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MCDN

PyTorch
remote-sensing
building-damage-assessment
disaster-response
suas
ordinal-classification
convnextv2
Model card Files
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MCDN checkpoints: 37 arm/holdout cells x 10 seeds, model card, manifest

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  1. .gitattributes +35 -0
  2. MANIFEST.json +0 -0
  3. MANIFEST.sha256 +740 -0
  4. README.md +142 -0
  5. all_features/Hurricane_Ida/seed_00/best_model.pt +3 -0
  6. all_features/Hurricane_Ida/seed_00/config_resolved.yaml +55 -0
  7. all_features/Hurricane_Ida/seed_11/best_model.pt +3 -0
  8. all_features/Hurricane_Ida/seed_11/config_resolved.yaml +55 -0
  9. all_features/Hurricane_Ida/seed_22/best_model.pt +3 -0
  10. all_features/Hurricane_Ida/seed_22/config_resolved.yaml +55 -0
  11. all_features/Hurricane_Ida/seed_33/best_model.pt +3 -0
  12. all_features/Hurricane_Ida/seed_33/config_resolved.yaml +55 -0
  13. all_features/Hurricane_Ida/seed_44/best_model.pt +3 -0
  14. all_features/Hurricane_Ida/seed_44/config_resolved.yaml +55 -0
  15. all_features/Hurricane_Ida/seed_55/best_model.pt +3 -0
  16. all_features/Hurricane_Ida/seed_55/config_resolved.yaml +55 -0
  17. all_features/Hurricane_Ida/seed_66/best_model.pt +3 -0
  18. all_features/Hurricane_Ida/seed_66/config_resolved.yaml +55 -0
  19. all_features/Hurricane_Ida/seed_77/best_model.pt +3 -0
  20. all_features/Hurricane_Ida/seed_77/config_resolved.yaml +55 -0
  21. all_features/Hurricane_Ida/seed_88/best_model.pt +3 -0
  22. all_features/Hurricane_Ida/seed_88/config_resolved.yaml +55 -0
  23. all_features/Hurricane_Ida/seed_99/best_model.pt +3 -0
  24. all_features/Hurricane_Ida/seed_99/config_resolved.yaml +55 -0
  25. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_00/best_model.pt +3 -0
  26. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_00/config_resolved.yaml +59 -0
  27. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_11/best_model.pt +3 -0
  28. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_11/config_resolved.yaml +59 -0
  29. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_22/best_model.pt +3 -0
  30. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_22/config_resolved.yaml +59 -0
  31. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_33/best_model.pt +3 -0
  32. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_33/config_resolved.yaml +59 -0
  33. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_44/best_model.pt +3 -0
  34. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_44/config_resolved.yaml +59 -0
  35. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_55/best_model.pt +3 -0
  36. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_55/config_resolved.yaml +59 -0
  37. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_66/best_model.pt +3 -0
  38. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_66/config_resolved.yaml +59 -0
  39. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_77/best_model.pt +3 -0
  40. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_77/config_resolved.yaml +59 -0
  41. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_88/best_model.pt +3 -0
  42. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_88/config_resolved.yaml +59 -0
  43. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_99/best_model.pt +3 -0
  44. all_features/Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire/seed_99/config_resolved.yaml +59 -0
  45. all_features/Hurricane_Michael/seed_00/best_model.pt +3 -0
  46. all_features/Hurricane_Michael/seed_00/config_resolved.yaml +55 -0
  47. all_features/Hurricane_Michael/seed_11/best_model.pt +3 -0
  48. all_features/Hurricane_Michael/seed_11/config_resolved.yaml +55 -0
  49. all_features/Hurricane_Michael/seed_22/best_model.pt +3 -0
  50. all_features/Hurricane_Michael/seed_22/config_resolved.yaml +55 -0
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README.md ADDED
@@ -0,0 +1,142 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: agpl-3.0
3
+ library_name: pytorch
4
+ inference: false
5
+ datasets:
6
+ - CRASAR/CRASAR-U-DROIDs
7
+ base_model:
8
+ - timm/convnextv2_nano.fcmae_ft_in22k_in1k
9
+ tags:
10
+ - remote-sensing
11
+ - building-damage-assessment
12
+ - disaster-response
13
+ - suas
14
+ - ordinal-classification
15
+ - convnextv2
16
+ ---
17
+
18
+ # Mask Centered Damage Net (MCDN)
19
+
20
+ [![Code](https://img.shields.io/badge/GitHub-MobileSensorLab%2Fmcdn-181717?logo=github)](https://github.com/MobileSensorLab/MCDN)
21
+ [![Python](https://img.shields.io/badge/Python-3.13-3776AB?logo=python&logoColor=white)](https://www.python.org/)
22
+ [![PyTorch](https://img.shields.io/badge/PyTorch-2.11-EE4C2C?logo=pytorch&logoColor=orange)](https://pytorch.org/)
23
+ [![Dataset](https://img.shields.io/badge/Dataset-CRASAR--U--DROIDs-FFD21E)](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs)
24
+ [![License: AGPL v3](https://img.shields.io/badge/License-GNU%20AGPL%20v3-663366.svg?logo=gnu&logoColor=white)](https://www.gnu.org/licenses/agpl-3.0)
25
+
26
+ Mask Centered Damage Net (MCDN) is a ~33.5M-parameter unitemporal structural damage classifier designed for use on sUAS
27
+ orthomosaics, built on a [ConvNeXt v2 Nano](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) backbone.
28
+ It grades individual structures on the four-level Joint Damage Scale (*No Damage*, *Minor*, *Major*, *Destroyed*) using only
29
+ a post-disaster sUAS orthomosaic and a cache of building footprints, with no pre-disaster imagery.
30
+
31
+ Most automated damage assessment is bitemporal: a Siamese network compares pre- and post-event satellite imagery and grades
32
+ the change. Applied to sUAS post-disaster imagery, that approach assumes a pre-event raster can be delivered to the point of
33
+ analysis, which a disaster zone with degraded communications generally cannot do, and would struggle to compare a sub-5 cm/px
34
+ sUAS orthomosaic to a satellite (30–80 cm/px) or crewed (15–30 cm/px) prior. MCDN replaces the pre-event raster with the
35
+ structure footprint, a vector prior small enough to cache on a field laptop in advance and already available for most of the
36
+ built world. The footprint localizes, centering each input chip on the structure being evaluated, and directs attention: the
37
+ rasterized mask is ingested as a fourth input channel and separately weights the model's spatial pooling, so features under
38
+ the roof dominate the pooled representation and surrounding debris contributes less. A FiLM gate conditions the head on
39
+ disaster typology, and a squared Earth Mover's Distance loss preserves the ordinal structure of the grades.
40
+
41
+ This repository holds the trained weights. Code, evaluation scripts, per-seed metrics, and the full README live in the
42
+ [MCDN repository](https://github.com/MobileSensorLab/MCDN); the training data is
43
+ [CRASAR/CRASAR-U-DROIDs](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs).
44
+
45
+ ## Headline performance
46
+
47
+ MCDN was evaluated on four holdouts: the CRASAR-U-DROIDs default train/test split, whose test events are all absent from
48
+ training, and three Leave One Event Out (LOEO) holdouts that each withhold a single event. Per-seed columns are mean ± SD over
49
+ the ten seeds published here; ensemble columns average those ten networks under 8-view D4 test-time augmentation.
50
+
51
+ | Holdout | Per-seed QWK | Per-seed Macro-F1 | Ensemble QWK | Ensemble Macro-F1 |
52
+ |---|:---:|:---:|:---:|:---:|
53
+ | DROIDs default split | 0.865 ± 0.002 | 0.768 ± 0.007 | **0.869** | **0.774** |
54
+ | LOEO Hurricane Michael | 0.861 ± 0.005 | 0.779 ± 0.005 | **0.861** | **0.779** |
55
+ | LOEO Mayfield Tornado | 0.849 ± 0.007 | 0.751 ± 0.014 | **0.862** | **0.768** |
56
+ | LOEO Hurricane Ida | 0.752 ± 0.010 | 0.686 ± 0.008 | **0.762** | **0.693** |
57
+
58
+ ![10-seed ensemble confusion matrices on the four holdouts, row-normalized](assets/confusion_triptych.svg)
59
+
60
+ Three of the four holdouts fall within 0.01 of one another. Hurricane Ida is the exception, and its deficit traces to a single
61
+ class boundary: most of Ida's severe errors are structures with intact roofs surrounded by storm surge, labeled *Minor* or
62
+ *Major* for damage to the interior and lower structure that a nadir roof view does not show, so the model under-grades them as
63
+ *No Damage*. Across all holdouts, errors concentrate on adjacent grades; on the default split, 727 of 820 errors are one grade
64
+ off while only 93 (2.4% of structures) are two or more.
65
+
66
+ A single network without test-time augmentation scores within 0.02 QWK of the ten-seed ensemble on every holdout; ensembling
67
+ contributes seed-to-seed stability more than accuracy. On a desktop RTX 5090 a single network grades about 830 structures per
68
+ second (108 with TTA) and the full 80-pass ensemble just under 10, with peak GPU memory at or below 3.3 GB.
69
+
70
+ ## What is here
71
+
72
+ 370 `best_model.pt` files across 10 ablation arms and 37 arm/holdout cells
73
+ (42.9 GB), ten fixed seeds per cell (0, 11, 22, 33, 44, 55, 66, 77, 88, 99). Each seed directory also
74
+ carries the `config_resolved.yaml` the run trained under, with its run-bookkeeping paths (`data.dir`, `checkpoint_root`)
75
+ normalized to the code repository's layout; every other field is verbatim.
76
+
77
+ ```
78
+ <arm>/<holdout>/seed_<NN>/best_model.pt
79
+ <arm>/<holdout>/seed_<NN>/config_resolved.yaml
80
+ MANIFEST.json # every file with size and SHA-256
81
+ MANIFEST.sha256 # sha256sum -c compatible
82
+ ```
83
+
84
+ Holdout directories name the event(s) withheld from training: `Hurricane_Ida`, `Hurricane_Michael`, `Mayfield_Tornado`, and
85
+ the dataset's default test split `Hurricane_Idalia+Hurricane_Michael+Mayfield_Tornado+Mussett_Bayou_Fire`.
86
+
87
+ ### Arms
88
+
89
+ C = footprint input channel, P = mask-weighted pooling, T = typology FiLM. Directory names follow the training presets, which
90
+ name the component *removed*; the label column follows the paper.
91
+
92
+ | Directory | Paper label | Components | Holdouts | Checkpoints |
93
+ |---|---|---|---|---|
94
+ | `all_features` | Full configuration | C+P+T | all 4 | 40 |
95
+ | `pooling_typology` | Pooling + typology | P+T | all 4 | 40 |
96
+ | `typology` | No typology | C+P | all 4 | 40 |
97
+ | `pooling_only` | Pooling only | P | all 4 | 40 |
98
+ | `mask_channel_only` | Channel only | C | all 4 | 40 |
99
+ | `mask` | Typology only | T | all 4 | 40 |
100
+ | `rgb_only` | RGB only | none | all 4 | 40 |
101
+ | `ce_loss` | Cross-entropy loss | C+P+T | all 4 | 40 |
102
+ | `no_smoothing` | No label smoothing | C+P+T | all 4 | 40 |
103
+ | `resolution` | Crewed-aircraft imagery (15–30 cm/px), full configuration | C+P+T | `Hurricane_Michael` | 10 |
104
+
105
+ ## Fetching
106
+
107
+ From a clone of the code repository, which places files where the evaluation scripts expect them, verifies every checkpoint
108
+ against `MANIFEST.json`, and skips files already present:
109
+
110
+ ```bash
111
+ python -m scripts.fetch_checkpoints --list
112
+ python -m scripts.fetch_checkpoints --arm all_features --fold Hurricane_Michael
113
+ python -m scripts.fetch_checkpoints --all
114
+ ```
115
+
116
+ Or directly:
117
+
118
+ ```python
119
+ from huggingface_hub import snapshot_download
120
+
121
+ snapshot_download("mobilesensorlab/mcdn", allow_patterns=["all_features/Hurricane_Michael/*"], local_dir="outputs/ablation")
122
+ ```
123
+
124
+ Reproducing the published metrics needs the dataset's sUAS test pool (~66 GB) alongside the weights; the code repository's
125
+ `scripts.fetch_dataset --sensor uas --split test` retrieves exactly that.
126
+
127
+ ## Loading
128
+
129
+ Checkpoints are PyTorch state dicts for `src.model.mcdn.MCDN`; build the model from the seed's `config_resolved.yaml` and call
130
+ `load_state_dict`. See `src/postproc/ensemble.py` in the code repository for the ten-seed, eight-view test-time-augmentation
131
+ ensemble used for every reported number, and `scripts/profile_mcdn_inference.py` for single-network inference.
132
+
133
+ ## Citation
134
+
135
+ The accompanying manuscript, *Mask Centered Damage Net: Building Damage Classification from Unitemporal sUAS Imagery and
136
+ Footprint Priors* (A. Kaplan and E. Best, Mobile Sensor Lab, University at Albany), is under review at IEEE JSTARS. Until it
137
+ is published, please cite this repository and the [code repository](https://github.com/MobileSensorLab/MCDN); a DOI and BibTeX entry will be added on
138
+ acceptance.
139
+
140
+ ## License
141
+
142
+ MCDN and its checkpoints licensed under the GNU Affero General Public License v3.0.
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+ typology_enabled: true
31
+ mask_weighted_pooling_enabled: true
32
+ mask_dilation_px: 0
33
+ runtime:
34
+ num_workers: 8
35
+ pin_memory: true
36
+ persistent_workers: true
37
+ prefetch_factor: 3
38
+ drop_last: true
39
+ val_batch_size_factor: 0.5
40
+ device: cuda
41
+ seed: 22
42
+ early_stopping_patience: 10
43
+ lr_drop_warmstart_from_best: true
44
+ reset_optimizer_on_lr_drop: false
45
+ ema_enabled: true
46
+ ema_decay: 0.996
47
+ checkpoint_root: outputs/ablation/all_features/Hurricane_Michael/seed_22
48
+ cuda_launch_blocking: false
49
+ verbose: false
50
+ print_every_n_batches: null
51
+ log_to_file: true
52
+ metadata:
53
+ created_utc: '2026-08-30T00:53:49.040229+00:00'
54
+ holdout_event: Hurricane Michael
55
+ holdout_selection: explicit