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Correct typos in the names of authors and projects
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@GwenaelleSa
GwenaelleSa committed Feb 2, 2024
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Expand Up @@ -371,7 +371,7 @@ <h2 id="exploratory-projects">Exploratory Projects<a class="headerlink" href="#e
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<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 Federal Office of Topography swisstopo - PROJ-ROADSURF <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><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>
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Expand Down Expand Up @@ -428,7 +428,7 @@ <h2 id="research-developments">Research Developments<a class="headerlink" href="
<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>
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<summary><a href="TASK-IDET/"><strong>OBJECT DETECTION FRAMEWORK</strong> <br/> November 2021</a></summary>
<p><strong>Alessandro Cerioni (Canton of Geneva) - Adrian Meyer (FHNW)</strong> <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. In a second iteration multiclass detections follow, later full semantic surface layers can be produced leveraging the obtained datasets 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><strong>Alessandro Cerioni (Canton of Geneva) - Clémence Herny (Exolabs) - Adrian Meyer (FHNW) - Gwenaëlle Salamin (Exolabs) </strong> <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>
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