ABSTRACT:This paper is reflecting a study on developing and reconstructing a 3D model from registering an aerial image on a point clouds or a DTM. The point clouds has been chosen as the main data for reconstructing a 3D model. For achieving the aspects of the study, all objects were individually detected and extracted from the point clouds and captured in a particular layer in a CAD environment. Then each object will be converted to a raster format and will be registered on the image. This process is called the reverse registration in this study. Since the object segmentation and detection from a digital image is a complex process, the reverse registration is implemented in order to assist the process of the object detection from the image. This paper will discuss two methods of object detection from point clouds for the reverse registration. These methods were proposed and implemented for this study. Also, the paper will discuss the reverse registration and how this method improves the process of the object detection and extraction from the image. Discussion of reconstructing a 3D model from registering the digital image on a DTM or DSM (both of which developed from the point clouds data) is another goal of this paper.

ABSTRACT:With growth of urbanisation, there is a requirement for using the leverage of smart city in city management. The core of smart city is Information and Communication Technologies (ICT), and one of its elements is smart transport which includes sustainable transport and Intelligent Transport Systems (ITS). Cities and especially megacities are facing urgent transport challenge in traffic management. Geospatial can provide reliable tools for monitoring and coordinating traffic. In this paper a method for monitoring and managing the ongoing traffic in roads using aerial images and CCTV will be addressed. In this method, the road network was initially extracted and geo-referenced and captured in a 3D model. The aim is to detect and geo-referenced any vehicles on the road from images in order to assess the density and the volume of vehicles on the roads. If a traffic jam was recognised from the images, an alternative route would be suggested for easing the traffic jam. In a separate test, a road network was replicated in the computer and a simulated traffic was implemented in order to assess the traffic management during a pick time using this method.

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This paper discusses a new approach in object extraction from aerial images with association of point cloud data. The extracted objects are captured in a 3D space for reconstructing a 3D model. The process includes three steps. In the first step the targeted objects are extracted from point cloud data and captured in a 3D space. The objects include buildings, trees, roads and background or terrain. In the second step the extracted objects are registered to the aerial image for assisting the object detection. Finally, the extracted objects from the aerial image are registered on the original 3D model for conversion to the point cloud data and then are captured in a 3D space for reconstructing a new 3D model. The final 3D model is flexible and editable. The objects can be edited, audited, and manipulated without affecting another objects or ruin the 3D model. Also, more data can be integrated in the 3D model improve its quality. The aspects of this project are: to reconstruct the final 3D model, and then each object can be interactively updated or modified without affecting the whole 3D model, and to provide a database for other users such as 3D GIS, city management and planning, Disaster Management System (DBS), and Smart City application.

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