Merge branch 'juan.sardi-barzallo-master-patch-99044' into 'master'

Juan.sardi barzallo master patch 99044

See merge request !1

README.md

+<<<<<<< README.md
+# LiDAR Point Cloud Semantic Segmentation
+
+=======
+>>>>>>> README.md
 ## Overview
-Process LiDAR point cloud data of the HFT campus to classify surfaces (ground, buildings, walkable areas). Export results into GIS-ready formats for digital twin integration.
+The relevance of point cloud data for capturing 3D environmental data in various scenarios, such as robotics, computer vision, autonomous driving, building and city digital twin models, is increasing rapidly.  Despite its ability to accurately describe the geometry or shape of surface elements, it is still unstructured data, meaning it is only a group of points represented in 3D space with x, y, and z coordinates, as well as other features, such as color (R, G, B). Therefore, it is essential to provide meaning and structure to this data by segmenting or classifying the points into groups with specific semantic labels. 
+
+Semantic segmentation of point clouds is time-consuming, especially for large datasets. 
+
+The goal of this task is to achieve semi-automatic (supervised) or automatic (unsupervised) semantic segmentation of the LiDAR point cloud dataset of one of the stories of the HFT building (Bau 4) and/or its exterior urban areas (streets, signs, sidewalks, etc.).
+
+## Targets:
+
+-Classify the building's LiDAR point cloud data into the following classes: wall, floor, ceiling, window, door, column, beam, stairs/ramps, roof, furniture, opening/passageway, and others. Alternatively, classify the exterior urban area's LiDAR data into the following classes: building, ground, road, sidewalk, signage, vegetation, vehicles, water, and others.
+
+-Visualize the data as separate, labeled point clouds (LAS, PTS, XYZ formats).  
 
-## Targets
-- Classify LiDAR into ground (DTM), buildings, walkable areas.  
-- Export outputs (GeoJSON, GPX, 3D tiles).  
-- Visualize in map applications.  
+-Visualize in map applications.  
 
-## Data Needs
-- LiDAR scans of HFT campus  
-- DEM/DTM alternative if LiDAR unavailable  
-- Reference: [HFT Data Wiki](https://wiki.hft-stuttgart.de/pages/viewpage.action?pageId=121441587&spaceKey=STRATINPUT&title=Datengrundlage%2B3D-Geb%C3%A4udemodell%2BHFT)  
+## Data Needs:
+- LiDAR scans of the HFT campus.  
+- 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).  
 
-## Acceptance Criteria
+## Acceptance Criteria:
 - Reasonable segmentation quality  
-- Outputs load in QGIS / web map  
-- README with reproducible steps  
+- 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)
-- Formats: LAS/LAZ (point clouds), GeoJSON/3D Tiles  
+- 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