File size: 8,745 Bytes
1f9f4d5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
{
  "type": "Feature",
  "stac_version": "1.1.0",
  "stac_extensions": [
    "https://stac-extensions.github.io/mlm/v1.5.0/schema.json",
    "https://stac-extensions.github.io/file/v2.1.0/schema.json"
  ],
  "id": "ENSEMBLE_6MODELS_CLOUDMASK_FT_20251118",
  "geometry": {
    "type": "Polygon",
    "coordinates": [
      [
        [
          -180.0,
          -90.0
        ],
        [
          -180.0,
          90.0
        ],
        [
          180.0,
          90.0
        ],
        [
          180.0,
          -90.0
        ],
        [
          -180.0,
          -90.0
        ]
      ]
    ]
  },
  "bbox": [
    -180,
    -90,
    180,
    90
  ],
  "properties": {
    "datetime": "2025-11-18T12:46:12Z",
    "created": "2025-11-18T12:46:12Z",
    "updated": "2025-11-18T12:46:12Z",
    "description": "Ensemble of 6 fine-tuned DeepLabV3/UNet++/LinkNet models for cloud detection in VGT-1, VGT-2, and PROBA-V imagery. Use load.py for inference.",
    "title": "Ensemble Cloud Detection Model (6 Models) - VGT1/VGT2/Proba-V",
    "mlm:name": "ensemble_fdr4vgt_cloudmask_ft",
    "mlm:architecture": "Ensemble (Mean/Max/Min aggregation) of 6 segmentation models",
    "mlm:tasks": [
      "semantic-segmentation"
    ],
    "mlm:framework": "pytorch",
    "mlm:framework_version": "2.5.1+cu121",
    "mlm:accelerator": "cuda",
    "mlm:accelerator_constrained": false,
    "mlm:accelerator_summary": "NVIDIA GPU with CUDA support (compute capability >= 7.0)",
    "mlm:accelerator_count": 1,
    "mlm:batch_size_suggestion": 8,
    "mlm:pretrained": true,
    "mlm:input": [
      {
        "name": "VGT_PROBA_TOC_reflectance",
        "bands": [
          "Blue (B0, ~450nm)",
          "Red (B2, ~645nm)",
          "Near-Infrared (B3, ~835nm)",
          "SWIR (MIR, ~1665nm)"
        ],
        "input": {
          "shape": [
            -1,
            4,
            512,
            512
          ],
          "dim_order": [
            "batch",
            "channel",
            "height",
            "width"
          ],
          "data_type": "float32"
        },
        "norm": {
          "type": "raw_toc_reflectance",
          "range": [
            0,
            10000
          ],
          "description": "Raw Top-of-Canopy reflectance values scaled by 10000"
        },
        "pre_processing_function": null
      }
    ],
    "mlm:output": [
      {
        "name": "cloud_probability",
        "tasks": [
          "semantic-segmentation"
        ],
        "result": {
          "shape": [
            -1,
            1,
            512,
            512
          ],
          "dim_order": [
            "batch",
            "channel",
            "height",
            "width"
          ],
          "data_type": "float32"
        },
        "classification:classes": [
          {
            "value": 0.0,
            "name": "clear",
            "description": "Clear sky (may contain cloud shadows)",
            "color_hint": "00000000"
          },
          {
            "value": 1.0,
            "name": "cloud",
            "description": "Cloud present",
            "color_hint": "FFFF00"
          }
        ],
        "post_processing_function": "Apply threshold to get binary mask. Standard threshold: 0.5. Recommended (balanced) threshold: 0.4.",
        "standard_threshold": 0.5,
        "recommended_threshold": 0.3535,
        "value_range": [
          0.0,
          1.0
        ],
        "description": "Per-pixel probability of cloud presence. Built-in sigmoid activation. Values close to 1.0 indicate high confidence of cloud."
      }
    ],
    "custom:export_format": "torch.export.pt2",
    "custom:has_sigmoid": true,
    "custom:sigmoid_location": "built-in wrapper",
    "custom:export_datetime": "2025-11-18T12:46:12Z",
    "custom:project": "FDR4VGT",
    "custom:project_url": "https://fdr4vgt.eu/",
    "custom:sensors": [
      "VGT-1",
      "VGT-2",
      "PROBA-V"
    ],
    "custom:sensor_notes": "Model applicable to SPOT-VGT1, SPOT-VGT2, and PROBA-V imagery",
    "custom:spatial_resolution": "1km",
    "custom:tile_size": 512,
    "custom:recommended_overlap": 64,
    "custom:applicable_start": "1998-03-01T00:00:00Z",
    "custom:applicable_end": null,
    "dependencies": [
      "torch>=2.0.0",
      "segmentation-models-pytorch>=0.3.0",
      "pytorch-lightning>=2.0.0"
    ],
    "custom:ensemble_size": 6,
    "custom:ensemble_strategy": "Mean probability aggregation (default), supports Max/Min modes"
  },
  "links": [
    {
      "rel": "about",
      "href": "https://fdr4vgt.eu/",
      "type": "text/html",
      "title": "FDR4VGT Project - Harmonized VGT Data Record"
    },
    {
      "rel": "license",
      "href": "https://creativecommons.org/licenses/by/4.0/",
      "type": "text/html",
      "title": "CC-BY-4.0 License"
    }
  ],
  "assets": {
    "load": {
      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/load.py",
      "type": "application/x-python-code",
      "title": "Ensemble model loader",
      "description": "Python code to load all models and combine them into an EnsembleModel class.",
      "roles": [
        "code"
      ]
    },
    "example_data": {
      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/example_data.safetensor",
      "type": "application/octet-stream; application=safetensors",
      "title": "Example VGT/PROBA-V image",
      "description": "Example VGT/PROBA-V Top-of-Canopy reflectance image for model inference.",
      "roles": [
        "mlm:example_data",
        "data"
      ]
    },
    "model_01_proba_unet": {
      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/proba_unet.pt2",
      "type": "application/octet-stream; application=pytorch",
      "title": "Model 1: unet_fdr4vgt_cloudmask_ft",
      "description": "The weights of the UNET model in torch.export .pt2 format with built-in sigmoid activation.",
      "mlm:artifact_type": "torch.export.pt2",
      "roles": [
        "mlm:model",
        "mlm:weights",
        "data"
      ]
    },
    "model_02_proba_1dpwdeeplabv3": {
      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/proba_1dpwdeeplabv3.pt2",
      "type": "application/octet-stream; application=pytorch",
      "title": "Model 2: 1dpwdeeplabv3_fdr4vgt_cloudmask_ft",
      "description": "The weights of the 1DPWDEEPLABV3 model in torch.export .pt2 format with built-in sigmoid activation.",
      "mlm:artifact_type": "torch.export.pt2",
      "roles": [
        "mlm:model",
        "mlm:weights",
        "data"
      ]
    },
    "model_03_proba_deeplabv3": {
      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/proba_deeplabv3.pt2",
      "type": "application/octet-stream; application=pytorch",
      "title": "Model 3: deeplabv3_fdr4vgt_cloudmask_ft",
      "description": "The weights of the DEEPLABV3 model in torch.export .pt2 format with built-in sigmoid activation.",
      "mlm:artifact_type": "torch.export.pt2",
      "roles": [
        "mlm:model",
        "mlm:weights",
        "data"
      ]
    },
    "model_04_proba_segformer": {
      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/proba_segformer.pt2",
      "type": "application/octet-stream; application=pytorch",
      "title": "Model 4: segformer_fdr4vgt_cloudmask_ft",
      "description": "The weights of the SEGFORMER model in torch.export .pt2 format with built-in sigmoid activation.",
      "mlm:artifact_type": "torch.export.pt2",
      "roles": [
        "mlm:model",
        "mlm:weights",
        "data"
      ]
    },
    "model_05_proba_1dpwseg": {
      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/proba_1dpwseg.pt2",
      "type": "application/octet-stream; application=pytorch",
      "title": "Model 5: 1dpwseg_fdr4vgt_cloudmask_ft",
      "description": "The weights of the 1DPWSEG model in torch.export .pt2 format with built-in sigmoid activation.",
      "mlm:artifact_type": "torch.export.pt2",
      "roles": [
        "mlm:model",
        "mlm:weights",
        "data"
      ]
    },
    "model_06_proba_unetpp": {
      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/proba_unetpp.pt2",
      "type": "application/octet-stream; application=pytorch",
      "title": "Model 6: unetpp_fdr4vgt_cloudmask_ft",
      "description": "The weights of the UNETPP model in torch.export .pt2 format with built-in sigmoid activation.",
      "mlm:artifact_type": "torch.export.pt2",
      "roles": [
        "mlm:model",
        "mlm:weights",
        "data"
      ]
    }
  },
  "collection": "FDR4VGT_CloudMask_Ensemble"
}