Update abstracts of PROJ-DQRY-TM

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@@ -511,7 +511,7 @@ <h1 id="automatic-detection-and-observation-of-mineral-extraction-sites-in-switz
 <p>Clémence Herny (Exolabs), Shanci Li (Uzufly), Alessandro Cerioni (État de Genève), Roxane Pott (swisstopo)</p>
 <p>Proposed by swisstopo - PROJ-DQRY-TM <br />
 October 2022 to February 2023 - Published on January 2024 <br /></p>
-<p><em><strong>Abstract</strong>: Study the evolution of mineral extraction sites (MES) is of primary importance for assessing the availability of mineral resources, managing MES, and evaluating the environmental impact of mining activity. In Switzerland, MES are inventoried locally by cantons and at the federal level by swisstopo. The later performs manual vectorisation of MES boundaries. Unfortunately, although the data is high quality, it is not regularly updated. To automate this tedious task and better observe the development of MES, swisstopo has solicited the STDL to carry out automatic detection of MES in Switzerland over years. We performed instance segmentation using a deep learning method to automatically detect MES in aerial images. The detection model was trained with 266 labels and orthophotos from the SWISSIMAGE RGB mosaic published in 2020. The selected trained model achieved a f1-score of 82% on the validation dataset. The model was used to perform detection by inference of potential MES in SWISSIMAGE RGB orthophotos from 1999 to 2021. The model shows good ability to detect potential MES with about 82% of labels detected for the 2020 SWISSIMAGE mosaic. Detection obtained with SWISSIMAGE orthophotos acquired in different years can be tracked to observe the temporal evolution of the detected object. The framework developed can perform detection in an area of interest (about a third of Switzerland) in just a few hours, which is a major advantage over manual mapping. We acknowledge that there is some missed and false detection in the final product, and the results need to be reviewed and validated by domain experts before being analysed and interpreted. The results can be used to perform statistics over time and update MES evolution in future image acquisitions.</em></p>
+<p><em><strong>Abstract</strong>: Studying the evolution of mineral extraction sites (MES) is of primary importance for assessing the availability of mineral resources, managing MES and evaluating the impact of mining activity on the environment. In Switzerland, MES are inventoried at local level by the cantons and at federal level by swisstopo. The latter performs manual vectorisation of MES boundaries. Unfortunately, although the data is of high quality, it is not regularly updated. To automate this tedious task and to better observe the evolution of MES, swisstopo has solicited the STDL to carry out an automatic detection of MES in Switzerland over the years. We performed instance segmentation using a deep learning method to automatically detect MES in RGB aerial images with a spatial resolution of 1.6 m px<sup>-1</sup>. The detection model was trained with 266 labels and orthophotos from the SWISSIMAGE RGB mosaic published in 2020. The selected trained model achieved a f1-score of 82% on the validation dataset. The model was used to do detection by inference of potential MES in SWISSIMAGE RGB orthophotos from 1999 to 2021. The model shows good ability to detect potential MES with about 82% of labels detected for the 2020 SWISSIMAGE mosaic. The detections obtained with SWISSIMAGE orthophotos acquired over different years can be tracked to observe their temporal evolution. The framework developed can perform detection in an area of interest (about a third of Switzerland at the most) in just a few hours, which is a major advantage over manual mapping. We acknowledge that there are some missed and false detections in the final product, and the results need to be reviewed and validated by domain experts before being analysed and interpreted. The results can be used to perform statistics over time and update MES evolution in future image acquisitions.</em></p>
 <h2 id="1-introduction">1. Introduction<a class="headerlink" href="#1-introduction" title="Permanent link">&para;</a></h2>
 <h3 id="11-context">1.1 Context<a class="headerlink" href="#11-context" title="Permanent link">&para;</a></h3>
 <p>Mineral extraction constitutes a strategic activity worldwide, including in Switzerland. Demand for mineral resources has been growing significantly in recent decades<sup id="fnref:1"><a class="footnote-ref" href="#fn:1">1</a></sup>, mainly due to the rapid increase in the production of batteries and electronic chips, or buildings construction, for example. As a result, the exploitation of some resources, such as rare earth elements, lithium, or sand, is putting pressure on their availability. Being able to observe the development of mineral extraction sites (MES) is of primary importance to adapting mining strategy and anticipating demand and shortage. Mining has also strong environmental and societal impact<sup id="fnref:2"><a class="footnote-ref" href="#fn:2">2</a></sup><sup id="fnref:3"><a class="footnote-ref" href="#fn:3">3</a></sup>. It implies the extraction of rocks and minerals from water ponds, cliffs, and quarries. The surface affected, initially natural areas, can reach up to thousands of square kilometres<sup id="fnref2:1"><a class="footnote-ref" href="#fn:1">1</a></sup>. The extraction of some minerals could lead to soil and water pollution and involves polluting truck transport. Economic and political interests of some resources might overwhelm land protection, and conflicts are gradually intensifying<sup id="fnref2:2"><a class="footnote-ref" href="#fn:2">2</a></sup>. </p>

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@@ -367,7 +367,7 @@ <h2 id="exploratory-projects">Exploratory Projects<a class="headerlink" href="#e
 <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>
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 <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>Study the evolution of mineral extraction sites (MES) is primordial for mineral resources management and environmental impact assessment. In this context, swisstopo has solicited the STDL to automate the vectorisation of MES over the years. This tedious task was previously performed manually and was not regularly updated. Automatic object detection was performed using a deep learning method on SWISSIMAGE RGB orthophotos (spatial resolution of 1.6 m px<sup>-1</sup>). The model proved its ability to accurately detect MES by achieving a f1-score of 82%. Detection by inference of potential MES was performed on images from 1999 to 2021, enabling us to track the evolution of MES over several years. Although the results are satisfactory, a careful examination of the detections must be performed by experts to validate them as real 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><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>
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