**2.1 Data collection and preprocessing**

*Geographic Information Systems and Science*

with unmanned aerial vehicles (UAVs) and mobile devices are selected as major data sources for actual site monitoring and modeling. As-planned models and project schedules provide main data sources to build a virtual construction process. In addition, the 3D environment construction environment is reproduced in the visualization system. Subsequent to data acquisition, images and models need to be processed such that they are compatible with KML. Models are divided into parts in order to denote distinct construction stages in line with construction schedule. Photogrammetry algorithms are also used in order to align unordered images within the WGS84 coordinate system adopted in Google Earth. Panoramic views and 3D reconstruction of the construction site are produced to facilitate a better comprehension of the construction environment. The resulting 3D point cloud captures the geometry of the construction site and is thereby used for cut/fill volume takeoff, as well as measuring the hauling distance between two areas. As-planned models are converted in the KML format and time-stamped in order to visualize the construction progress. The system provides stakeholders with a visually intuitive platform to perceive the construction site and identify potential problems such as spatial limits in connection with site accesses and site layouts through integrated information visualization. By storing data on the cloud, KML enables efficient

**136**

**Figure 1.**

*GoogleEarthWork for earthwork construction planning.*

Google Earth provides project managers with free high-resolution satellite images and topographic information of the environment around a construction site. Such information is essential to plan for site accesses, site layouts, and traffic flows. As-planned information in 2D/3D drawings is crucial for scope definition, quantity takeoff, and progress monitoring. For earthwork projects specifically, the as-designed surface is required to take off cut/fill volumes. Besides, the structures being built also affect site accessibility and traffic flows.

For as-built information, site photos have been widely used on a construction site for updating construction progress and reporting safety issues or other problems. However, images collected by different personnel are barely reused due to lack of efficient image management tools. It is desirable to automatically organize images with locations in a GIS system, but the positioning accuracy of mobile devices is inadequate for two main reasons, namely, (1) low-end localization sensors embedded in mobile devices and (2) multipath effect of radio frequency signals. In general, the camera pose obtained from a consumer-grade mobile device does not satisfy the need for geo-referencing and AR applications. Higher positioning accuracy can be obtained from aerial images taken by UAV due to high-grade localization sensors embedded and lessened multipath effects. After bundle adjustment [51], the camera pose can be further improved. By taking the optimized geo-location of aerial images as references, ground imageries can also be precisely aligned in the physical coordinate system. In addition, 3D reconstruction from images is instrumental in quantifying cut/fill volumes of earthmoving jobs and fixing distances and slopes of haul roads in earthwork construction planning. Most recent research endeavors [52] have demonstrated the cost-effectiveness of UAV photogrammetry for earthwork volume estimation.

Structure from motion (SfM) [53] has been well studied in photogrammetry and computer vision domains to reconstruct the 3D structure of the scene from image collections and to recover the pose of these images. Taking unordered images as inputs, SfM outputs the precise image position and orientation, plus 3D reconstruction of the site as point cloud or model. Besides, high-resolution panoramas stitched from aerial photos are cost-effective substitutes for outdated low-resolution satellite images. As an incremental approach, SfM is suitable for processing construction site photos collected on an irregular basis along the time line. However, it requires redundant images in order to ensure "realism" of the scene. This is usually not assured when ground photos are taken by different personnel on a construction site. Therefore aerial images taken by UAV are used to materialize connecting and aligning scattered ground images. With a sequence of imageries taken on the construction site, the system implements the SfM procedure, starting from the first aerial imagery and taking it as the reference in subsequent processing of images taken by cell phones on the ground.

The direct output of SfM includes the camera pose and a 3D point cloud of the object. A much denser 3D reconstruction of the object can be achieved using stereo matching subject to coplanar constraints [54]. To visualize the 3D reconstruction in *GoogleEarthWork*, a mesh model of the object is also produced. Further, panoramic images are generated by projecting original aerial photos onto the mesh model. An example is given in the subsequent section.

#### **2.2 Information integration with KML**

Based on XML, KML uses a tag-based structure with nested elements to manage data and information associated with an object in a hierarchical manner. Different from CityGML which is designed to represent geometric objects, the strength of KML lies in visualization on a web-based GIS platform. It defines basic elements to represent geometric objects, raster images, as well as their visual effects. Elements predefined in KML are divided into several categories according to their functionality: *Feature* for vector and raster geo-data, *Geometry* for 3D objects, *AbstractView* for navigation, *TimePrimitive* for date and time, and others for visualization style, LOD, and so on. As for GIS, geo-referencing elements are the most important for defining one object. Each object needs to be geo-referenced by <*Location* > and < *Orientation* > elements. A < *Scaling* > element is also available if scaling is necessary. The detailed information can be found through the KML reference; those elements intensively used in this research are listed in **Table 3**.

Objects defined with elements in the *Feature* category are listed on the navigation panel of the Google Earth interface for interactive selection. These elements include <*GroundOverlay*> and <*PhotoOverlay*> for images, as well as <*Placemark*>, <*NetworkLink*> for geometries and models. <*GroundOverlay*> elements are used to align satellite images or panoramic images over the 3D terrain model. <*PhotoOverlay*> elements are capable to align normal images with the 3D environment for AR visualization. A 3D model can be placed under <*Placemark*> or <*NetworkLink*> elements. Geometric objects can be represented either with primary basic shapes predefined in KML or hyperlinks of models in KML files or XML-based COLLADA files [55]. <*Folder*> and <*Document*> are elements that can be used repetitively to efficiently organize hierarchical contents.

Aerial images (which are taken by UAV) provide a unique view angle of the construction site with fewer obstacles. Besides, these images can be taken on a periodical basis to capture updates and progress on site. The stitched panoramic image has much higher resolution than satellite images available in Google Earth. The <*GroundOverlay*> element can be applied to replace the outdated lower resolution satellite image with high-resolution mosaics made of most recent images. To support real-time visualization, large panoramic images are preprocessed and managed with special elements designated for visualization in different levels of detail.

On a construction site, ground imageries are usually taken at "random" locations and angles. Consequently, they are fragmented in nature and only used as evidence shown in documents in practice. However, by aligning the image at the


**139**

**Figure 2.**

*Integrated project data management and visualization.*

*Google Earth Augmented for Earthwork Construction Planning*

exact location and orientation in relation to 3D models and the site environment, fragmented information provided by individual images can be well organized and seamlessly integrated. Different from real-time AR technologies which demand considerable computing resources and remain too expensive to implement on site, the <*PhotoOverlay*> element in KML affords a pragmatic approach for realizing costeffective AR experiences and efficient site photo management. Each <*PhotoOverlay*> object is defined by (1) a <*Camera*> element specifying the position and orientation of the image, (2) a <*ViewVolume*> specifying the field of view (FOV) of the image, (3) a <*Icon*> element to store the link to the image, and (4) an optional <*TimeStamp*> element stating the date when the image is captured. Given the rotation angles of the camera (omega, phi, and kappa) obtained from photogrammetry software, the heading, tilt, and roll can be derived with equations presented in [1]. The view volume of the image can also be derived from the estimated focal length and the image size. The image capturing date and time can be readily extracted from the header of the image file; thereby, a time stamp can be added to each image to show actual progress. This also enables retrieval and viewing of images only relevant to a particular time frame. An example of information integration in *GoogleEarthWork* through using KML is presented in **Figure 2**; ground images captured with cell phones and digital cameras, aerial images collected using UAVs, and 3D models are embedded in the Google Earth platform so that the surrounding environment of the construction site

*DOI: http://dx.doi.org/10.5772/intechopen.82008*

is also rendered in a cost-effective fashion.

**2.3 Constraints identification in earthwork planning**

Analytical simulation or optimization for construction operations planning requires knowledge of practical constraints on the construction site so as to make a sufficient problem definition. In rough grading, a certain volume of earth needs to be excavated at one area and filled at another. Accessibility issues during project execution become the primary concern for earthwork construction

#### **Table 3.**

*Intensively used KML elements in GoogleEarthWork.*

### *Google Earth Augmented for Earthwork Construction Planning DOI: http://dx.doi.org/10.5772/intechopen.82008*

*Geographic Information Systems and Science*

**2.2 Information integration with KML**

Based on XML, KML uses a tag-based structure with nested elements to man-

age data and information associated with an object in a hierarchical manner. Different from CityGML which is designed to represent geometric objects, the strength of KML lies in visualization on a web-based GIS platform. It defines basic elements to represent geometric objects, raster images, as well as their visual effects. Elements predefined in KML are divided into several categories according to their functionality: *Feature* for vector and raster geo-data, *Geometry* for 3D objects, *AbstractView* for navigation, *TimePrimitive* for date and time, and others for visualization style, LOD, and so on. As for GIS, geo-referencing elements are the most important for defining one object. Each object needs to be geo-referenced by <*Location* > and < *Orientation* > elements. A < *Scaling* > element is also available if scaling is necessary. The detailed information can be found through the KML refer-

ence; those elements intensively used in this research are listed in **Table 3**.

repetitively to efficiently organize hierarchical contents.

Objects defined with elements in the *Feature* category are listed on the navigation panel of the Google Earth interface for interactive selection. These elements include <*GroundOverlay*> and <*PhotoOverlay*> for images, as well as <*Placemark*>, <*NetworkLink*> for geometries and models. <*GroundOverlay*> elements are used to align satellite images or panoramic images over the 3D terrain model. <*PhotoOverlay*> elements are capable to align normal images with the 3D environment for AR visualization. A 3D model can be placed under <*Placemark*> or <*NetworkLink*> elements. Geometric objects can be represented either with primary basic shapes predefined in KML or hyperlinks of models in KML files or XML-based COLLADA files [55]. <*Folder*> and <*Document*> are elements that can be used

Aerial images (which are taken by UAV) provide a unique view angle of the construction site with fewer obstacles. Besides, these images can be taken on a periodical basis to capture updates and progress on site. The stitched panoramic image has much higher resolution than satellite images available in Google Earth. The <*GroundOverlay*> element can be applied to replace the outdated lower resolution satellite image with high-resolution mosaics made of most recent images. To support real-time visualization, large panoramic images are preprocessed and managed with special elements designated for visualization in different levels of detail. On a construction site, ground imageries are usually taken at "random" locations and angles. Consequently, they are fragmented in nature and only used as evidence shown in documents in practice. However, by aligning the image at the

**Element Function Objects** <Model> 3D model representation and visualization 3D models

Associate date/time for 4D exploration of objects

<ExtendedData> Customized data organization and visualization Documents, webs, et al.

Panoramic mosaics

Original images

Image pose

Schedule

<GroundOverlay> Raster data alignment and overlay on Google Earth terrain

<Camera> Camera position and orientation for AR and navigation

and activities

*Intensively used KML elements in GoogleEarthWork.*

<PhotoOverlay> Image placement and orientation for AR visualization

**138**

**Table 3.**

<TimeStamp> <TimeSpan>

exact location and orientation in relation to 3D models and the site environment, fragmented information provided by individual images can be well organized and seamlessly integrated. Different from real-time AR technologies which demand considerable computing resources and remain too expensive to implement on site, the <*PhotoOverlay*> element in KML affords a pragmatic approach for realizing costeffective AR experiences and efficient site photo management. Each <*PhotoOverlay*> object is defined by (1) a <*Camera*> element specifying the position and orientation of the image, (2) a <*ViewVolume*> specifying the field of view (FOV) of the image, (3) a <*Icon*> element to store the link to the image, and (4) an optional <*TimeStamp*> element stating the date when the image is captured. Given the rotation angles of the camera (omega, phi, and kappa) obtained from photogrammetry software, the heading, tilt, and roll can be derived with equations presented in [1]. The view volume of the image can also be derived from the estimated focal length and the image size. The image capturing date and time can be readily extracted from the header of the image file; thereby, a time stamp can be added to each image to show actual progress. This also enables retrieval and viewing of images only relevant to a particular time frame.

An example of information integration in *GoogleEarthWork* through using KML is presented in **Figure 2**; ground images captured with cell phones and digital cameras, aerial images collected using UAVs, and 3D models are embedded in the Google Earth platform so that the surrounding environment of the construction site is also rendered in a cost-effective fashion.
