-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathindex.html
606 lines (382 loc) · 50.8 KB
/
index.html
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
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
<!doctype html>
<html lang="en" class="no-js">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<link rel="icon" href="assets/images/favicon.png">
<meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.21">
<title>Swiss Territorial Data Lab</title>
<link rel="stylesheet" href="assets/stylesheets/main.66ac8b77.min.css">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,300i,400,400i,700,700i%7CRoboto+Mono:400,400i,700,700i&display=fallback">
<style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
<link rel="stylesheet" href="assets/stylesheets/extra.css">
<script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
</head>
<body dir="ltr">
<input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
<input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
<label class="md-overlay" for="__drawer"></label>
<div data-md-component="skip">
<a href="#swiss-territorial-data-lab-stdl" class="md-skip">
Skip to content
</a>
</div>
<div data-md-component="announce">
</div>
<header class="md-header md-header--shadow" data-md-component="header">
<nav class="md-header__inner md-grid" aria-label="Header">
<a href="." title="Swiss Territorial Data Lab" class="md-header__button md-logo" aria-label="Swiss Territorial Data Lab" data-md-component="logo">
<img src="assets/logo-stdl-transparent.svg" alt="logo">
</a>
<label class="md-header__button md-icon" for="__drawer">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
</label>
<div class="md-header__title" data-md-component="header-title">
<div class="md-header__ellipsis">
<div class="md-header__topic">
<span class="md-ellipsis">
Swiss Territorial Data Lab
</span>
</div>
<div class="md-header__topic" data-md-component="header-topic">
<span class="md-ellipsis">
Homepage
</span>
</div>
</div>
</div>
<script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
<label class="md-header__button md-icon" for="__search">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
</label>
<div class="md-search" data-md-component="search" role="dialog">
<label class="md-search__overlay" for="__search"></label>
<div class="md-search__inner" role="search">
<form class="md-search__form" name="search">
<input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
<label class="md-search__icon md-icon" for="__search">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
</label>
<nav class="md-search__options" aria-label="Search">
<button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
</button>
</nav>
</form>
<div class="md-search__output">
<div class="md-search__scrollwrap" data-md-scrollfix>
<div class="md-search-result" data-md-component="search-result">
<div class="md-search-result__meta">
Initializing search
</div>
<ol class="md-search-result__list" role="presentation"></ol>
</div>
</div>
</div>
</div>
</div>
<div class="md-header__source">
<a href="https://github.com/swiss-territorial-data-lab" title="Go to repository" class="md-source" data-md-component="source">
<div class="md-source__icon md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
</div>
<div class="md-source__repository">
STDL on Github
</div>
</a>
</div>
</nav>
</header>
<div class="md-container" data-md-component="container">
<main class="md-main" data-md-component="main">
<div class="md-main__inner md-grid">
<div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
<div class="md-sidebar__scrollwrap">
<div class="md-sidebar__inner">
<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
<label class="md-nav__title" for="__drawer">
<a href="." title="Swiss Territorial Data Lab" class="md-nav__button md-logo" aria-label="Swiss Territorial Data Lab" data-md-component="logo">
<img src="assets/logo-stdl-transparent.svg" alt="logo">
</a>
Swiss Territorial Data Lab
</label>
<div class="md-nav__source">
<a href="https://github.com/swiss-territorial-data-lab" title="Go to repository" class="md-source" data-md-component="source">
<div class="md-source__icon md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
</div>
<div class="md-source__repository">
STDL on Github
</div>
</a>
</div>
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item md-nav__item--active">
<input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
<label class="md-nav__link md-nav__link--active" for="__toc">
<span class="md-ellipsis">
Homepage
</span>
<span class="md-nav__icon md-icon"></span>
</label>
<a href="." class="md-nav__link md-nav__link--active">
<span class="md-ellipsis">
Homepage
</span>
</a>
<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
<label class="md-nav__title" for="__toc">
<span class="md-nav__icon md-icon"></span>
Table of contents
</label>
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
<li class="md-nav__item">
<a href="#exploratory-projects" class="md-nav__link">
<span class="md-ellipsis">
Exploratory Projects
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#research-developments" class="md-nav__link">
<span class="md-ellipsis">
Research Developments
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#steering-committee" class="md-nav__link">
<span class="md-ellipsis">
Steering Committee
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#submitting-a-project" class="md-nav__link">
<span class="md-ellipsis">
Submitting a project
</span>
</a>
</li>
</ul>
</nav>
</li>
<li class="md-nav__item">
<a href="https://github.com/swiss-territorial-data-lab" class="md-nav__link">
<span class="md-ellipsis">
GitHub
</span>
</a>
</li>
</ul>
</nav>
</div>
</div>
</div>
<div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
<div class="md-sidebar__scrollwrap">
<div class="md-sidebar__inner">
<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
<label class="md-nav__title" for="__toc">
<span class="md-nav__icon md-icon"></span>
Table of contents
</label>
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
<li class="md-nav__item">
<a href="#exploratory-projects" class="md-nav__link">
<span class="md-ellipsis">
Exploratory Projects
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#research-developments" class="md-nav__link">
<span class="md-ellipsis">
Research Developments
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#steering-committee" class="md-nav__link">
<span class="md-ellipsis">
Steering Committee
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#submitting-a-project" class="md-nav__link">
<span class="md-ellipsis">
Submitting a project
</span>
</a>
</li>
</ul>
</nav>
</div>
</div>
</div>
<div class="md-content" data-md-component="content">
<article class="md-content__inner md-typeset">
<h1 id="swiss-territorial-data-lab-stdl">Swiss Territorial Data Lab - STDL<a class="headerlink" href="#swiss-territorial-data-lab-stdl" title="Permanent link">¶</a></h1>
<p>The STDL aims to promote collective innovation around the Swiss territory and its digital copy. It mainly explores the possibilities provided by data science to improve official land registering.</p>
<p>A multidisciplinary team composed of cantonal, federal and academic partners is reinforced by engineers specialized in geographical data science to tackle the challenges around the management of territorial data-sets.</p>
<p>The developed STDL platform codes and documentation are published under open licenses to allow partners and Swiss territory management actors to leverage the developed technologies.</p>
<h2 id="exploratory-projects">Exploratory Projects<a class="headerlink" href="#exploratory-projects" title="Permanent link">¶</a></h2>
<p>Exploratory projects in the field of the Swiss territorial data are conducted at the demand of institutions or actors of the Swiss territory. The exploratory projects are conducted with the supervision of the principal in order to closely analyze the answers to the specifications along the project. The goal of exploratory project aims to provide proof-of-concept and expertise in the application of technologies to Swiss territorial data.</p>
<details class="abstract" open="open">
<summary><a href="PROJ-SDA/"><span style="text-transform:uppercase; font-weight:bold;"> Automatic detection of soil degraded by human activities and potentially suitable for rehabilitation </span> <br/> December 2024</a></summary>
<p><strong>Clémence Herny (Exolabs) - Clotilde Marmy (Exolabs) - Gwenaëlle Salamin (Exolabs) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo)</strong> <br /> Proposed by the Canton of Ticino - PROJ-SDA <br /> <br /> <em>Each Swiss canton is required to make an inventory of potentially rehabilitatable soils for maintaining the land crop rotation quota. To assist the Canton of Ticino and the Canton of Vaud in this task, the STDL has developed a deep learning-based framework to automatically identify soils degraded by human activities in the past. A model was trained to segment the extent of human activities in a multi-year dataset of aerial imagery. It achieved a f1-score of 0.53. The trained model was applied to historical imagery from 1946 to the present day for the the two cantons. A vector layer showing the distribution of human activities by year was produced in just a few days for each canton. Recall was preferred to precision in order to obtain exhaustive results, but this implies a large number of FP detections. Therefore, a thorough review of the results is necessary before they can be used. Despite the average performance of the model, it allows the identification of new areas that can be added to the inventory and fasten the process compared to a fully manual process.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-SDA/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-BORDERPOINTS/"><span style="text-transform:uppercase; font-weight:bold;"> Classification of border points on old cadastral plans </span> <br/> December 2024</a></summary>
<p><strong>Gwenaëlle Salamin (Exolabs) - Clémence Herny (Exolabs) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo) - Swann Destouches (Uzufly)</strong> <br /> Proposed by the Canton of Fribourg - PROJ-BORDERPOINTS <br /> <br /> <em>Currently, all the lines delineating ground parcels have been approximately digitized in the canton of Fribourg, but the 80'000 border points at the intersections have never been materialized in a dataset. The STDL tested two methods to classify the nature of border points: instance segmentation with a match between detections and approximate border points, and image classification on the neighborhood of each approximate point. Both methods achieved a balanced f1 score of over 0.75 on a test dataset. However, the method based on instance segmentation was proved more versatile for the wide variety of configuration that can be encountered on historical cadastral plans. Consequently, the expert examined only those results at the scale of entire plans and he declared himself satisfied with the quality of the classification.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-BORDERPOINTS/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-VEGROOFS/"><span style="text-transform:uppercase; font-weight:bold;"> Green roofs: automatic detection of roof vegetation, vegetation type and covered surface from aerial imagery </span> <br/> November 2024</a></summary>
<p><strong>Clotilde Marmy (ExoLabs) - Ueli Mauch (Canton of Zürich) - Swann Destouches (Uzufly) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo)</strong> <br /> Proposed by the Canton of Zürich and Canton of Geneva - PROJ-VEGROOFS <br /> <br /> <em>With rising temperatures and increased rainfall, mapping green roofs is becoming important for urban planning in dense areas like Geneva, Zürich and the surrounding areas. Green roofs, whether engineered or spontaneous, provide cooling, rain capture, and habitats, supporting biodiversity. Using national aerial imagery and land survey data, the study focuses on identifying green roofs and distinguishing among various vegetation types, including extensive, intensive, spontaneous, lawn, and terrace categories. Machine learning and deep learning approaches have been developed to detect and classify green roofs in two study areas on the cantons of Geneva and Zürich. Regarding the machine learning setup, statistical descriptors for the roof occupancy were derived from airborne images to train a random forest and a logistic regression predicting if a roof was green or not. Metrics on the test dataset showed that the best performance was achieved by combining a random forest and logistic regression models, trained with pixel statistics from potential vegetated areas defined by NDVI and luminosity thresholds on the original images. This combination yielded a recall of 0.87 for the green class and an F1-score of 0.85. The approach leveraging a deep neural network for classification of the roofs in the six classes of the project is still in development.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-VEGROOFS/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-CADMAP/"><span style="text-transform:uppercase; font-weight:bold;"> Vectorization of historical cadastral plans from the 1850s in the Canton of Geneva </span> <br/> July 2024</a></summary>
<p><br /> <strong>Shanci Li (Uzufly) - Alessandro Cerioni (Canton of Geneva) - Clémence Herny (ExoLabs) - Henrich Duriaux (Canton of Geneva) - Roxane Pott (Swisstopo)</strong><br />
Proposed by the Canton of Geneva - PROJ-CADMAP <br /> <br /> <em>This project aims to vectorize historical cadastral plans using an innovative AI-driven pipeline. To overcome the complexities of plans manually-crafted by experts, the pipeline uses GIS software, computer vision algorithm and advanced deep learning techniques, such as deformable convolutional networks and vision transformers for automated map topology extraction and vectorization. The process includes removing background noise, deciphering symbols and improving vectorization accuracy using graph-based methods. An optical character recognition model extracts parcel indices and all information is combined in a spatially-referenced vector polygon format. Final vectorization yields a median Hausdorff distance of 3 pixels, while semantic classification of the detected polygons achieves an IoU of 0.98. Although most of the tasks are automated, minor manual corrections are still required to achieve satisfactory results. This semi-automated workflow saves at least 90% of the time required for fully manual vectorization of the entire historical plan. The vectorization of historical plans greatly facilitates the analysis of historical geographical data.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-CADMAP/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-ROOFTOPS/"><span style="text-transform:uppercase; font-weight:bold;"> Detection of occupied and free surfaces on rooftops </span> <br/> May 2024</a></summary>
<p><strong>Clémence Herny (Exolabs) - Gwenaëlle Salamin (Exolabs) - Alessandro Cerioni (État de Genève) - Roxane Pott (swisstopo)</strong> <br /> Proposed by the Canton of Geneva- PROJ-ROOFTOPS <br /> <br /> <em>Free roof surfaces offer great potential for the installation of new infrastructure, such as solar panels and vegetated rooftops. In this project, in collaboration with the Canton of Geneva, we have developed and tested three methods to automatically identify occupied and free surfaces on roofs: (1) classification of roof plane occupancy based on a random forest, (2) segmentation of objects in LiDAR point clouds based on a clustering and (3) segmentation of objects in aerial imagery based on a deep learning. The results are vector layers containing information about surface occupancy. The methods developed on a subset of 122 buildings achieved satisfactory performance. About 85% of the roof planes were correctly classified. The segmentation method was able to detect most of the objects with f1 scores of 0.78 and 0.75 for the LiDAR-based segmentation and the image-based segmentation respectively. The global shape of the occupied surface was more difficult to reproduce with a median intersection over the union of 0.35 and 0.37 respectively. The results of all three methods were considered satisfactory by the experts, with 70% to 95% of the results considered acceptable. Considering the quality of the results and the computational time, only the classification method was selected for application at the cantonal level.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-ROOFTOPS/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-SOILS/"><span style="text-transform:uppercase; font-weight:bold;"> Automatic Soil Segmentation </span> <br/> April 2024</a></summary>
<p><strong>Nicolas Beglinger (swisstopo) - Clotilde Marmy (ExoLabs) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo)</strong> <br /> Proposed by the Canton of Fribourg - PROJ-SOILS <br /> <br /> <em>This project focuses on developing an automated methodology to distinguish areas covered by pedological soil from areas comprised of non-soil. The goal is to generate high-resolution maps (10cm) to aid in the location and assessment of polluted soils. Towards this end, we utilize deep learning models to classify land cover types using raw, raster-based aerial imagery and digital elevation models (DEMs). Specifically, we assess models developed by the Institut National de l’Information Géographique et Forestière (IGN), the Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud (HEIG-VD), and the Office Fédéral de la Statistique (OFS). The performance of the models is evaluated with the Matthew's correlation coefficient (MCC) and the Intersection over Union (IoU), as well as with qualitatifve assessments conducted by the beneficiaries of the project. In addition to testing pre-existing models, we fine-tuned the model developed by the HEIG-VD on a dataset specifically created for this project. The fine-tuning aimed to optimize the model performance on the specific use-case and to adapt it to the characteristics of the dataset: higher resolution imagery, different vegetation appearances due to seasonal differences, and a unique classification scheme. Fine-tuning with a mixed-resolution dataset improved the model performance of its application on lower-resolution imagery, which is proposed to be a solution to square artefacts that are common in inferences of attention-based models. Reaching an MCC score of 0.983, the findings demonstrate promising performance. The derived model produces satisfactory results, which have to be evaluated in a broader context before being published by the beneficiaries. Lastly, this report sheds light on potential improvements and highlights considerations for future work.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-SOILS/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-QALIDAR/"><span style="text-transform:uppercase; font-weight:bold;"> Cross-generational change detection in classified LiDAR point clouds for a semi-automated quality control </span> <br/> April 2024</a></summary>
<p><strong>Nicolas Münger (Uzufly) - Gwenaëlle Salamin (ExoLabs) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo)</strong> <br /> Proposed by the Federal Office of Topography swisstopo - PROJ-QALIDAR <br /> <br /> <em>The acquisition of LiDAR data has become standard practice at national and cantonal levels during the recent years in Switzerland. In 2024, swisstopo will complete a comprehensive campaign of 6 years covering the whole Swiss territory. The produced point clouds are classified post-acquisition, i.e. each point is attributed to a certain category, such as "building" or "vegetation". Despite the global control performed by providers, local inconsistencies in the classification persist. To ensure the quality of a Swiss-wide product, extensive time is invested by swisstopo in the control of the classification. This project aims to highlight changes in a new point cloud compared to a previous generation acting as reference. We propose here a method where a common grid is defined for the two generations of point clouds and their information is converted in voxels, summarizing the distribution of classes and comparable one-to-one. This method highlights zones of change by clustering the concerned voxels. Experts of the swisstopo LiDAR team declared themselves satisfied with the precision of the method.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-QALIDAR/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-DQRY-TM/"><span style="text-transform:uppercase; font-weight:bold;"> Automatic detection and observation of mineral extraction sites in Switzerland </span> <br/> January 2024</a></summary>
<p><strong>Clémence Herny (ExoLabs) - Shanci Li (Uzufly) - Alessandro Cerioni (Etat de Genève) - Roxane Pott (Swisstopo)</strong> <br /> Proposed by the Federal Office of Topography swisstopo - TASK-DQRY <br /> <br /> <em>The study of the evolution of mineral extraction sites (MES) is primordial for the management of mineral resources and the assessment of their environmental impact. In this context, swisstopo has solicited the STDL to automate the vectorisation of MES over the years. This tedious task was previously carried out manually and was not regularly updated. Automatic object detection using a deep learning method was applied to SWISSIMAGE RGB orthophotos with a spatial resolution of 1.6 m px<sup>-1</sup>. The trained model proved its ability to accurately detect MES, achieving a f1-score of 82%. Detection by inference was performed on images from 1999 to 2021, enabling us to track the evolution of potential MES over several years. Although the results are satisfactory, a careful examination of the detections must be carried out by experts to validate them as true MES. Despite this remaining manual work involved, the process is faster than a full manual vectorisation and can be used in the future to keep MES information up-to-date.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-DQRY-TM/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-HETRES/"><span style="text-transform:uppercase; font-weight:bold;"> Dieback of beech trees: methodology for determining the health state of beech trees from airborne images and LiDAR point clouds </span> <br/> August 2023</a></summary>
<p><strong>Clotilde Marmy (ExoLabs) - Gwenaëlle Salamin (ExoLabs) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo)</strong> <br /> Proposed by the Republic and Canton of Jura - PROJ-HETRES <br /> <br /> <em>Beech trees are sensitive to drought and repeated episodes can cause dieback. This issue affects the Jura forests requiring the development of new tools for forest management. In this project, descriptors for the health state of beech trees were derived from LiDAR point clouds, airborne images and satellite images to train a random forest predicting the health state per tree in a study area (5 km²) in Ajoie. A map with three classes was produced: healthy, unhealthy, dead. Metrics computed on the test dataset revealed that the model trained with all the descriptors has an overall accuracy up to 0.79, as well as the model trained only with descriptors derived from airborne imagery. When all the descriptors are used, the yearly difference of NDVI between 2018 and 2019, the standard deviation of the blue band, the mean of the NIR band, the mean of the NDVI, the standard deviation of the canopy cover and the LiDAR reflectance appear to be important descriptors.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-HETRES/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-LANDSTATS/"><span style="text-transform:uppercase; font-weight:bold;"> Using spatio-temporal neighbor data information to detect changes in land use and land cover </span> <br/> April 2023</a></summary>
<p><strong>Shanci Li (Uzufly) - Alessandro Cerioni (Canton of Geneva) - Clotilde Marmy (ExoLabs) - Roxane Pott (swisstopo)</strong> <br /> Proposed by the Swiss Federal Statistical Office - PROJ-LANDSTATS <br /> <br /><em>From 2020 on, the Swiss Federal Statistical Office started to update the land use/cover statistics over Switzerland for the fifth time. To help and lessen the heavy workload of the interpretation process, partially or fully automated approaches are being considered. The goal of this project was to evaluate the role of spatio-temporal neighbors in predicting class changes between two periods for each survey sample point. The methodolgy focused on change detection, by finding as many unchanged tiles as possible and miss as few changed tiles as possible. Logistic regression was used to assess the contribution of spatial and temporal neighbors to the change detection. While time deactivation and less-neighbors have a 0.2% decrease on the balanced accuracy, the space deactivation causes 1% decrease. Furthermore, XGBoost, random forest (RF), fully convolutional network (FCN) and recurrent convolutional neural network (RCNN) performance are compared by the means of a custom metric, established with the help of the interpretation team. For the spatial-temporal module, FCN outperforms all the models with a value of 0.259 for the custom metric, whereas the logistic regression indicates a custom metrics of 0.249. Then, FCN and RF are tested to combine the best performing model with the model trained by OFS on image data only. When using temporal-spatial neighors and image data as inputs, the final integration module achieves 0.438 in custom metric, against 0.374 when only the the image data is used.It was conclude that temporal-spatial neighbors showed that they could light the process of tile interpretation.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-LANDSTATS/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-ROADSURF/"><span style="text-transform:uppercase; font-weight:bold;"> Classification of road surfaces </span> <br/> March 2023</a></summary>
<p><strong>Gwenaëlle Salamin (swisstopo) - Clémence Herny (Exolabs) - Roxane Pott (swisstopo) - Alessandro Cerioni (Canton of Geneva)</strong> <br /> Proposed by the Federal Office of Topography swisstopo - PROJ-ROADSURF <br /> <br /> <em>The Swiss road network extends over 83’274 km. Information about the type of road surface is useful not only for the Swiss Federal Roads Office and engineering companies, but also for cyclists and hikers. Currently, the data creation and update is entirely done manually at the Swiss Federal Office of Topography. This is a time-consuming and methodical task, potentially suitable to automation by data science methods. The goal of this project is classifying Swiss roads according to their surface type, natural or artificial. We first searched for statistical differences between these two classes, in order to then perform supervised classification based on machine-learning methods. As we could not find any discriminant feature, we used deep learning methods.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-ROADSURF/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-TREEDET/"><span style="text-transform:uppercase; font-weight:bold;"> Tree Detection from Point Clouds for the Canton of Geneva </span> <br/> March 2022</a></summary>
<p><strong>Alessandro Cerioni (Canton of Geneva) - Flann Chambers (University of Geneva) - Gilles Gay des Combes (CJBG - City of Geneva and University of Geneva) - Adrian Meyer (FHNW) - Roxane Pott (swisstopo)</strong><br /> Proposed by the Canton of Geneva - PROJ-TREEDET <br/> <br /> <em>Trees are essential assets, in urban context among others. Since several years, the Canton of Geneva maintains a digital inventory of isolated (or "urban") trees. This project aimed at designing a methodology to automatically update Geneva's tree inventory, using high-density LiDAR data and off-the-shelf software. Eventually, only the sub-task of detecting and geolocating trees was explored. Comparisons against ground truth data show that the task can be more or less tricky depending on how sparse or dense trees are. In mixed contexts, we managed to reach an accuracy of around 60%, which unfortunately is not high enough to foresee a fully unsupervised process. Still, as discussed in the concluding section there may be room for improvement.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-TREEDET/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-TPNL/"><span style="text-transform:uppercase; font-weight:bold;"> Detection of thermal panels on canton territory to follow renewable energy deployment </span> <br/> February 2022</a></summary>
<p><strong>Nils Hamel (UNIGE) - Huriel Reichel (FHNW)</strong> <br />Project in collaboration with Geneva and Neuchâtel States - TASK-TPNL<br /> <br /> <em>Deployment of renewable energy becomes a major stake in front of our societies challenges. This imposes authorities and domain expert to promote and to demonstrate the deployment of such energetic solutions. In case of thermal panels, politics ask domain expert to certify, along the year, of the amount of deployed surface. In front of such challenge, this project aims to determine to which extent data science can ease the survey of thermal panel installations deployment and how the work of domain expert can be eased.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-TPNL/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-DQRY/"><span style="text-transform:uppercase; font-weight:bold;"> Automatic detection of quarries and the lithology below them in Switzerland </span> <br/> January 2022</a></summary>
<p><strong>Huriel Reichel (FHNW) - Nils Hamel (UNIGE)</strong> <br /> Proposed by the Federal Office of Topography swisstopo - TASK-DQRY <br /> <br /> <em>Mining is an important economic activity in Switzerland and therefore it is monitored by the Confederation through swisstopo. To this points, the identification of quarries has been mode manually, which even being done with very high quality, unfortunately does not follow the constant changing and updating pattern of these features. For this reason, swisstopo contacted the STDL to automatically detect quarries through the whole country. The training was done using SWISSIMAGE with 10cm spatial resolution and the Deep Learning Framework from the STDL. Moreover there were two iteration steps with the domain expert which included the manual correction of detection for new training. Interaction with the domain expert was very relevant for final results and summing to his appreciation, an f1-score of 85% was obtained in the end, which due to peculiar characteristics of quarries can be considered an optimal result.</em> </p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-DQRY/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-TGLN/"><span style="text-transform:uppercase; font-weight:bold;"> Updating the «Cultivable Area» Layer of the Agricultural Office, Canton of Thurgau </span> <br/> June 2021</a></summary>
<p><strong>Adrian Meyer (FHNW) - Pascal Salathé (FHNW)</strong> <br /> Proposed by the Canton of Thurgau - PROJ-TGLN <br /> <br /> <em>The Cultivable agricultural area layer ("LN, Landwirtschaftliche Nutzfläche") is a GIS vector product maintained by the cantonal agricultural offices and serves as the key calculation index for the receipt of direct subsidy contributions to farms. The canton of Thurgau requested a spatial vector layer indicating locations and area consumption extent of the largest silage bale deposits intersecting with the known LN area, since areas used for silage bale storage are not eligible for subsidies. Having detections of such objects readily available greatly reduces the workload of the responsible official by directing the monitoring process to the relevant hotspots. Ultimately public economical damage can be prevented which would result from the payout of unjustified subsidy contributions.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-TGLN/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-TGPOOL/"><span style="text-transform:uppercase; font-weight:bold;"> Swimming Pool Detection for the Canton of Thurgau </span> <br/> April 2021</a></summary>
<p><strong>Adrian Meyer (FHNW) - Alessandro Cerioni (Canton of Geneva)</strong> <br /> Proposed by the Canton of Thurgau - PROJ-TGPOOL <br /> <br /> <em>The Canton of Thurgau entrusted the STDL with the task of producing swimming pool detections over the cantonal area. Specifically interesting was to leverage the ground truth annotation data from the Canton of Geneva to generate a predictive model in Thurgau while using the publicly available SWISSIMAGE aerial imagery datasets provided by swisstopo. The STDL object detection framework produced highly accurate predictions of swimming pools in Thurgau and thereby proved transferability from one canton to another without having to manually redigitize annotations. These promising detections showcase the highly useful potential of this approach by greatly reducing the need of repetitive manual labour.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-TGPOOL/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-REGBL/"><span style="text-transform:uppercase; font-weight:bold;"> Completion of the federal register of buildings and dwellings </span> <br/> February 2021</a></summary>
<p><strong>Nils Hamel (UNIGE) - Huriel Reichel (swisstopo)</strong> <br /> Proposed by the Federal Statistical Office - TASK-REGBL <br /> <br /> <em>The Swiss Federal Statistical Office is in charge of the national Register of of Buildings and Dwellings (RBD) which keep tracks of every existing building in Switzerland. Currently, the register is being completed with building in addition to regular dwellings to offer a reliable and official source of information. The completion of the register introduced issue dues to missing information and their difficulty to be collected. The construction years of the building is one missing information for large amount of register entries. The Statistical Office mandated the STDL to investigate on the possibility to use the Swiss National Maps to extract this missing information using an automated process. A research was conducted in this direction with the development of a proof-of-concept and a reliable methodology to assess the obtained results.</em> </p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-REGBL/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-GEPOOL/"><span style="text-transform:uppercase; font-weight:bold;"> Swimming Pool Detection from Aerial Images over the Canton of Geneva </span> <br/> January 2021</a></summary>
<p><strong>Alessandro Cerioni (Canton of Geneva) - Adrian Meyer (FHNW)</strong> <br /> Proposed by the Canton of Geneva - PROJ-GEPOOL <br /> <br /> <em>Object detection is one of the computer vision tasks which can benefit from Deep Learning methods. The STDL team managed to leverage state-of-art methods and already existing open datasets to first build a swimming pool detector, then to use it to potentially detect unregistered swimming pools over the Canton of Geneva. Despite the success of our approach, we will argue that domain expertise still remains key to post-process detections in order to tell objects which are subject to registration from those which aren't. Pairing semi-automatic Deep Learning methods with domain expertise turns out to pave the way to novel workflows allowing administrations to keep cadastral information up to date.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-GEPOOL/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="PROJ-DTRK/"><span style="text-transform:uppercase; font-weight:bold;"> Difference models applied to the land register </span> <br/> November 2020</a></summary>
<p><strong>Nils Hamel (UNIGE) - Huriel Reichel (swisstopo)</strong> <br />Project scheduled in the STDL research roadmap - TASK-DTRK<br /> <br /> <em>Being able to track modifications in the evolution of geographical datasets is one important aspect in territory management, as a large amount of information can be extracted out of differences models. Differences detection can also be a tool used to assess the evolution of a geographical model through time. In this research project, we apply differences detection on INTERLIS models of the official Swiss land registers in order to emphasize and follow its evolution and to demonstrate that change in reference frames can be detected and assessed.</em></p>
<p><div style="text-align: right"><a class="md-button" href="PROJ-DTRK/">Full article</a></div></p>
</details>
<h2 id="research-developments">Research Developments<a class="headerlink" href="#research-developments" title="Permanent link">¶</a></h2>
<p>Research developments are conducted aside of the research projects to provide a framework of tools and expertise around the Swiss territorial data and related technologies. The research developments are conducted according to the research plan established by the <em>data scientists</em> and validated by the steering committee.</p>
<details class="abstract">
<summary><a href="TASK-IDET/"><strong>OBJECT DETECTION FRAMEWORK</strong> <br/> November 2021</a></summary>
<p>**Alessandro Cerioni (Canton of Geneva) - Clémence Herny (Exolabs) - Adrian Meyer (FHNW) - Gwenaëlle Salamin (Exolabs) ** <br /> Project scheduled in the STDL research roadmap - TASK-IDET <br /> <br /> <em>This strategic component of the STDL consists of the automated analysis of geospatial images using deep learning while providing practical applications for specific use cases. The overall goal is the extraction of vectorized semantic information from remote sensing data. The involved case studies revolve around concrete object detection use cases deploying modern machine learning methods and utilizing a multitude of available datasets. The goal is to arrive at a prototypical platform for object detection which is highly useful not only for cadastre specialists and authorities but also for stakeholders at various contact points in society.</em></p>
<p><div style="text-align: right"><a class="md-button" href="TASK-IDET/">Full article</a></div></p>
</details>
<details class="abstract">
<summary><a href="TASK-DIFF/"><strong>AUTOMATIC DETECTION OF CHANGES IN THE ENVIRONMENT</strong> <br/> November 2020</a></summary>
<p><strong>Nils Hamel (UNIGE)</strong> <br /> Project scheduled in the STDL research roadmap - TASK-DIFF <br /> <br /> <em>Developed at EPFL with the collaboration of Cadastre Suisse to handle large scale geographical models of different nature, the STDL 4D platform offers a robust and efficient indexation methodology allowing to manage storage and access to large-scale models. In addition to spatial indexation, the platform also includes time as part of the indexation, allowing any area to be described by models in both spatial and temporal dimensions. In this development project, the notion of model temporal derivative is explored and proof-of-concepts are implemented in the platform. The goal is to demonstrate that, in addition to their formal content, models coming with different temporal versions can be derived along the time dimension to compute difference models. Such proof-of-concept is developed for both point cloud and vectorial models, demonstrating that the indexation formalism of the platform is able to ease considerably the computation of difference models. This research project demonstrates that the time dimension can be fully exploited in order to access the data it holds.</em></p>
<p><div style="text-align: right"><a class="md-button" href="TASK-DIFF/">Full article</a></div></p>
</details>
<h2 id="steering-committee">Steering Committee<a class="headerlink" href="#steering-committee" title="Permanent link">¶</a></h2>
<p>The steering committee of the Swiss Territorial Data Lab is composed of Swiss public administrations bringing their expertise and competences to guide the conducted projects and developments.</p>
<div align="center">
<img src="assets/images/Steering_Committee.png"> <br />
<i>Members of the STDL steering committee</i>
</div>
<h2 id="submitting-a-project">Submitting a project<a class="headerlink" href="#submitting-a-project" title="Permanent link">¶</a></h2>
<p>To submit a project to the STDL, simply fill <a href="https://www.stdl.ch/fr/Soumettre-un-projet.htm">this form</a>. To contact the STDL, please write an email to <a href="mailto:[email protected]">[email protected]</a>. We will reply as soon as possible!</p>
</article>
</div>
<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
</div>
</main>
<footer class="md-footer">
<div class="md-footer-meta md-typeset">
<div class="md-footer-meta__inner md-grid">
<div class="md-copyright">
<div class="md-copyright__highlight">
Copyright © 2020-2021 Swiss Territorial Data Lab
</div>
Made with
<a href="https://squidfunk.github.io/mkdocs-material/" target="_blank" rel="noopener">
Material for MkDocs
</a>
</div>
<div class="md-social">
<a href="https://github.com/swiss-territorial-data-lab" target="_blank" rel="noopener" title="Repo on Github" class="md-social__link">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
</a>
<a href="mailto:<[email protected]>" target="_blank" rel="noopener" title="Send us an eMail!" class="md-social__link">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M498.1 5.6c10.1 7 15.4 19.1 13.5 31.2l-64 416c-1.5 9.7-7.4 18.2-16 23s-18.9 5.4-28 1.6L284 427.7l-68.5 74.1c-8.9 9.7-22.9 12.9-35.2 8.1S160 493.2 160 480v-83.6c0-4 1.5-7.8 4.2-10.7l167.6-182.9c5.8-6.3 5.6-16-.4-22s-15.7-6.4-22-.7L106 360.8l-88.3-44.2C7.1 311.3.3 300.7 0 288.9s5.9-22.8 16.1-28.7l448-256c10.7-6.1 23.9-5.5 34 1.4z"/></svg>
</a>
</div>
</div>
</div>
</footer>
</div>
<div class="md-dialog" data-md-component="dialog">
<div class="md-dialog__inner md-typeset"></div>
</div>
<script id="__config" type="application/json">{"base": ".", "features": ["navigation.expand"], "search": "assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
<script src="assets/javascripts/bundle.a7c05c9e.min.js"></script>
<script src="assets/javascripts/config.js"></script>
<script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
<script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
</body>
</html>