Compared to traditional photogrammetry, GPS and land surveying, LIDAR provide: 1) higher and homogenous accuracy, 2) fast acquisition of massive data (point clouds) and processing, 3) minimum human dependence, 4) weather/light independence, 5) canopy and vegetation penetration through LIDAR pulses, 6) higher data density (very dense point clouds), 7) Ground Control Points (GCPs) independence, i.e., few GCPs are needed for the georeference process and for the qualitative and quantitative control (this makes LIDAR ideal for mapping inaccessible and featureless areas), 8) additional information such intensity, multiple returns, etc., and 9) cost-effectiveness for big covered areas, e.g., for national/public work studies or services. However, several research approaches have been proposed the recent years that fuse LIDAR and aerial imagery to exploit both: the good geometric quality of ALS and the spectral image information for object detection and feature extraction, forest canopy modeling, 3D mapping and reconstruction, 3D city modeling in terms of Level of Details (LoDs), smart cities, BIMs, etc. Despite the constant technological development of computer vision, computer graphics and aerial/space technology, the accurate, reliable and rapid 3D reconstruction of complex scenes such the industrial ones still remains challenging.
In this study a multi-modal data fusion approach is implemented, that is LIDAR and aerial RGB imagery, to create 3D modern cartographic backgrounds for smart and safe management of industrial areas. A complex industrial area of 4.3 km2 near at Patras city, Greece was used as case study.
At first step, the LIDAR point clouds and the aerial RGB imagery were simultaneously acquired over the area of interest. The adjustment of neighboring LIDAR strips was firstly performed through strip alignment and then a georeference process using ground control points (GCPs) and check points (CPs) was carried out afterwards. The LIDAR point clouds were classified and refined to extract building, vegetation and ground points. The ground points were used to extract the corresponding Digital Terrain Model (DTM) of the area of interest. Also an aerial triangulation of the aerial imagery was conducted via bundle adjustment using GPS/INS data and GCPs/CPs. Finally the orthoimages of the area of interest were generated using the LIDAR/DTM. Concerning the used GCPs and CPs: 1) were consisted of characteristic points (e.g., pitches, corners at buildings, etc), 2) were measured by the ComNav Τ300 receiver station both on GPS/RTK and GPS/Relative static modes, and 3) their coordinates were calculated using the GNSS network of reference stations of SmartNet Europe/MetricaNET.
Point density varied considerably over the whole block depending on the LIDAR strip overlap, i.e., 5 points/m2 and 30 points/m2 for regions covered by only one strip and more than one strip respectively. Multiple echoes and intensities were recorded. The ground sample distance (GSD) of the orthoimages was 10 cm. Several 2D and 3D feature/spatial data products were extracted using the Erdas Imagine software (Raster and Point cloud tools, Imagine Photogrammetry, Terrain analysis tool and VirtualGIS tool) such: 1) true-ortho intensity images, 2) Digital Surfaces Models (DSMs), Digital Terrain Models (DTMs) and RGB orthoimages, 3) 3D LIDAR point clouds coloured by the orthoimages, 4) inspection of critical infrastructures such power lines and street lighting network, 5) automatic detection of buildings and 3D modeling in LoD1, and 6) aspect and slope maps of the DSMs. The results demonstrate the utility and the functionality of LIDAR points clouds for high-level 3D reconstruction of complex scenes such the industrial areas. The fusion with the aerial imagery contributed to the extraction of appropriate cartographic backgrounds in order to potentially feed smart management systems such as BIMs.
|3D LIDAR point cloud coloured by the intensity (left); True-ortho intensity image (right)|
|Superimposition of the DSM to the orthoimage|
|3D LIDAR point cloud coloured by the orthoimage|
|Inspection of power lines and street lighting network|
|3D building models in LoD1 visualized in a 3D Virtual GIS environment|
|Slope (top) and aspect (bottom) maps of the DSM|
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