| ---
|
| license: agpl-3.0
|
| library_name: pytorch
|
| inference: false
|
| datasets:
|
| - CRASAR/CRASAR-U-DROIDs
|
| base_model:
|
| - timm/convnextv2_nano.fcmae_ft_in22k_in1k
|
| tags:
|
| - remote-sensing
|
| - building-damage-assessment
|
| - disaster-response
|
| - suas
|
| - ordinal-classification
|
| - convnextv2
|
| ---
|
|
|
| # Mask Centered Damage Net (MCDN)
|
|
|
| [](https://www.python.org/)
|
| [](https://pytorch.org/)
|
| [](https://github.com/MobileSensorLab/MCDN)
|
| [](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs)
|
|
|
| [](https://www.gnu.org/licenses/agpl-3.0)
|
|
|
| Mask Centered Damage Net (MCDN) is a ~33.5M-parameter unitemporal structural damage classifier designed for use on sUAS
|
| orthomosaics, built on a [ConvNeXt v2 Nano](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) backbone.
|
| It grades individual structures on the four-level Joint Damage Scale (*No Damage*, *Minor*, *Major*, *Destroyed*) using only
|
| a post-disaster sUAS orthomosaic and a cache of building footprints, with no pre-disaster imagery.
|
|
|
| Most automated damage assessment is bitemporal: a Siamese network compares pre- and post-event satellite imagery and grades
|
| the change. Applied to sUAS post-disaster imagery, that approach assumes a pre-event raster can be delivered to the point of
|
| analysis, which a disaster zone with degraded communications generally cannot do, and would struggle to compare a sub-5 cm/px
|
| sUAS orthomosaic to a satellite (30–80 cm/px) or crewed (15–30 cm/px) prior. MCDN replaces the pre-event raster with the
|
| structure footprint, a vector prior small enough to cache on a field laptop in advance and already available for most of the
|
| built world. The footprint localizes, centering each input chip on the structure being evaluated, and directs attention: the
|
| rasterized mask is ingested as a fourth input channel and separately weights the model's spatial pooling, so features under
|
| the roof dominate the pooled representation and surrounding debris contributes less. A FiLM gate conditions the head on
|
| disaster typology, and a squared Earth Mover's Distance loss preserves the ordinal structure of the grades.
|
|
|
| This repository holds the trained weights. Code, evaluation scripts, per-seed metrics, and the full README live in the
|
| [MCDN repository](https://github.com/MobileSensorLab/MCDN); the training data is
|
| [CRASAR/CRASAR-U-DROIDs](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs).
|
|
|
| ## Headline performance
|
|
|
| MCDN was evaluated on four holdouts: the CRASAR-U-DROIDs default train/test split, whose test events are all absent from
|
| training, and three Leave One Event Out (LOEO) holdouts that each withhold a single event. Per-seed columns are mean ± SD over
|
| the ten seeds published here; ensemble columns average those ten networks under 8-view D4 test-time augmentation.
|
|
|
| | Holdout | Per-seed QWK | Per-seed Macro-F1 | Ensemble QWK | Ensemble Macro-F1 |
|
| |---|:---:|:---:|:---:|:---:|
|
| | DROIDs default split | 0.865 ± 0.002 | 0.768 ± 0.007 | **0.869** | **0.774** |
|
| | LOEO Hurricane Michael | 0.861 ± 0.005 | 0.779 ± 0.005 | **0.861** | **0.779** |
|
| | LOEO Mayfield Tornado | 0.849 ± 0.007 | 0.751 ± 0.014 | **0.862** | **0.768** |
|
| | LOEO Hurricane Ida | 0.752 ± 0.010 | 0.686 ± 0.008 | **0.762** | **0.693** |
|
|
|
| 
|
|
|
| Three of the four holdouts fall within 0.01 of one another. Hurricane Ida is the exception, and its deficit traces to a single
|
| class boundary: most of Ida's severe errors are structures with intact roofs surrounded by storm surge, labeled *Minor* or
|
| *Major* for damage to the interior and lower structure that a nadir roof view does not show, so the model under-grades them as
|
| *No Damage*. Across all holdouts, errors concentrate on adjacent grades; on the default split, 727 of 820 errors are one grade
|
| off while only 93 (2.4% of structures) are two or more.
|
|
|
| A single network without test-time augmentation scores within 0.02 QWK of the ten-seed ensemble on every holdout; ensembling
|
| contributes seed-to-seed stability more than accuracy. On a desktop RTX 5090 a single network grades about 830 structures per
|
| second (108 with TTA) and the full 80-pass ensemble just under 10, with peak GPU memory at or below 3.3 GB.
|
|
|
| ## What is here
|
|
|
| 370 `best_model.pt` files across 10 ablation arms and 37 arm/holdout cells
|
| (42.9 GB), ten fixed seeds per cell (0, 11, 22, 33, 44, 55, 66, 77, 88, 99). Each seed directory also
|
| carries the `config_resolved.yaml` the run trained under, with its run-bookkeeping paths (`data.dir`, `checkpoint_root`)
|
| normalized to the code repository's layout; every other field is verbatim.
|
|
|
| ```
|
| <arm>/<holdout>/seed_<NN>/best_model.pt
|
| <arm>/<holdout>/seed_<NN>/config_resolved.yaml
|
| MANIFEST.json # every file with size and SHA-256
|
| MANIFEST.sha256 # sha256sum -c compatible
|
| ```
|
|
|
| Holdout directories name the event(s) withheld from training: `Hurricane_Ida`, `Hurricane_Michael`, `Mayfield_Tornado`, and
|
| the dataset's default test split `Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire`.
|
|
|
| ### Arms
|
|
|
| C = footprint input channel, P = mask-weighted pooling, T = typology FiLM. Directory names follow the training presets, which
|
| name the component *removed*; the label column follows the paper.
|
|
|
| | Directory | Paper label | Components | Holdouts | Checkpoints |
|
| |---|---|---|---|---|
|
| | `all_features` | Full configuration | C+P+T | all 4 | 40 |
|
| | `pooling_typology` | Pooling + typology | P+T | all 4 | 40 |
|
| | `typology` | No typology | C+P | all 4 | 40 |
|
| | `pooling_only` | Pooling only | P | all 4 | 40 |
|
| | `mask_channel_only` | Channel only | C | all 4 | 40 |
|
| | `mask` | Typology only | T | all 4 | 40 |
|
| | `rgb_only` | RGB only | none | all 4 | 40 |
|
| | `ce_loss` | Cross-entropy loss | C+P+T | all 4 | 40 |
|
| | `no_smoothing` | No label smoothing | C+P+T | all 4 | 40 |
|
| | `resolution` | Crewed-aircraft imagery (15–30 cm/px), full configuration | C+P+T | `Hurricane_Michael` | 10 |
|
|
|
| ## Fetching
|
|
|
| From a clone of the code repository, which places files where the evaluation scripts expect them, verifies every checkpoint
|
| against `MANIFEST.json`, and skips files already present:
|
|
|
| ```bash
|
| python -m scripts.fetch_checkpoints --list
|
| python -m scripts.fetch_checkpoints --arm all_features --fold Hurricane_Michael
|
| python -m scripts.fetch_checkpoints --all
|
| ```
|
|
|
| Or directly:
|
|
|
| ```python
|
| from huggingface_hub import snapshot_download
|
|
|
| snapshot_download("mobilesensorlab/mcdn", allow_patterns=["all_features/Hurricane_Michael/*"], local_dir="outputs/ablation")
|
| ```
|
|
|
| Reproducing the published metrics needs the dataset's sUAS test pool (~66 GB) alongside the weights; the code repository's
|
| `scripts.fetch_dataset --sensor uas --split test` retrieves exactly that.
|
|
|
| ## Loading
|
|
|
| Checkpoints are PyTorch state dicts for `src.model.mcdn.MCDN`; build the model from the seed's `config_resolved.yaml` and call
|
| `load_state_dict`. See `src/postproc/ensemble.py` in the code repository for the ten-seed, eight-view test-time-augmentation
|
| ensemble used for every reported number, and `scripts/profile_mcdn_inference.py` for single-network inference.
|
|
|
| ## Citation
|
|
|
| The accompanying manuscript, *Mask Centered Damage Net: Building Damage Classification from Unitemporal sUAS Imagery and
|
| Footprint Priors* (A. Kaplan and E. Best, Mobile Sensor Lab, University at Albany), is under review at IEEE JSTARS. Until it
|
| is published, please cite this repository and the [code repository](https://github.com/MobileSensorLab/MCDN); a DOI and BibTeX entry will be added on
|
| acceptance.
|
|
|
| ## License
|
|
|
| MCDN and its checkpoints licensed under the GNU Affero General Public License v3.0.
|
|
|
