3D Model of the City Using LiDAR and Visualization of Flood in Three-Dimension

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1 3D Model of the City Using LiDAR and Visualization of Flood in Three-Dimension R.Queen Suraajini, Department of Civil Engineering, College of Engineering Guindy, Anna University, India, K.R.Sharanya, Department of Civil Engineering, College of Engineering Guindy, Anna University, India T.Sambath, Department of Civil Engineering, College of Engineering Guindy, Anna University, India L. Subbaraj, Department of Civil Engineering, College of Engineering Guindy, Anna University, India Introduction: Floods occur naturally. There has been increased risk in flood prone areas due to urbanization and increasing population. GIS is used to store, manipulate and visualize the spatial and nonspatial data. GIS is considered as the powerful tool to analyze the flood data due to its data manipulating techniques. By integrating GIS with hydrological models we can visualize the flood. This paper will helpful in flood management and decision making processes. ArcGIS is used for 3D city modeling and ISIS is used for flood simulation. The impervious area has increased in the city due to urbanization. So the inundation occurs frequently and cause huge loses. So the forecasting should be accurate. The simulation model will be helpful in assessing the amount of area that will be affected during the particular amount of rainfall. The simulation of the flood will help to evacuate people and to do disaster management planning for the planners. Study Area: The city of Chennai has long history of flooding. Chennai, earlier known Madras, is one of the four major metropolitan cities, located in southern India. It lies between 12 09, NE and 13 09, NE. It has a population of 6.04 million and an area of Sq.km. The population growth is increasing at an average rate of 25 percent per decade. Chennai has two administrative boundaries; the outer boundary is Chennai metropolitan boundary which encompasses the suburban areas while the inner one is the corporation boundary which includes only the urban area. It shares its boundary with Andhra Pradesh at the North, Bay of Bengal at the East, Indian Ocean at the South and Kerala at its West. Data: The input for modeling includes LiDAR data, Orthophoto, DEM, Drainage map.

2 Software The software for developing 3D city model is as follows GIS software Google Sketchup ESRI plugin 3D modeling/authoring software Google Sketchup Virtual reality player Cortona player Flood visualization Floodarea Methodology: First step is to extract buildings and roads from the LiDAR data of the city. The second step is to add height information to the extracted buildings. The third step is to create the 3D model of the city using ArcGIS and Google Sketchup. The fourth step is to calculate the land overflow caused by the storm. The final step is to do simulation of the flood in the 3D model. 1. Generating 3D models Laser range images and existing maps or 2D geographic database is used for reconstruct 3D spatial objects in urban areas. The strategy for generating a 3D city model as follows: Interpolation of a DSM and a DTM from the original data. Laser range image filtering and segmentation. Generation of 2D spatial objects in the ground surface by 2D map raster-vector conversion. Reconstruction of parametric models. Texture mapping and visualization. Interpolation of original LIDAR data First we generate a Digital Surface Map (DSM) i.e a regular array of altitude values, by resampling the unstructured point cloud of airborne laser scans, and filling remaining holes in the DSM by nearest-neighbor interpolation. Range filtering and segmentation Airborne laser scanners allow direct measurement of terrain surface, including objects like trees or buildings which rise from the ground. Small geometrical errors like the non - planar

3 triangulation of a planar façade may disturb the impression of looking at a real scene. So buildings have to be separated from the terrain surface. The DTM (bare earth, slope, aspect) are subtracted from the DSM. The remaining are then classified into buildings, trees and other features (shrubs, cars). Most buildings are orthogonal in shape with flat or sloped roofs while trees and shrubs are more dome-shaped with some irregularities within them. Therefore the texture variance provides a good separation between buildings and trees. By applying height thresholds to the normalized DSM thus created, an initial building mask is obtained. Mathematical Morphology (MM) is used for range filtering and segmentation. MM operators (such as dilation, erosion, opening, closing, hit or miss, thinning..) can be described as a kind of combination of shift and logical operators. Opening filter is used to remove dirty voxels and small connected volumes. Closing filter is used to fill the small holes within surfaces and to link short gaps among objects. Small regions are filtered out by MM operators. Larger regions are segmented by means of planar segmentation. Segmentation 3D objects are also based on open processing. The basic idea of MM based object segmentation is filtering all the parts smaller than the given structuring elements, then segmenting the objects by the logic difference operations between the original data set and the filtered data set. The algorithm for feature recovering is based on the conditional dilation operations, in which the segmented object parts serve as the dilation seeds and the original 3D data set data set serve as the masking field for limiting dilated ranges. Building detection Using the filtered data the building is detected. This DSM can be utilized for generating a model from airborne view. Two digital elevation models are derived by interpolation: a DTM is computed from the points classifieds as terrain points with a high degree of smoothing, whereas a DSM is computed from all points without smoothing. An initial building mask is created by thresholding the height differences between the DSM and the DTM. Some of the regions will still contain some terrain features. These regions can be eliminated by evaluating a terrain roughness. The classification of texture such as homogeneous, linear, or point-like, helps in evaluating terrain roughness. For each initial building region, the number of point-like pixels is counted. The regions containing more than 50% point-like pixels are very likely to contain vegetation rather than buildings, and they are eliminated.

4 Acquisition of 2D ground-surfaces from existing maps The basic procedure for raster- vector conversion of 2D scanned maps contains following steps: MM based basic image filtering, segmentation and feature extraction. 2D raster-vector conversion, data compression, and line feature refinement. Generation of topological relation and linked to recognized attribute features (such as contour elevations and the number of land parcels). Using above algorithm different kinds of feature such as broken points, crossed points, line width, pattern length, pattern density or distribution can be segmented. After extracting the buildings and road, we have add the height information to the buildings as one of the attributes. It is then exported to Google sketchup using ESRIGooglesketchup plug-in. In Google sketchup we can get the 3D model. This 3D model has to be texture mapped. Texture mapping: The goal of texture processing is to provide a rectified image for each visible building face. Hence for each image the corresponding façade polygon has to be selected from the 3D city model generated in the previous processing step. For texture mapping the image has to be correctly positioned, orientated and scaled to represent its associated surface. The image section representing a planar surface is rectified by applying projective transformation, which is default in Google SketchUp. Using Google SketchUp texture mapping is done. The model is then exported in vrml format for visualization. Validation of the 3D city model: The Google sketchup model in then exported to Google Earth. By using Google Earth we can check whether the model is geo-referenced correctly or not. If the model orients itself correctly to Google earth, then the model is correctly Georeferenced. 2. Floodarea: Floodarea is simulation software where we can view the flood in 3D. It is ArcGIS extension and has spatial analyst capability. By using this software we can delineate flood

5 inundated areas. The flood area is calculated based on the drainage network and the hydrograph. Based on the weightage it calculates the flood inundated areas and displays in 3D. Results: As a pilot study, we have created 3D model of Anna University, Chennai. We have simulated the flood in 3D for Anna University. a. Model1 : 3D model of Anna university, Chennai (CEG campus) B. Model2: 3D model of Anna University, Chennai (CEG campus)

6 Conclusion: Thus we have developed the 3D model of Anna University and the flood is simulated. Author Details: Dr. L. Subbaraj, Scientist D, Institute of Remote Sensing, Anna University, Chennai R.Queen Suraajini B.E Geo-Informatics, Department of Civil Engineering, College of Engineering Guindy, Anna University, Chennai K.R.Sharanya B.E Geo-Informatics, Department of Civil Engineering, College of Engineering Guindy, Anna University, Chennai /82, Josium Shanmugam street, Aruppukottai, Virudhunagar T. Sambath B.E Geo-Informatics, Department of Civil Engineering, College of Engineering Guindy, Anna University, Chennai

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