File size: 16,946 Bytes
f524e77
f92e62d
ddf28d6
 
d051dc9
88d4d9c
 
4e74f4a
 
 
 
 
 
 
43f98e7
 
77f46b1
43f98e7
 
67ffafe
16ea832
77f46b1
4dd259c
77f46b1
43f98e7
 
16ea832
 
 
f851b5d
77f46b1
 
4d8a7ca
48d3911
4d8a7ca
91e4a35
2dd1152
 
 
 
6c33ac0
2dd1152
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
77f46b1
 
5d303b4
77f46b1
2dd1152
 
4d8a7ca
 
 
2dd1152
 
4d8a7ca
 
 
77f46b1
5d303b4
 
77f46b1
2dd1152
 
 
16ea832
2dd1152
 
 
 
16ea832
2dd1152
f851b5d
 
2dd1152
 
 
16ea832
2dd1152
 
 
 
16ea832
2dd1152
f851b5d
 
2dd1152
 
 
16ea832
2dd1152
 
 
 
16ea832
2dd1152
f851b5d
 
2dd1152
 
 
16ea832
2dd1152
 
 
 
16ea832
2dd1152
77f46b1
 
2dd1152
 
 
16ea832
2dd1152
 
 
 
16ea832
2dd1152
77f46b1
 
2dd1152
 
 
16ea832
2dd1152
 
 
 
16ea832
2dd1152
77f46b1
 
2dd1152
 
 
16ea832
2dd1152
 
 
 
16ea832
2dd1152
77f46b1
 
2dd1152
 
 
16ea832
 
2dd1152
 
 
16ea832
2dd1152
f851b5d
 
2dd1152
 
 
16ea832
2dd1152
6c33ac0
2dd1152
 
16ea832
2dd1152
f851b5d
 
2dd1152
 
 
16ea832
2dd1152
 
 
 
 
77f46b1
5d303b4
 
f851b5d
43f98e7
 
 
67ffafe
 
16ea832
 
 
 
4dd259c
da2eca5
43f98e7
3590fd4
 
 
d01abd8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3590fd4
77f46b1
4dd259c
77f46b1
43f98e7
16ea832
 
 
 
77f46b1
 
16ea832
 
43f98e7
d01abd8
3590fd4
936bc32
f6d1762
da2eca5
936bc32
 
16ea832
936bc32
 
 
 
 
 
 
 
 
 
16ea832
 
936bc32
16ea832
 
 
 
936bc32
 
 
3590fd4
a29bd77
 
16ea832
a29bd77
67ffafe
16ea832
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a29bd77
 
 
 
43f98e7
16ea832
 
4dd259c
70a0882
 
 
 
 
 
 
 
 
 
16ea832
 
43f98e7
 
d01abd8
77f46b1
 
 
d01abd8
 
43f98e7
 
16ea832
43f98e7
16ea832
 
 
 
 
 
 
 
 
 
 
 
29ae5d6
16ea832
 
 
 
9978f9b
43f98e7
67ffafe
43f98e7
 
 
d01abd8
67ffafe
a60bbc9
 
43f98e7
 
 
67ffafe
590ac68
13bbb82
88d4d9c
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
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
---
license: etalab-2.0
pretty_name: French Land Cover from Aerospace Imagery
size_categories:
- 10B<n<100B
task_categories:
- image-segmentation
tags:
- IGN
- Aerial
- Satellite
- Environement
- Multimodal
- Earth Observation
---

# Datset Card for FLAIR land-cover semantic segmentation

## Context & Data
<hr style='margin-top:-1em; margin-bottom:0' />
The hereby FLAIR (#1 and #2) dataset is sampled countrywide and is composed of over 20 billion annotated pixels of very high resolution aerial imagery at 0.2 m spatial resolution, acquired over three years and different months (spatio-temporal domains). 
Aerial imagery patches consist of 5 channels (RVB-Near Infrared-Elevation) and have corresponding annotation (with 19 semantic classes or 13 for the baselines). 
Furthermore, to integrate broader spatial context and temporal information, high resolution Sentinel-2 satellite 1-year time series with 10 spectral band are also provided. 
More than 50,000 Sentinel-2 acquisitions with 10 m spatial resolution are available.
<br>

The dataset covers 55 distinct spatial domains, encompassing 974 areas spanning 980 km². This dataset provides a robust foundation for advancing land cover mapping techniques.
We sample two test sets based on different input data and focus on semantic classes. The first test set (flair#1-test) uses very high resolution aerial imagery only and samples primarily anthropized land cover classes. 
In contrast, the second test set (flair#2-test) combines aerial and satellite imagery and has more natural classes with temporal variations represented.<br><br>

<style type="text/css">
.tg  {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:13px;
  overflow:hidden;padding:2px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:13px;
  font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-km2t{border-color:#ffffff;font-weight:bold;text-align:left;vertical-align:top}
.tg .tg-rime{background-color:#E4DF7C;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-r3rw{background-color:#a97101;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-zv4m{border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-nto1{background-color:#000000;border-color:inherit;text-align:left;vertical-align:top}
.tg .tg-9efv{background-color:#938e7b;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-8jgo{border-color:#ffffff;text-align:center;vertical-align:top}
.tg .tg-b45e{background-color:#194A26;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-9xgv{background-color:#1553ae;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-3m6m{background-color:#f80c00;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-2e1p{background-color:#db0e9a;border-color:#ffffff;color:#db0e9a;text-align:left;vertical-align:top}
.tg .tg-l5fa{background-color:#FFF30D;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-2cns{background-color:#3DE6EB;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-jjsp{background-color:#FFF;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-2w6m{background-color:#8AB3A0;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-nla7{background-color:#6B714F;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-qg2z{background-color:#46E483;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-nv8o{background-color:#C5DC42;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-grz5{background-color:#F3A60D;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-bja1{background-color:#99F;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-69kt{background-color:#660082;border-color:#ffffff;text-align:left;vertical-align:top}
.tg .tg-r1r4{background-color:#5F0;border-color:#ffffff;text-align:left;vertical-align:top}
</style>
<table class="tg">
<thead>
  <tr>
    <th class="tg-zv4m"></th>
    <th class="tg-zv4m">Class</th>
    <th class="tg-8jgo">Train/val (%)</th>
    <th class="tg-8jgo">Test flair#1 (%)</th>
    <th class="tg-8jgo">Test flair#2 (%)</th>    
    <th class="tg-zv4m"></th>
    <th class="tg-zv4m">Class</th>
    <th class="tg-8jgo">Train/val (%)</th>
    <th class="tg-8jgo">Test flair#1 (%)</th>
    <th class="tg-8jgo">Test flair#2 (%)</th>   
  </tr>
</thead>
<tbody>
  <tr>
    <td class="tg-2e1p"></td>
    <td class="tg-km2t">(1) Building</td>
    <td class="tg-8jgo">8.14</td>
    <td class="tg-8jgo">8.6</td>   
    <td class="tg-8jgo">3.26</td>
    <td class="tg-l5fa"></td>
    <td class="tg-km2t">(11) Agricultural Land</td>
    <td class="tg-8jgo">10.98</td>
    <td class="tg-8jgo">6.95</td>    
    <td class="tg-8jgo">18.19</td>
  </tr>
  <tr>
    <td class="tg-9efv"></td>
    <td class="tg-km2t">(2) Pervious surface</td>
    <td class="tg-8jgo">8.25</td>
    <td class="tg-8jgo">7.34</td>    
    <td class="tg-8jgo">3.82</td>
    <td class="tg-rime"></td>
    <td class="tg-km2t">(12) Plowed land</td>
    <td class="tg-8jgo">3.88</td>
    <td class="tg-8jgo">2.25</td>    
    <td class="tg-8jgo">1.81</td>
  </tr>
  <tr>
    <td class="tg-3m6m"></td>
    <td class="tg-km2t">(3) Impervious surface</td>
    <td class="tg-8jgo">13.72</td>
    <td class="tg-8jgo">14.98</td>    
    <td class="tg-8jgo">5.87</td>
    <td class="tg-2cns"></td>
    <td class="tg-km2t">(13) Swimming pool</td>
    <td class="tg-8jgo">0.01</td>
    <td class="tg-8jgo">0.04</td>    
    <td class="tg-8jgo">0.02</td>
  </tr>
  <tr>
    <td class="tg-r3rw"></td>
    <td class="tg-km2t">(4) Bare soil</td>
    <td class="tg-8jgo">3.47</td>
    <td class="tg-8jgo">4.36</td>    
    <td class="tg-8jgo">1.6</td>
    <td class="tg-jjsp"></td>
    <td class="tg-km2t">(14) Snow</td>
    <td class="tg-8jgo">0.15</td>
    <td class="tg-8jgo">-</td>    
    <td class="tg-8jgo">-</td>
  </tr>
  <tr>
    <td class="tg-9xgv"></td>
    <td class="tg-km2t">(5) Water</td>
    <td class="tg-8jgo">4.88</td>
    <td class="tg-8jgo">5.98</td>    
    <td class="tg-8jgo">3.17</td>
    <td class="tg-2w6m"></td>
    <td class="tg-km2t">(15) Clear cut</td>
    <td class="tg-8jgo">0.15</td>
    <td class="tg-8jgo">0.01</td>    
    <td class="tg-8jgo">0.82</td>
  </tr>
  <tr>
    <td class="tg-b45e"></td>
    <td class="tg-km2t">(6) Coniferous</td>
    <td class="tg-8jgo">2.74</td>
    <td class="tg-8jgo">2.39</td>
    <td class="tg-8jgo">10.24</td>
    <td class="tg-nla7"></td>
    <td class="tg-km2t">(16) Mixed</td>
    <td class="tg-8jgo">0.05</td>
    <td class="tg-8jgo">-</td>    
    <td class="tg-8jgo">0.12</td>
  </tr>
  <tr>
    <td class="tg-qg2z"></td>
    <td class="tg-km2t">(7) Deciduous</td>
    <td class="tg-8jgo">15.38</td>
    <td class="tg-8jgo">13.91</td>
    <td class="tg-8jgo">24.79</td>
    <td class="tg-nv8o"></td>
    <td class="tg-km2t">(17) Ligneous</td>
    <td class="tg-8jgo">0.01</td>
    <td class="tg-8jgo">0.03</td>
    <td class="tg-8jgo">-</td>
  </tr>
  <tr>
    <td class="tg-grz5"></td>
    <td class="tg-km2t">(8) Brushwood</td>
    <td class="tg-8jgo">6.95</td>
    <td class="tg-8jgo">6.91</td>
    <td class="tg-8jgo">3.81</td>    
    <td class="tg-bja1"></td>
    <td class="tg-km2t">(18) Greenhouse</td>
    <td class="tg-8jgo">0.12</td>
    <td class="tg-8jgo">0.2</td>    
    <td class="tg-8jgo">0.15</td>
  </tr>
  <tr>
    <td class="tg-69kt"></td>
    <td class="tg-km2t">(9) Vineyard</td>
    <td class="tg-8jgo">3.13</td>
    <td class="tg-8jgo">3.87</td>
    <td class="tg-8jgo">2.55</td>
    <td class="tg-nto1"></td>
    <td class="tg-km2t">(19) Other</td>
    <td class="tg-8jgo">0.14</td>
    <td class="tg-8jgo">0.-</td>    
    <td class="tg-8jgo">0.04</td>
  </tr>
  <tr>
    <td class="tg-r1r4"></td>
    <td class="tg-km2t">(10) Herbaceous vegetation</td>
    <td class="tg-8jgo">17.84</td>
    <td class="tg-8jgo">22.17</td>
    <td class="tg-8jgo">19.76</td>
    <td class="tg-zv4m"></td>
    <td class="tg-zv4m"></td>
    <td class="tg-zv4m"></td>
    <td class="tg-zv4m"></td>
  </tr>
</tbody>
</table>

<br><br>


## Dataset Structure
<hr style='margin-top:-1em; margin-bottom:0' />
The FLAIR dataset consists of a total of 93 462 patches: 61 712 patches for the train/val dataset, 15 700 patches for flair#1-test and 16 050 patches for flair#2-test.

Each patch includes a high-resolution aerial image (512x512) at 0.2 m, a yearly satellite image time series (40x40 by default by wider areas are provided) with a spatial resolution of 10 m 
and associated cloud and snow masks (available in train/val and flair#2-test), and pixel-precise elevation and land cover annotations at 0.2 m resolution (512x512).

<p align="center"><img src="flair-patches.png" alt="" style="width:70%;max-width:600px;"/></p><br>


### Band order

<div style="display: flex;">
<div style="width: 15%;margin-right: 1;"">
Aerial
<ul>
<li>1. Red</li>
<li>2. Green</li>
<li>3. Blue</li>
<li>4. NIR</li>
<li>5. nDSM</li>
</ul>
</div>

<div style="width: 25%;">
Satellite
<ul>
<li>1. Blue (B2 490nm)</li>
<li>2. Green (B3 560nm)</li>
<li>3. Red (B4 665nm)</li>
<li>4. Red-Edge (B5 705nm)</li>
<li>5. Red-Edge2 (B6 470nm)</li>
<li>6. Red-Edge3 (B7 783nm)</li>
<li>7. NIR (B8 842nm)</li>
<li>8. NIR-Red-Edge (B8a 865nm)</li>
<li>9. SWIR (B11 1610nm)</li>
<li>10. SWIR2 (B12 2190nm)</li>
</ul>
</div>

</div>

### Annotations
Each pixel has been manually annotated by photo-interpretation of the 20 cm resolution aerial imagery, carried out by a team supervised by geography experts from the IGN. 
Movable objects like cars or boats are annotated according to their underlying cover.

### Data Splits
The dataset is made up of 55 distinct spatial domains, aligned with the administrative boundaries of the French départements. 
For our experiments, we designate 32 domains for training, 8 for validation, and reserve 10 official test sets for flair#1-test and flair#2-test. 
It can also be noted that some domains are common in the flair#1-test and flair#2-test datasets but cover different areas within the domain.  
This arrangement ensures a balanced distribution of semantic classes, radiometric attributes, bioclimatic conditions, and acquisition times across each set. 
Consequently, every split accurately reflects the landscape diversity inherent to metropolitan France. 
It is important to mention that the patches come with meta-data permitting alternative splitting schemes. 


Official domain split: <br/>

<div style="display: flex; flex-wrap: nowrap; align-items: center">
    <div style="flex: 40%;">
        <img src="flair-splits.png" alt="flair-splits">
</div>

  <div style="flex: 60%; margin: auto;"">
  <table border="1">
    <tr>
      <th><font color="#c7254e">TRAIN:</font></th>
      <td>D006, D007, D008, D009, D013, D016, D017, D021, D023, D030, D032, D033, D034, D035, D038, D041, D044, D046, D049, D051, D052, D055, D060, D063, D070, D072, D074, D078, D080, D081, D086, D091</td>
    </tr>
    <tr>
      <th><font color="#c7254e">VALIDATION:</font></th>
      <td>D004, D014, D029, D031, D058, D066, D067, D077</td>
    </tr>
    <tr>
      <th><font color="#c7254e">TEST-flair#1:</font></th>
      <td>D012, D022, D026, D064, D068, D071, D075, D076, D083, D085</td>
    </tr>
    <tr>
      <th><font color="#c7254e">TEST-flair#2:</font></th>
      <td>D015, D022, D026, D036, D061, D064, D068, D069, D071, D084</td>
    </tr>    
  </table>
  </div>
</div>

<br><br>


## Baseline code 
<hr style='margin-top:-1em; margin-bottom:0' />
<br>

### Flair #1 (aerial only)
A U-Net architecture with a pre-trained ResNet34 encoder from the pytorch segmentation models library is used for the baselines. 
The used architecture allows integration of patch-wise metadata information and employs commonly used image data augmentation techniques. 

Flair#1 code repository &#128193; : https://github.com/IGNF/FLAIR-1<br/>
Link to the paper : https://arxiv.org/pdf/2211.12979.pdf <br>

Please include a citation to the following article if you use the FLAIR#1 dataset:

```
@article{ign2022flair1,
  doi = {10.13140/RG.2.2.30183.73128/1},
  url = {https://arxiv.org/pdf/2211.12979.pdf},
  author = {Garioud, Anatol and Peillet, Stéphane and Bookjans, Eva and Giordano, Sébastien and Wattrelos, Boris},
  title = {FLAIR #1: semantic segmentation and domain adaptation dataset},
  publisher = {arXiv},
  year = {2022}
}
```
<br>

### Flair #2 (aerial and satellite)
We propose the U-T&T model, a two-branch architecture that combines spatial and temporal information from very high-resolution aerial images and high-resolution satellite images into a single output. The U-Net architecture is employed for the spatial/texture branch, using a ResNet34 backbone model pre-trained on ImageNet. For the spatio-temporal branch, 
the U-TAE architecture incorporates a Temporal self-Attention Encoder (TAE) to explore the spatial and temporal characteristics of the Sentinel-2 time series data, 
applying attention masks at different resolutions during decoding. This model allows for the fusion of learned information from both sources, 
enhancing the representation of mono-date and time series data.

U-T&T code repository &#128193; : https://github.com/IGNF/FLAIR-2<br/>
Link to the paper : https://arxiv.org/abs/2310.13336 <br>

<th><font color="#c7254e"><b>IMPORTANT!</b></font></th> <b>The structure of the current dataset differs from the one that comes with the GitHub repository.</b> 
To work with the current dataset, you need to replace the <font color=‘#D7881C’><em>src/load_data.py</em></font> file with the one provided here. 
You also need to add the following lines to the <font color=‘#D7881C’><em>flair-2-config.yml</em></font> file under the <em><b>data</b></em> tag: <br>

```
HF_data_path : " " # Path to unzipped FLAIR HF dataset
domains_train : ["D006_2020","D007_2020","D008_2019","D009_2019","D013_2020","D016_2020","D017_2018","D021_2020","D023_2020","D030_2021","D032_2019","D033_2021","D034_2021","D035_2020","D038_2021","D041_2021","D044_2020","D046_2019","D049_2020","D051_2019","D052_2019","D055_2018","D060_2021","D063_2019","D070_2020","D072_2019","D074_2020","D078_2021","D080_2021","D081_2020","D086_2020","D091_2021"]
domains_val : ["D004_2021","D014_2020","D029_2021","D031_2019","D058_2020","D066_2021","D067_2021","D077_2021"]   
domains_test : ["D015_2020","D022_2021","D026_2020","D036_2020","D061_2020","D064_2021","D068_2021","D069_2020","D071_2020","D084_2021"]
```
<br>
Please include a citation to the following article if you use the FLAIR#2 dataset:

```
@inproceedings{garioud2023flair,
      title={FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery}, 
      author={Anatol Garioud and Nicolas Gonthier and Loic Landrieu and Apolline De Wit and Marion Valette and Marc Poupée and Sébastien Giordano and Boris Wattrelos},
      year={2023},
      booktitle={Advances in Neural Information Processing Systems (NeurIPS) 2023},
      doi={https://doi.org/10.48550/arXiv.2310.13336},
}
```
<br>

## CodaLab challenges
<hr style='margin-top:-1em; margin-bottom:0' />

The FLAIR dataset was used for two challenges organized by IGN in 2023 on the CodaLab platform.<br>
Challenge FLAIR#1 : https://codalab.lisn.upsaclay.fr/competitions/8769 <br>
Challenge FLAIR#2 : https://codalab.lisn.upsaclay.fr/competitions/13447 <br>

flair#1-test | The podium:  
🥇 businiao - 0.65920  
🥈 Breizhchess - 0.65600  
🥉 wangzhiyu918 - 0.64930  

flair#2-test | The podium:  
🥇 strakajk - 0.64130  
🥈 Breizhchess - 0.63550  
🥉 qwerty64 - 0.63510  


## Acknowledgment
<hr style='margin-top:-1em; margin-bottom:0' />
This work was performed using HPC/AI resources from GENCI-IDRIS (Grant 2022-A0131013803). This work was supported by the project "Copernicus / FPCUP” of the European Union, by the French Space Agency (CNES) and by Connect by CNES.<br>


## Contact
<hr style='margin-top:-1em; margin-bottom:0' />
If you have any questions, issues or feedback, you can contact us at: ai-challenge@ign.fr 
<br>


## Dataset license
<hr style='margin-top:-1em; margin-bottom:0' />
The "OPEN LICENCE 2.0/LICENCE OUVERTE" is a license created by the French government specifically for the purpose of facilitating the dissemination of open data by public administration.<br/>
This licence is governed by French law.<br/>
This licence has been designed to be compatible with any free licence that at least requires an acknowledgement of authorship, and specifically with the previous version of this licence as well as with the following licences: United Kingdom’s “Open Government Licence” (OGL), Creative Commons’ “Creative Commons Attribution” (CC-BY) and Open Knowledge Foundation’s “Open Data Commons Attribution” (ODC-BY).