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MapFormer: Boosting Change Detection by Using Semantic Pre-change Information

This is the official codebase for the ICCV 2023 paper "MapFormer: Boosting Change Detection by Using Semantic Pre-change Information" by Maximilian Bernhard, Niklas Strauß, and Matthias Schubert.

Abstract

Change detection in remote sensing imagery is essential for a variety of applications such as urban planning, disaster management, and climate research. However, existing methods for identifying semantically changed areas overlook the availability of semantic information in the form of existing maps describing features of the earth's surface. In this paper, we leverage this information for change detection in bi-temporal images. We show that the simple integration of the additional information via concatenation of latent representations suffices to significantly outperform state-of-the-art change detection methods. Motivated by this observation, we propose the new task of Conditional Change Detection, where pre-change semantic information is used as input next to bi-temporal images. To fully exploit the extra information, we propose MapFormer, a novel architecture based on a multi-modal feature fusion module that allows for feature processing conditioned on the available semantic information. We further employ a supervised, cross-modal contrastive loss to guide the learning of visual representations. Our approach outperforms existing change detection methods by an absolute 11.7% and 18.4% in terms of binary change IoU on DynamicEarthNet and HRSCD, respectively. Furthermore, we demonstrate the robustness of our approach to the quality of the pre-change semantic information and the absence pre-change imagery. The code is available at https://github.com/mxbh/mapformer.

This repository is based on MMSegmentation and the FHD repository. Particularly, our main method components are implemented in ./mmseg/models/ccd/bc_heads.py.

Requirements

Python >= 3.9
See requirements.txt

Data Preparation

DynamicEarthNet

Download DynamicEarthNet here.
Structure the monthly data containing annotations in the following format:

├──./data/DynamicEarthNet/prepocessed/full/
│  ├──images/
│  │  ├──cube_id/
│  │  │  ├──2018-01-01.tif
│  │  │  ├──2018-02-01.tif
│  │  │  ...
│  │  │  └──2019-12-01.tif
│  │  ...
│  ├──labels/
│  │  ├──cube_id/
│  │  │  ├──2018-01-01.tif
│  │  │  ├──2018-02-01.tif
│  │  │  ...
│  │  │  └──2019-12-01.tif
│  │  ...
│  ├──splits/
│  │  │  ├──train35.txt
│  │  │  ├──val10.txt
│  │  │  └──test10.txt

Use ./tools/convert_datasets/create_dynearthnet_tiles.py to create tiles:

python ./tools/convert_datasets/create_dynearthnet_tiles.py \
  --data_dir=./data/DynamicEarthNet/prepocessed/ \
  --out_dir=./data/DynamicEarthNet/prepocessed/tiles500 \
  --tile_size=512

HRSCD

Download HRSCD here.
Structure the data in the following way:

├──./data/HRSCD/
│  ├──images/
│  │  ├──2006/
│  │  │  ├──D14/
│  │  │  │  ├──XXX.tif
│  │  │  │  ...
│  │  │  ├──D35/
│  │  │  │  ├──XXX.tif
│  │  │  │  ...
│  │  ├──2012/
│  │  │  ...
│  ├──labels/
│  │  ├──2006/
│  │  │  ├──D14/
│  │  │  │  ├──XXX.tif
│  │  │  │  ...
│  │  │  ├──D35/
│  │  │  │  ├──XXX.tif
│  │  │  │  │  ...
│  │  ├──2012/
│  │  │  ...
│  ├──change/
│  │  ├──D14/
│  │  │  ├──XXX.tif
│  │  │  ...
│  │  ├──D35/
│  │  │  ├──XXX.tif
│  │  │  ...

Use ./tools/convert_datasets/create_hrscd_tiles.py to create tiles:

python ./tools/convert_datasets/create_hrscd_tiles.py \
  --data_dir=./data/HRSCD/ \
  --out_dir=./data/HRSCD/preprocessed/tiles500 \
  --tile_size=500

# Used for faster inference, internally splitted into 500px tiles within the model.
python ./tools/convert_datasets/create_hrscd_tiles.py \
  --data_dir=./data/HRSCD/ \
  --out_dir=./data/HRSCD/preprocessed/tiles2000 \
  --tile_size=2000

Manually put the provided split files in ./data/HRSCD/preprocessed/tilesXXX/splits/ (e.g., via symbolic links).

Pre-trained Backbones

Download MiT-b2 weights from the official SegFormer repo and put them to ./model_ckpt/mit_b2.pth.
As the open-cd codebase uses the mmseg implementation of MiT, download the corresponding weights from openmmlab as well and put them to ./model_ckpt/mit_b2_20220624-66e8bf70.pth.

Training

Before training, install the requirements, download the pre-trained backbone weights, and prepare the datasets (see below). Then run:

python tools/train.py ./configs/conditional_bcd/dynamicearthnet/mapformer.yaml \
  --work-dir=./runs/conditional_bcd/dynamicearthnet/mapformer

For other experiments, simply replace the paths for other configs (see ./configs).

Testing

To get the results on the test set, run:

python tools/test.py ./path/to/config.yaml \
  ./path/to/checkpoint.pth \
  --eval BC BC_precision BC_recall SC SCS mIoU \
  --samples-per-gpu=1