(Automation of Earth Surface Extracting From Aerial LIDAR)

Various methods have been developed to measure the location of physical objects on a landscape with high positional accuracy. A new method that has been gaining popularity is the Airborne Light Detection and Ranging (LiDAR). LiDAR works by scanning a landscape (the combination of ground, buildings, vegetation, etc.,) by multiple passes. In each scan (pass), pulses of laser light are emitted from an airborne platform and their return time is measured, thus enabling the range from the point of emission to the landscape to be determined. The product of airborne laser scanning is a cloud of points located in a 3D space. ALS is capable of delivering clouds of very dense and accurate points that represent the landscape in a relatively short time. However, in spite of the ability to measure objects with high positional accuracy, the automatic detection and interpretation of individual objects in landscapes remains a challenge. An example of such a challenge is the classification of the cloud points produced by ALS. The classification of LiDAR cloud points consists first of all of assigning the points as either object points or bare ground ones. The points labeled object points are then further classified as either buildings or vegetation. As a measurement technique, LiDAR is highly promising, research has been conducted here to automate the detection of bare ground, buildings and vegetation in LiDAR cloud points. In this Research, we describe a new automated scheme that utilizes the so-called “Edge Topology based Iterative Segmentation” (ETIS) model to classify the LiDAR points as ground and objects points. First ground seed points based on edges topology are to be selected and then the initial DTM is to be constructed, the second step is an iterative densification of the DTM using a cloud point segmentation method based on local slope parameter. General ground point filtering parameters have been used was achieved in this method, instead of scene- wise optimization of the parameters, in a way that many groups of benchmark datasets have been without changing the thresholds values. Data provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission, have been used to compare the performance of ETIS. The new method is also tested against the 16 other publicized filtering methods. The results indicat that the proposed method is capable of producing a high fidelity terrain model.

Automation of Topographic map from CAD to Geo Database in GIS

GIS software provide manual import tools to maps produced on CAD software to be transformed to geo-database. This operation consumes time and effort. The "transformation" however will not be adequate unless we analyze the relation between CAD and GIS software in preparing maps. The question raised here if this relation competitive or integrative? This research tries to answer this matter by studying it from different angles: modeling, spatial feature, scale, spatial analysis and data management. Analyses reveal that this relation isn't competitive at all, but rather integrative, as CAD software produce technical\design plans, whereas GIS software are dedicated for the production of general and thematic maps. Thus, CAD based spatial data (topographic, cadastral, master plans) could be "up-graded" to be efficient in GIS environment. However available tools to make this are basically manual, and for that, an automated approach was developed to execute this upgrade from CAD to GIS. This new approach was applied and evaluated and the output results were satisfactory accurate, time\effort saving, and indeed didn't miss any of CAD layers. This all could be achieved if being conditioned with the approach constrains.