Update algorithm_catalog/eurac_pv_farm_detection/eurac_pv_farm_detection_description.md

Co-authored-by: Serge Croisé <SergeCroise@users.noreply.github.com>

Author: HansVRP

algorithm_catalog/eurac_pv_farm_detection/eurac_pv_farm_detection_description.md

@@ -4,7 +4,7 @@ Photovoltaic farms (PV farms) mapping is essential for establishing valid polici
 
 To resolve this limitation we implemented the detection workflow for mapping solar farms using Sentinel-2 imagery in an openEO process [1].
 
-Open-source data is used to construct the training dataset, leveraging OpenStreetMap (OSM) to gather PV farms polygons across different countries. Different filtering techniques are involved in the creation of the training set, in particular land cover and terrain. To ensure model robustness, we leveraged the temporal resolution of Sentinel-2 L2A data and utilized openEO to create a reusable workflow that simplifies the data access in the cloud, allowing the collection of training samples over Europe efficiently.
+Open-source data is used to construct the training dataset, leveraging OpenStreetMap (OSM) to gather PV farm polygons across different countries. Different filtering techniques are involved in the creation of the training set, in particular land cover and terrain. To ensure model robustness, we leveraged the temporal resolution of Sentinel-2 L2A data and utilized openEO to create a reusable workflow that simplifies the data access in the cloud, allowing the collection of training samples over Europe efficiently.
 
 This workflow includes preprocessing steps such as cloud masking, gap filling, outliers filtering as well as feature extraction. Alot of effort is put in the best training samples generation, ensuring an optimal starting point for the subsequent steps. After compiling the training dataset, we conducted a statistical discrimination analysis of different pixel-level models to determine the most effective one. Our goal is to compare time-series machine learning (ML) models like InceptionTime, which uses 3D data as input, with tree-based models like Random Forest (RF), which employs 2D data along with feature engineering.