Deploying to main from @ swiss-territorial-data-lab/stdl-tech-website@79f05b8 🚀

Author: acerioni

index.html

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 <h1 id="swiss-territorial-data-lab-stdl">Swiss Territorial Data Lab - STDL<a class="headerlink" href="#swiss-territorial-data-lab-stdl" title="Permanent link">&para;</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>
+<p>The Swiss Territorial Data Lab (STDL) aims to solve some of the problems actually faced by public administrations, by applying data science to geographical data. The STDL promotes collective innovation and co-creation by bringing together domain experts from public administrations and geodata scientists.</p>
+<p>Code and methodologies developed by the STDL are released under open licence terms, in order to promote adoption and collaboration not only by STDL partners but also by third parties.</p>
 <h2 id="exploratory-projects">Exploratory Projects<a class="headerlink" href="#exploratory-projects" title="Permanent link">&para;</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>
+<p>The exploratory projects listed here-below were carried out jointly by domain experts and geodata scientists from the STDL. Typically, each one lasted between six and eight months, during which prototypes were developed and assessed through qualitative and quantitative metrics.</p>
 <details class="abstract" open="open">
 <summary><a href="PROJ-DOLINES/"><span style="text-transform:uppercase; font-weight:bold;"> Mapping sinkholes of Switzerland based on DEM processing </span> <br/> June 2025</a></summary>
 <p><strong>Gwenaëlle Salamin (ExoLabs) - Clotilde Marmy (ExoLabs) - Clémence Herny (ExoLabs) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo)</strong> <br /> Proposed by the geology domain of the Federal Office of Topography swisstopo - PROJ-DOLINES<br /> <br /> <em>Sinkholes, or dolines, are key geomorphological features in Switzerland’s karst landscapes, yet their mapping remains inconsistent due to heterogeneous inventories and subjective expert interpretation. This study aimed to standardize and accelerate sinkhole cartography across diverse Swiss geological contexts by testing four automatic detection methods using LiDAR-derived digital terrain models. Each method was optimized based on ground truth data and evaluated through quantitative metrics and expert validation. While six of eleven areas of interest showed satisfactory detection of sinkhole-dense zones, smaller sinkholes were frequently missed, and overall automation was hindered by and incomplete ground truth dataset. These findings underscore the necessity of establishing a clear, standardized definition of sinkholes and improving ground truth quality before reliable automatic detection can be achieved.</em></p>
@@ -510,8 +509,8 @@ <h2 id="exploratory-projects">Exploratory Projects<a class="headerlink" href="#e
 <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>
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-<h2 id="research-developments">Research Developments<a class="headerlink" href="#research-developments" title="Permanent link">&para;</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>
+<h2 id="tools">Tools<a class="headerlink" href="#tools" title="Permanent link">&para;</a></h2>
+<p>The solutions listed here-below were initially developed within the frame of a specific project, then generalized and reused for other applications as well. Their source code is available on the <a href="https://github.com/swiss-territorial-data-lab/">STDL's GitHub</a>.</p>
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 <summary><a href="TASK-IDET/"><strong>OBJECT DETECTION FRAMEWORK</strong></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>