Our paper "Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion" can be found here, and our presentation in the AI for Earth Sciences workshop at ICLR 2020 is located on this page.
April 11, 2020: Note that some of these instructions may be outdated due to the nature of the competition, but we are in the process of cleaning up code for reproducability. Training and testing will work for semantic segmentation, but the capibilities for instance segmentation with COCO data, as well as some handy visualizations, need to be better documented. Stay tuned, and reach out to [email protected] or [email protected] for details.
This is the codebase used for our xview2 submission, which received 2nd place in Track 3: "Evaluation Only". On the leaderboard (https://xview2.org/challenge), our final submission was ranked 40 before unverified code was removed. The project is built on top of the detectron2 repo by Facebook. The goal of this project is to do building damage assessment with before/after image pairs. We use a model to utilize this multi-temporal information. The prediction of our network is a 5-channel pixel-wise damage level prediction:
- 0: no building
- 1: undamaged building
- 2: building with minor damage
- 3: building with major damage
- 4: destroyed building
More specifics on the segmentation problem can be found at https://xview2.org/challenge.
Start by cloning the repo: git clone --recurse-submodules -j8 [email protected]:ethanweber/xview2.git. Then, follow this in detail for installing dependencies: https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md. Install other packages as neeeded. A conda environment is convenient to manage everything.
Download data from the xview2 site: https://xview2.org/download. Store the in the data folder, as decribed below. We do not use the "holdout set" in our work.
- data/
- original_train/ (original w/o tier3)
- test/ (test data)
- train/ (combined w/ tier3)
- train_gt/
- train_images_quad/
Notice that the folders should be named in this format, where data/train contains both the "training set" and "additional tier3 training data". We use "train" for the experiments in our work.
We store configs in the following format.
- configs/xview
- *.yaml configs to use with detectron2
Our best config is located at configs/xview/joint-11.yaml. In this file, we see the following configuation:
_BASE_: "../Base-RCNN-FPN.yaml"
DATASETS:
TRAIN: ("xview_semantic_damage_quad_train",)
TEST: ("xview_semantic_damage_quad_val",)
INPUT:
MIN_SIZE_TRAIN: (512,)
MAX_SIZE_TRAIN: 512
MIN_SIZE_TEST: 0
MODEL:
META_ARCHITECTURE: "SemanticSegmentor"
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
SEM_SEG_HEAD:
NUM_CLASSES: 5
RESNETS:
DEPTH: 50
SOLVER:
IMS_PER_BATCH: 8
BASE_LR: 0.01
STEPS: (210000, 250000)
MAX_ITER: 270000
OUTPUT_DIR: "./outputs/output_joint_11"
TEST:
EVAL_PERIOD: 5000
xview_semantic_damage_quad_* is the training and validation set used while training the model. This consists of pre/post images and their semantic segmentation ground truth labels. Notice that we use 512 as the image size, which is smaller than the original 1024 x 1024 images in originally downloaded xBD dataset. See notebooks/NB_make_quad_folder.ipynb to create new this dataset, which is the origal dataset but split into quadrants for higher resolution.
Look at detectron2_repo/detectron2/data/datasets/builtin.py, where the datasets are registered by name. It's crucial the data exists where specified in the data folder. Note that this codebase originally reformated xBD annotations to COCO, to use instance segmentation, but we've moved away from this and switched to semenatic segmentation. The code is not maintained for COCO, but some notebook files demonstrate creating this data, such as notebooks/NB_create_xview_data.ipynb and notebooks/NB_visualize_xview_coco_data.ipynb.
Go to the main directory and run a training process.
Example execution with one GPU. This is for the baseline localization model.
cd xview
export CUDA_VISIBLE_DEVICES=0
python detectron2_repo/tools/train_net.py --config-file configs/xview/joint-11.yaml
To run from a checkpoint: (make sure path to checkpoint is correct)
python detectron2_repo/tools/train_net.py --config-file configs/xview/joint-11.yaml MODEL.WEIGHTS "outputs/output_joint_11/model_0054999.pth"
We compute the metrics used by xview2 and display them in Tensorboard during training. Original code for the metrics is located at detectron2_repo/detectron2/evaluation/xview_evaluation.py. Notice that intermediate results during training are stored in cfg.OUTPUT_DIR/PRED_DIR, cfg.OUTPUT_DIR/PRED_DIR, and cfg.OUTPUT_DIR/PRED_DIR, where cfg.OUTPUT_DIR is specified as in the .yml config above.
Use notebooks/NB_create_submission_from_model-quad.ipynb to create the submission.
These two folders will be made from the script.
mkdir SUBMISSION_LOCALIZATION
mkdir SUBMISSION_DAMAGE
Then create a .zip folder containing all the images (in both folders) and submit to xview2. It will be marked by a timestamp. Upload this diretly to the xview website.
Use notebooks/NB_visualize_submission_folder.ipynb file to look at some of the predictions in your most recent submission folder.
# avoid too many files open errors
ulimit -n 4096
# start a notebook
jupyter notebook --ip 0.0.0.0 --port 8888 --no-browser
# simple fix to pycocotools
https://github.com/cocodataset/cocoapi/issues/49
# activate conda
eval "$(conda shell.bash hook)" && conda activate xview
# ssh / github issues
eval "$(ssh-agent -s)"
ssh-add ~/.ssh/id_rsa_personal
# transfer learning
https://github.com/facebookresearch/detectron2/issues/222
