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PROJ-SOILS/index.html

@@ -592,7 +592,7 @@ <h1 id="automatic-soil-segmentation">Automatic Soil Segmentation<a class="header
 <p>Nicolas Beglinger (swisstopo) - Clotilde Marmy (ExoLabs) - Alessandro Cerioni (Canton of Geneva) - Roxane Pott (swisstopo) - Thilo Dürr-Auster (Canton of Fribourg) - Daniel Käser (Canton of Fribourg)</p>
 <p>Proposed by the <a href="https://www.fr.ch/dime/sen">Service de l'environnement</a> (SEn) of the Canton of Fribourg - PROJ-SOILS<br/>
 May 2023 to April 2024 - Published in April 2024</p>
-<p>All code is available on <a href="https://github.com/swiss-territorial-data-lab/proj-soils">GitHub</a>.</p>
+<p>All code is available on <a href="https://github.com/swiss-territorial-data-lab/proj-soil">GitHub</a>.</p>
 <p><em><strong>Abstract</strong>: 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>
 <h2 id="1-introduction">1. Introduction<a class="headerlink" href="#1-introduction" title="Permanent link">&para;</a></h2>
 <p>Polluted soils present diverse health risks. In particular, contamination with lead, mercury, and polycyclic aromatic hydrocarbons (PAHs) currently mobilizes the Federal Office for the Environment <sup id="fnref:1"><a class="footnote-ref" href="#fn:1">1</a></sup>. Therefore, it is necessary to know about the location of contaminated soils, like for prevention and management of soil displacement during construction works.</p>