# AnyDisasterMapping **Repository Path**: geok/AnyDisasterMapping ## Basic Information - **Project Name**: AnyDisasterMapping - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-05-14 - **Last Updated**: 2026-06-17 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

🌍Any Disaster Mapping

Earth Observation for Disaster Mapping: Benchmarks, Methods, Challenges and Future Perspectives

[Hongruixuan Chen](https://scholar.google.com/citations?user=XOk4Cf0AAAAJ)1,†, [Jian Song](https://scholar.google.com/citations?user=fx-27bQAAAAJ)1,†, [Weihao Xuan](https://scholar.google.com/citations?user=7e0W-2AAAAAJ)2,1,†, [Junjue Wang](https://scholar.google.com/citations?user=H58gKSAAAAAJ)2,†, [Heli Qi](https://scholar.google.com/citations?user=CH-rTXsAAAAJ)1, [Zeqi Zhou](https://scholar.google.com/citations?user=pqG03s0AAAAJ)3, [Pengyu Dai](https://scholar.google.com/citations?user=bxKhSZgAAAAJ)1,2
[Olivier Dietrich](https://scholar.google.com/citations?user=st6IqcsAAAAJ)4, [Erika Gutierrez](https://www.google.com/search?q=Erika+Gutierrez&oq=Erika+Gutierrez&gs_lcrp=EgRlZGdlKgYIABBFGDsyBggAEEUYOzIGCAEQABgeMgYIAhAAGB4yBggDEAAYHjIGCAQQABgeMgYIBRAAGB4yBggGEAAYHjIGCAcQRRg8MgcICBDrBxhA0gEHMjkxajBqNKgCALACAA&sourceid=chrome&ie=UTF-8)5, [Lars Bromly](https://www.google.com/search?q=Lars+Bromly&oq=Lars+Bromly&gs_lcrp=EgRlZGdlKgYIABBFGDsyBggAEEUYOzIJCAEQABgNGIAEMggIAhAAGA0YHjIKCAMQABiABBiiBDIHCAQQABjvBTIHCAUQABjvBTIKCAYQABiABBiiBDIGCAcQRRg70gEHMjQ0ajBqN6gCALACAA&sourceid=chrome&ie=UTF-8)5, [Edoardo Nemni](https://scholar.google.com/citations?user=eUo-LuAAAAAJ)6, [Yafei Ou](https://scholar.google.com/citations?user=2SyrQ1oAAAAJ)1, [Jie Zhao](https://scholar.google.com/citations?user=snOLm2MAAAAJ)7, [Zhuo Zheng](https://scholar.google.com/citations?user=CREpn_AAAAAJ)8, [Yonghao Xu](https://scholar.google.com/citations?user=sQs2ztAAAAAJ)9
[Ronny Hänsch](https://scholar.google.com/citations?user=mUlpUlUAAAAJ)10, [Wenzhe Jiao](https://scholar.google.com/citations?user=1v9ooFUAAAAJ)11, [Marco Chini](https://www.researchgate.net/profile/Marco-Chini)12, [Claudio Persello](https://scholar.google.com/citations?user=CI3bxVMAAAAJ)13, [Junshi Xia](https://scholar.google.com/citations?user=n1aKdTkAAAAJ)1, [Shijian Lu](https://scholar.google.com/citations?user=uYmK-A0AAAAJ)14, [Lixin Wang](https://scholar.google.com/citations?user=pQPtUesAAAAJ)15, [Zhe Zhu](https://scholar.google.com/citations?user=9ODFYW4AAAAJ)16
[Evan Shelhamer](https://scholar.google.com/citations?user=-ltRSM0AAAAJ&hl=zh-CN)17, [Jocelyn Chanussot](https://scholar.google.com/citations?user=6owK2OQAAAAJ)18, [Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ)4, [Naoto Yokoya](https://scholar.google.com/citations?user=DJ2KOn8AAAAJ)2,1 †Equal contribution
1 RIKEN AIP, 2 The University of Tokyo, 3 Brown University, 4 ETH Zurich, 5 United Nations Satellite Centre
6 Barcelona School of Economics, 7 Technical University of Munich, 8 Stanford University, 9 Linköping University
10 German Aerospace Center (DLR), 11 Texas A&M University, 12 Luxembourg Institute of Science and Technology
13 University of Twente, 14 Nanyang Technological University, 15 Indiana University Indianapolis
16 University of Connecticut, 17 The University of British Columbia, 18 Université Grenoble Alpes [**Paper**](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6725082) | [**Installation**](#installation) | [**Dataset Preparation**](#dataset-preparation) | [**Pretrained Weights**](#pretrained-weights) | [**Quick Start**](#quick-start) | [**Repo Layout**](#repository-layout)
## 🔭Overview *Any Disaster Mapping* is the official repository for our [review paper in EO-based disaster mapping](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6725082). One of our key motivations is that current disaster mapping research is highly fragmented: benchmarks, tasks, and model implementations are often inconsistent across papers, making fair evaluation, reproduction, and further development unnecessarily difficult. This repo unifies widely used disaster mapping benchmarks and representative deep learning models across major research directions, and provides a consistent training and evaluation pipeline for: - Infrastructure damage - Flood mapping - Landslide segmentation - Wildfire analysis It is designed to help researchers: - Reproduce the results reported in our paper - Evaluate models under a unified protocol - Use strong baselines out of the box - Build and test their own improvements with minimal engineering overhead ## 🛠️Installation Base environment and optional model-specific extras. ```bash # NOTE: --index-url should match the version of your local CUDA toolkit for compiling ChangeMamba kernels (cu126 is just an example) pip install torch torchvision xformers --index-url https://download.pytorch.org/whl/cu126 pip install -e . ``` Some models require optional extras: - ChangeMamba selective scan kernel: ```bash # run `conda install -c conda-forge gcc=13 gxx=13 -y` if you meet GCC issues cd src/models/ChangeMamba/kernels/selective_scan pip install . --no-build-isolation ``` - Local pretrained checkpoints under `pretrained_weight/` for model families such as SegFormer, HRNet, SAM/SAM2, DINOv3, HyperSigma, SkySense, SpectralGPT, and ChangeMamba. See [Pretrained Weights](#pretrained-weights) below. ## 🧪Dataset Preparation Dataset preparation guides are organized by disaster domain: - Infrastructure damage: [scripts/data_prep/infra_damage/README.md](scripts/data_prep/infra_damage/README.md) - Flood: [scripts/data_prep/flood/README.md](scripts/data_prep/flood/README.md) - Landslide: [scripts/data_prep/landslide/README.md](scripts/data_prep/landslide/README.md) - Wildfire: [scripts/data_prep/wildfire/README.md](scripts/data_prep/wildfire/README.md) ## 📦Pretrained Weights Recommended local checkpoint layout for the supported model zoo. Create the local checkpoint directory first: ```bash mkdir -p pretrained_weight ``` ### Direct Downloads ```bash # pretrain-vit-base-e199.pth wget -O pretrained_weight/pretrain-vit-base-e199.pth \ https://zenodo.org/records/7338613/files/pretrain-vit-base-e199.pth # SpectralGPT+.pth wget -O "pretrained_weight/SpectralGPT+.pth" \ "https://zenodo.org/records/8412455/files/SpectralGPT+.pth?download=1" # spec-vit-base-ultra-checkpoint-1599.pth wget -O pretrained_weight/spec-vit-base-ultra-checkpoint-1599.pth \ https://huggingface.co/WHU-Sigma/HyperSIGMA/resolve/main/spec-vit-base-ultra-checkpoint-1599.pth huggingface-cli download UTokyo-Yokoya-Lab/AnyDisaster-Pretrained_Weight \ vssm_tiny_0230_ckpt_epoch_262.pth --local-dir pretrained_weight --local-dir-use-symlinks False ``` ### Additional Upstream Checkpoints - DINOv3 Source: https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/ Download and save as: - `dinov3_vitb16_pretrain_lvd1689m-73cec8be.pth` (ViT-B/16, LVD-1689M) - `dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth` (ViT-L/16, LVD-1689M) - `dinov3_vitl16_pretrain_sat493m-eadcf0ff.pth` (ViT-L/16, SAT-493M) - SAM v1 Source: https://github.com/facebookresearch/segment-anything Download and save as: - `sam_vit_b_01ec64.pth` (SAM ViT-B) - `sam_vit_l_0b3195.pth` (SAM ViT-L) - SAM 2.1 Source: https://github.com/facebookresearch/sam2 Download and save as: - `sam2.1_hiera_small.pt` (SAM 2.1 Hiera-Small) - `sam2.1_hiera_base_plus.pt` (SAM 2.1 Hiera-Base+) - SegFormer MiT encoders Source: https://github.com/NVlabs/SegFormer Download and save as: - `mit_b0.pth` - `mit_b1.pth` - `mit_b2.pth` - `mit_b3.pth` - `mit_b4.pth` - `mit_b5.pth` - HyperSIGMA spatial backbone Source: https://huggingface.co/WHU-Sigma/HyperSIGMA Download the upstream file, rename, and save as: - `HSI_spatial_checkpoint-1600.pth` - SkySense backbone Source: - https://github.com/Jack-bo1220/SkySense - https://www.notion.so/SkySense-Checkpoints-a7fcff6ce29a4647a08c7fe416910509 Select the `hr` (high-resolution RGB / RGBNIR) variant, not the `s2` Sentinel-2 variant. Save as: - `skysense_model_backbone_hr.pth` For commercial use, contact the authors (`yansheng.li@whu.edu.cn`). After completing all downloads, `pretrained_weight/` should contain: ```text pretrained_weight/ ├── dinov3_vitb16_pretrain_lvd1689m-73cec8be.pth ├── dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth ├── dinov3_vitl16_pretrain_sat493m-eadcf0ff.pth ├── HSI_spatial_checkpoint-1600.pth ├── mit_b0.pth ├── mit_b1.pth ├── mit_b2.pth ├── mit_b3.pth ├── mit_b4.pth ├── mit_b5.pth ├── pretrain-vit-base-e199.pth ├── sam2.1_hiera_base_plus.pt ├── sam2.1_hiera_small.pt ├── sam_vit_b_01ec64.pth ├── sam_vit_l_0b3195.pth ├── skysense_model_backbone_hr.pth ├── spec-vit-base-ultra-checkpoint-1599.pth ├── SpectralGPT+.pth └── vssm_tiny_0230_ckpt_epoch_262.pth ``` ## 🚀 Quick Start Minimal training and evaluation commands. ### Train Train with a YAML config: ```bash python train.py --config configs/infra/xbd/unet.yaml ``` ### Evaluate Evaluate an experiment directory: ```bash python test.py --exp_path results/xbd/unet ``` ## 🗂️Repository Layout Main directories and what they are responsible for: - `src/core/`: trainer, config loader, registry, augmentation, metrics - `src/tasks/`: task handlers for segmentation, change detection, and semantic change detection - `src/datasets/`: dataset adapters and runtime data contracts - `src/models/`: model wrappers and vendored third-party implementations - `configs/`: experiment configs grouped by domain and dataset - `scripts/data_prep/`: dataset preparation guides and helper scripts - `docs/`: architecture notes and extension guidance ## 🏗️Architecture And Extension Internal runtime design and extension entry points. - Architecture overview: [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) - Extension guide: [docs/EXTENSION_GUIDE.md](docs/EXTENSION_GUIDE.md) ## 📜Reference If this code repo contributes to your research, please kindly consider citing our paper and give this repo ⭐️ :) ``` @misc{chen2026earthobservationdisastermapping, author = {Chen, Hongruixuan and Song, Jian and Xuan, Weihao and Wang, Junjue and Qi, Heli and Zhou, Zeqi and Dai, Pengyu and Dietrich, Olivier and Gutierrez, Erika and Bromly, Lars and Nemni, Edoardo and Ou, Yafei and Zhao, Jie and Zheng, Zhuo and Xu, Yonghao and H{\"a}nsch, Ronny and Jiao, Wenzhe and Chini, Marco and Persello, Claudio and Xia, Junshi and Lu, Shijian and Wang, Lixin and Zhu, Zhe and Shelhamer, Evan and Chanussot, Jocelyn and Schindler, Konrad and Yokoya, Naoto}, title = {Earth Observation for Disaster Mapping: Benchmarks, Methods, Challenges and Future Perspectives}, year = {2026}, month = {may}, note = {SSRN preprint}, doi = {10.2139/ssrn.6725082}, url = {https://ssrn.com/abstract=6725082} } ``` ## 🙋Q & A For any questions, please feel free to leave it in the issue section or send inquiry email to qschrx@gmail.com.