Update README.md

README.md

@@ -14,16 +14,16 @@ The goal of this task is to achieve semi-automatic (supervised) or automatic (un
 -Visualize in map applications.  
 
 ## Data Needs:
-- [LiDAR scans of the HFT campus] (https://hftstuttgartde.sharepoint.com/:f:/s/Informatic-weekend-2025/EmJmSuv8yL1Pq6lqgFEeh2oBdKe6v6bHmrcyL3zCeRhwpw?e=IQWEcL).  
+- [LiDAR scans of the HFT campus](https://hftstuttgartde.sharepoint.com/:f:/s/Informatic-weekend-2025/EmJmSuv8yL1Pq6lqgFEeh2oBdKe6v6bHmrcyL3zCeRhwpw?e=IQWEcL).  
 - DEM/DTM alternative if LiDAR is unavailable.  
-- Reference: [HFT Data Wiki] (https://wiki.hft-stuttgart.de/pages/viewpage.action?pageId=121441587&spaceKey=STRATINPUT&title=Datengrundlage%2B3D-Geb%C3%A4udemodell%2BHFT).  
+- Reference: [HFT Data Wiki](https://wiki.hft-stuttgart.de/pages/viewpage.action?pageId=121441587&spaceKey=STRATINPUT&title=Datengrundlage%2B3D-Geb%C3%A4udemodell%2BHFT).  
 
 ## Acceptance Criteria:
 - Reasonable segmentation quality  
 - A README with reproducible steps (workflow and code) for application to the rest of the HFT buildings for semantic segmentation. 
 
 ## Environment Setup
-- Tools: [CloudCompare] (https://github.com/CloudCompare/CloudCompare) (point cloud segmentation), Meshlab (mesh editing), QGIS (GIS integration)
-- Semi-automatic (supervised) classfication algorithms (as reference) - Random Forest and Gradient Boosting (https://github.com/Yarroudh/SemanticML)
+- Tools: [CloudCompare](https://github.com/CloudCompare/CloudCompare) (point cloud segmentation), Meshlab (mesh editing), QGIS (GIS integration)
+- Semi-automatic (supervised) classfication algorithms (as reference) - [Random Forest and Gradient Boosting](https://github.com/Yarroudh/SemanticML)
 - Formats: pts, e57, rcp/rcs (point clouds) 
 - OS: Mac / Windows