Sample4Geo Model please download the CVUSA weights from the repository

5. Evaluate the model

   python ./geolocalization/2_eval_cvdisaster.py

Again specify the split you want to evaluate, also change the checkpoint_start parameter in the dataclass to the path of the trained weights.

1. Put the CVIAN dataset into ./disaster_perception_mapping. You should have:

    disaster_perception_mapping
    ├── 1_sat.py
    ├── 1_svi.py
    ├── 2_cv.py
    ├── 3_estimation.py
    ├── CVIAN
    │   ├── 00_SVI
    │   │   ├── 0_MinorDamage
    │   │   ├── 1_ModerateDamage
    │   │   └── 2_SevereDamage
    │   ├── 01_Satellite
    │   │   ├── 0_MinorDamage
    │   │   ├── 1_ModerateDamage
    │   │   └── 2_SevereDamage
    │   └── 02_Position
    │       ├── CVIAN_position.geojson
    │       └── CVIAN_position_shapefile
    └── ...
   

2. Prepare docker container

   cd disaster_perception_mapping
   docker run -it --gpus device=1 --name gcvit --mount type=bind,source="$(pwd)",target=/root tensorflow/tensorflow:2.10.1-gpu

3. Install gcvit inside the container

   python -m pip install --upgrade pip
   python -m pip install gcvit tensorflow_addons geojson rasterio scikit-learn --root-user-action=ignore  # anyway, we are in docker ;)

4. Go to directory

   cd ~

• 1_svi.py and 1_sat.py will generate single-view results (GCViTTiny, 5/5 split).

• 2_cv.py will generate cross-view results (GCViTTiny). You can also change backbones of the model and specify training ratio using command-line options. For example,

  python 2_cv.py --tr-ratio 0.5 --backbone tiny

  means using GCViT Tiny, and train/test split ratio is 0.5.

• 3_estimation.py can create a text file where each line contains the estimation results, ground truth and corresponding images (ID).

CVIAN is a cross-view dataset to support geolocalization and disaster mapping with street-view and very high resolution (VHR) satellite imagery in Florida, USA after Hurricane IAN in 2022. The dataset is openly availble at doi:10.14459/2024mp1749324.

CVIAN contains 4,121 pairs of street-view and VHR satellite imagery, which are manually classified into 3 classes (i.e., light, medium, and heavy damage). The VHR satellite imagery was originally provided by the National Oceanic and Atmospheric Administration (NOAA) at a spatial resolution of 30cm per pixel on September 30, 2022. The street-view imagery was collected from the open-source Mapilliary platform, speicifally from a mapping campaign done by Site Tour 360 after Hurricane IAN hit the study area.

CVIAN is the first of this kind dataset that can support both cross-view geolocalization and disaster mapping at the same time.

If you find our work useful in your research please consider citing the CVDisaster paper.

@article{li2024cvdisaster,
     title={Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN},
     author={Li, Hao and Deuser, Fabian and Yin, Wenping and Luo, Xuanshu and Walther, Paul and Mai, Gengchen and Huang, Wei and Werner, Martin},
     year={2024},
     eprint={2408.06761},
     archivePrefix={arXiv},
     primaryClass={cs.CV},
     url={https://arxiv.org/abs/2408.06761},
}

Please go to Dr. Hao Li's Homepage for more information.