*3.2. Animal Material*

The samples of an *Inflatella* sp. sponge were collected by dredging near Kashevarov Bank, the Sea of Okhotsk (55◦30 N; 146◦05 E, Russia) at a depth of 214–197 m and were freeze dried after collection. The species was identified by Dr. Krasokhin V. B. from G.B. Elyakov Pacific Institute of Bioorganic

Chemistry, FEB RAS. A voucher specimen (PIBOC O07-33(11)) is deposited at the G.B. Elyakov Pacific Institute of Bioorganic Chemistry, FEB RAS (Vladivostok, Russia).


**Abstract**

This chapter introduces *GoogleEarthWork* which is an augmented geographic information system (GIS) based on Google Earth to manage and visualize heterogeneous site information, especially 3D models, aerial and ground images, panoramas, and GIS data of the site environment. The concept is to realize a highly automated end-to-end earthwork construction planning system that is able to generate project management deliverables from heterogeneous information and enhance the usefulness and intelligence of GIS for better project planning and control in earthwork construction. With identified constraints from the augmented Google Earth, the earthwork planning problem is formulated, and an optimized executable plan can be automatically generated, including work breakdown structure and project network model. Demonstration cases are provided to prove concepts of and illustrate functionalities of *GoogleEarthWork* in support of earthwork construction planning in realistic settings.

**Keywords:** Google Earth, Keyhole Markup Language, earthwork construction, automated planning

#### **1. Introduction**

Construction project planning and control requires an integral and comprehensive understanding of the construction site. During the planning process, a large volume of data are collected and created to identify potential problems on the construction site and select proper construction methods and procedures in order to ensure safety and on-time delivery of the project. Such data include (1) as-planned information that describes the design and the scope of the project, (2) as-built information that describes the actual situation on the construction site, and (3) environmental information that can be used to evaluate the impact of the environment on the project and the impact of the project on the environment. At present, engineers and project managers can be overwhelmed with various information coming from different sources (as listed in **Table 1**); however, maintaining large-volume heterogeneous datasets would become a big burden unless they can be linked and managed together to enable efficient information retrieval and facilitate problem identification [1].

The adoption of advanced sensing and information management technologies in construction is greatly hindered by (1) high expenses on system development yet unclear benefits of implementation [20–22], (2) inefficient visualization and oversimplified site modeling methods for coping with complicated site environment [20], (3) insufficient integration and interoperability [23, 24], and (4) technology barriers and organizational difficulties in information sharing and distribution [20, 25].

#### **Table 1.**

*Typical datasets available on a construction project.*

Several technologies have been applied on project information management and visualization, including building information modeling (BIM) [2], augmented reality (AR) [26–28], the integration of BIM and AR, the integration of GIS and BIM, and Google Earth, as listed in **Table 2**.

BIM demonstrates great potential to model rich geometric and semantic information of a building object but lacks the capability to incorporate as-built and environmental information. AR has gained substantial attention lately due to its capability to combine site photos and as-planned 3D models. However, the absence of an accurate model of the surrounding environment, for example, those 3D site models generally provided by 3D GIS systems, makes AR less instrumental in construction engineering applications that demand the representation of frequent, intensive interactions and relationships between the facilities being built and the site environment, especially where the project is situated in crowded cities or environmentally fragile areas. Researchers have also leveraged on the benefits of integrating BIM and AR [29–32]. Nonetheless, incorporating AR into BIM software is still practically infeasible due to inherent limitations of BIM software in handling large external datasets for real-time rendering [31].

GIS has achieved significant success in managing large-scale heterogeneous spatial information. Considerable attention has been placed on the integration of BIM models and GIS so as to integrate the indoor as-built information and the outdoor environmental information [33, 34]. To tackle unstructured data, researchers utilized variants of Extensible Markup Language (XML) to develop shared project information models thanks to its extensibility and interoperability on the web schemas [35–37]. Both the open source BIM standard of industrial foundation class

**135**

*Google Earth Augmented for Earthwork Construction Planning*

semantic mapping [42, 47–49] and ontology [42, 48].

photos, 3D models, and the building environment.

to the generation of an optimized earthwork execution plan.

**2. GIS-based site information management and visualization**

Google Earth has been widely used by scientists and relevant stakeholders in addressing environmental and construction planning issues thanks to its ubiquity and rich geographic information. Diversified geographical information is presented to the user through a combination of digital elevation models, satellite imagery, 3D building models, street views, and user-uploaded images. Features such as tiling and level of detail (LOD) for images and 3D models enable Google Earth to manage large datasets with ease and efficiency, eclipsing majority of BIM software. Besides, KML enriches the extensibility of Google Earth significantly by providing users a standardized language to add data and customize analyses. With temporal and spatial information associated with each object, Google Earth enables efficient information retrieval through content navigation, 3D exploration, and time window filtering. The *GoogleEarthWork*—which is prototyped based on Google Earth using KML—seamlessly integrates information contained in unordered images, geometric models, and 3D GIS system. As presented in **Figure 1**, the system encompasses data collection, data processing, data management, and information visualization and distribution. Aerial and ground imageries of the construction site captured

(IFC) [38–40] and Web GIS formats (including LandXML [39, 41], City Geography Markup Language (CityGML) [42–45], and *Keyhole Markup Language* (KML) [1]) are based on XML. LandXML is mainly intended for enhancing interoperability of data utilization in the land development industry. The integration between IFC and CityGML is the most investigated approach for integrated information modeling of buildings [43]. Majority of the works have attempted to covert semantically rich IFC models to CityGML models by taking advantage of the capability of GIS to handle huge datasets with a server-based approach [34, 43]. Earlier works [43, 44, 46] in this area focused on the conversion of geometric models. Ensuing research endeavors were intended to improve the conversion of semantic information [45, 47] using

The integration of BIM and CityGML provides an effective means to manage indoor building information and outdoor environmental information. However, it lacks the functionality to support AR modeling based on site photos or videos. In contrast, KML—which represents a markup language specialized for data modeling in Google Earth—focuses on data integration and visualization. It provides various data models to support advanced visualization techniques including AR. In [50], KML was used to visualize building energy simulation results integrated with BIM. Another related endeavor [1] proposed the use of KML and Google Earth to generate a cost-effective site information management platform which integrated site

In this chapter, we introduce an augmented GIS system called *GoogleEarthWork*—

which is conceptualized from an academia-industry joint research endeavor and prototyped by taking advantage of KML and Google Earth for managing and visualizing heterogeneous site information in support of proactive project planning and control in the particular application context of rough grading earthwork construction. *GoogleEarthWork* focuses on the integration of 3D models, aerial and ground images, panoramas, and GIS data of the site environment that are commonly used for earthwork construction planning. Such datasets are seamlessly synthesized to facilitate the identification of quantitative and qualitative constraints in earthwork construction planning through applying computer vision techniques. Further, *GoogleEarthWork* runs on an automated earthwork planner engine program, leading

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

**Table 2.**

*Site information management methods.*

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

*Geographic Information Systems and Science*

**Data Usage** 2D drawings (as-designed) Design

and Google Earth, as listed in **Table 2**.

Satellite images, topographic data, et al. in GIS

*Typical datasets available on a construction project.*

(environmental)

**Table 1.**

**134**

**Table 2.**

*Site information management methods.*

**Technology Information** BIM 3D models

large external datasets for real-time rendering [31].

AR 3D models + images BIM + AR 3D models + images

GIS + BIM 3D models + satellite images + topographic

Google Earth 3D models + images + satellite images + topographic

Several technologies have been applied on project information management and visualization, including building information modeling (BIM) [2], augmented reality (AR) [26–28], the integration of BIM and AR, the integration of GIS and BIM,

3D models (as-designed) Design/construction prototyping [2, 3]

Images/videos (as-built) Site inspection and reporting

Laser scanning (as-built) As-built modeling [9–11]

Site layout planning [4] Crane path and lift planning [5]

As-built modeling [6, 7] Progress monitoring [8]

Progress monitoring [8]

Site layout planning [12–17] Route planning [15]

Data management and visualization [1, 18, 19]

BIM demonstrates great potential to model rich geometric and semantic information of a building object but lacks the capability to incorporate as-built and environmental information. AR has gained substantial attention lately due to its capability to combine site photos and as-planned 3D models. However, the absence of an accurate model of the surrounding environment, for example, those 3D site models generally provided by 3D GIS systems, makes AR less instrumental in construction engineering applications that demand the representation of frequent, intensive interactions and relationships between the facilities being built and the site environment, especially where the project is situated in crowded cities or environmentally fragile areas. Researchers have also leveraged on the benefits of integrating BIM and AR [29–32]. Nonetheless, incorporating AR into BIM software is still practically infeasible due to inherent limitations of BIM software in handling

GIS has achieved significant success in managing large-scale heterogeneous spatial information. Considerable attention has been placed on the integration of BIM models and GIS so as to integrate the indoor as-built information and the outdoor environmental information [33, 34]. To tackle unstructured data, researchers utilized variants of Extensible Markup Language (XML) to develop shared project information models thanks to its extensibility and interoperability on the web schemas [35–37]. Both the open source BIM standard of industrial foundation class (IFC) [38–40] and Web GIS formats (including LandXML [39, 41], City Geography Markup Language (CityGML) [42–45], and *Keyhole Markup Language* (KML) [1]) are based on XML. LandXML is mainly intended for enhancing interoperability of data utilization in the land development industry. The integration between IFC and CityGML is the most investigated approach for integrated information modeling of buildings [43]. Majority of the works have attempted to covert semantically rich IFC models to CityGML models by taking advantage of the capability of GIS to handle huge datasets with a server-based approach [34, 43]. Earlier works [43, 44, 46] in this area focused on the conversion of geometric models. Ensuing research endeavors were intended to improve the conversion of semantic information [45, 47] using semantic mapping [42, 47–49] and ontology [42, 48].

The integration of BIM and CityGML provides an effective means to manage indoor building information and outdoor environmental information. However, it lacks the functionality to support AR modeling based on site photos or videos. In contrast, KML—which represents a markup language specialized for data modeling in Google Earth—focuses on data integration and visualization. It provides various data models to support advanced visualization techniques including AR. In [50], KML was used to visualize building energy simulation results integrated with BIM. Another related endeavor [1] proposed the use of KML and Google Earth to generate a cost-effective site information management platform which integrated site photos, 3D models, and the building environment.

In this chapter, we introduce an augmented GIS system called *GoogleEarthWork* which is conceptualized from an academia-industry joint research endeavor and prototyped by taking advantage of KML and Google Earth for managing and visualizing heterogeneous site information in support of proactive project planning and control in the particular application context of rough grading earthwork construction. *GoogleEarthWork* focuses on the integration of 3D models, aerial and ground images, panoramas, and GIS data of the site environment that are commonly used for earthwork construction planning. Such datasets are seamlessly synthesized to facilitate the identification of quantitative and qualitative constraints in earthwork construction planning through applying computer vision techniques. Further, *GoogleEarthWork* runs on an automated earthwork planner engine program, leading to the generation of an optimized earthwork execution plan.


**6. Nanopesticides interaction with other environmental contaminants**

The evaluation of pesticides is done on a product-by-product basis, in a similar fashion to other contaminants. In the real environment, mixed interactions are possible as they co-occur with other substances. A nanopesticide may have collaborative interaction with other contaminants through the Trojan-horse effect, so hazard analysis of environmental mixtures is generally not needed for product hazard estimation [24]. In this effect the substance are carried to a tissue of organism or to an organism by interaction of substance with ENP, due to which the interior exposure to contaminants is enhanced, and might not get accumulated. The need of hour is to integrate these interrelations into authoritative hazard analyzing schemes.


5.3 Some relationships between htopð Þ F and hp,Pð Þ F

Theorem 5.2.  Then we have

$$h\_{p\_\bullet, \mathcal{P}}(\mathbf{F}) \le h\_{\text{top}}(\mathbf{F}) \le h\_{b\_\bullet, \mathcal{P}}(\mathbf{F}) + h\_{p\_\bullet, \mathcal{P}}(\mathbf{F}).$$

Proof. Note that s max ,Pn ð Þ n, ϵ ≤s n, ð Þ ϵ, On for any cover P of X and any ϵ>0. Then the former inequality is obtained. Now we show the later one. If hb,Pð Þ¼ F ∞, then there is nothing to prove. Now assuming hb,Pð Þ F < ∞.

 For each Wnð Þ D ∈Y, let M Dð Þ be a ~ dn-ð Þ n, ϵ -separated subset of Wnð Þ D with cardinality s n, ð Þ ϵ, Wnð Þ D . Put M ¼ ∪Wnð Þ D ∈YM Dð Þ. We claim that M is a ~ dn-ð Þ n, 3ϵ -spanning subset of On.

In fact, for any x ¼ ð Þ x1, x2, ⋯, xn ∈ On, since Y is a dH-ð Þ n, ϵ -separated subset of On,Pn with maximum cardinality and Pn covers Xn, there is an E∈Pn with xn ∈ E and a Wnð Þ D ∈Y such that dHð Þ Wnð Þ D , Wnð Þ E ≤ϵ. Then it follows that there is a y ¼ y1, y2, ⋯, yn ∈ Wnð Þ D such that ~ dnð Þ x, y ≤ ϵ. Also note that M Dð Þ is a ~ dn-ð Þ n, ϵ separated subset of Wnð Þ D with maximum cardinality; there is a z∈ M Dð Þ such that ~ dnð Þ y, z ≤ϵ. Hence we have

$$
\tilde{d}\_n(\mathfrak{x}, z) \le \tilde{d}\_n(\mathfrak{x}, \mathfrak{y}) + \tilde{d}\_n(\mathfrak{y}, z) < 3\epsilon.
$$

This yields the claim that M is a ~ dn-ð Þ n, ϵ -spanning subset of On. So we have r n, ð Þ 3ϵ, On ≤ ∣M∣, where M denotes the cardinality of M. Using the claim we have

$$\begin{split} r(n, \mathfrak{z}, O\_{\mathfrak{n}}) \leq & |M| \leq |Y| \cdot \max\left\{ |M(D)| \colon W\_{\mathfrak{n}}(D) \in Y \right\} \\ \leq & s\left(n, \mathfrak{e}, O\_{\mathfrak{n}\_{\mathfrak{n}}, \mathcal{P}\_{\mathfrak{n}}}\right) \cdot s\_{\max, \mathcal{P}\_{\mathfrak{n}}}(n, \mathfrak{e}). \end{split}$$

Taking limits as the requirements of the related definitions of entropies establishes the desired inequality.  Write FX ¼ fi : fi : X ! X is continous, i ≥1 and PXð Þ¼ P δ ð Þ ð Þδ ,Pð Þδ , ⋯ .

Theorem 5.3.

$$h\_{\text{top}}(\mathbf{F}\_X) = h\_{\text{p, }\mathcal{P}\_{\mathbf{X}}(\delta)}(\mathbf{F}\_X) = \lim\_{\epsilon \to 0} \lim\_{\delta \to 0} \limsup\_{n \to \infty} \frac{1}{n} \log s\_{\text{max, } \mathcal{P}(\delta)}(n, \epsilon).$$

Proof. Note that lim n!∞ 1 n log ∣Pð Þδ ∣ ¼ 0. Then, by Theorem 5.2, we have the former equality. Now we show the later equality.

Clearly, s n, ð Þ ϵ, On ≥s max ,PX ð Þδ ð Þ n, ϵ for any δ> 0, so we have

$$\limsup\_{n \to \infty} \log s(n, \epsilon, O\_n) \ge \lim\_{\delta \to 0} \limsup\_{n \to \infty} \frac{1}{n} \log s\_{\max, \mathcal{P}\_\lambda(\delta)}(n, \epsilon).$$

This implies

$$h\_{\text{top}}(\mathbf{F}\_X) \ge \lim\_{\varepsilon \to 0} \lim\_{\delta \to 0} \limsup\_{n \to \infty} \frac{1}{n} \log s\_{\text{max}, \mathcal{P}\_\mathbf{X}(\delta)}(n, \varepsilon). \tag{22}$$

On the other hand, from the proof of Theorem 5.2, it follows that

$$r(n, \mathfrak{z}\epsilon, \mathcal{O}\_n) \le \mathfrak{s}\left(n, \mathfrak{e}, \mathcal{O}\_{n, \mathcal{P}(\delta)}\right) \cdot s\_{\max, \mathcal{P}\_\mathcal{X}(\delta)}(n, \mathfrak{e})$$

for any integer n ≥1, any sufficiently small ϵ> 0 and any δ>0. Noting that s n, ϵ, On,Pð Þδ ≤ ∣Pð Þδ ∣ for any integer n≥1, then we have

$$\lim\_{n \to \infty} \sup\_n \frac{1}{n} \log r(n, \mathfrak{A}; O\_n) \le \lim\_{\delta \to 0} \lim\_{n \to \infty} \sup\_n \frac{1}{n} \log \mathfrak{s}\_{\max, \mathcal{P}\_X(\delta)}(n, \mathfrak{e}).$$

This implies

$$h\_{\text{top}}(\mathbf{F}\_X) \le \lim\_{\epsilon \to 0} \lim\_{\delta \to 0} \limsup\_{n \to \infty} \frac{1}{n} \log \varepsilon\_{\text{max}, \mathcal{P}\_\mathbf{X}(\delta)}(n, \epsilon). \tag{23}$$

Thus, combining (22) and (23) gets the later equality. This completes the proof. □

Remark 5.4. The first equality of Theorem 5.3 is in fact a simpler version of Theorem 7.6 of [40] (a useful result for calculating the classical topological entropy) when restricting to the autonomous discrete dynamical systems.

Given a nonautonomous discrete dynamical system ð Þ X, ℱ , when does htopð Þ¼ F hp,Pð Þ F for any cover P of X? The following theorem gives an answer to this question.

Theorem 5.5. Let ð Þ X, F be a nonautonomous discrete dynamical system. Then htopð Þ¼ F hp,Pð Þ F for any cover P of X if the following conditions hold:

(1) For each integer i ≥1, there exists δi >0 such that diþ1 fi ð Þ x , fi ð Þy ≥dið Þ x, y whenever dið Þ x, y ≤δi for x, y∈Xi.

(2) For each integer i ≥1, every x∈Xiþ1 has an open neighborhood Ux whose preimage f �1 i ð Þ Ux is an union of disjoint open sets on each of which fi is a homeomorphism.

(3) lim supn!∞ 1 n log Nð Þ¼ ϵn, Xn 0 for every monotonic decreasing sequence f g ϵn with lim n!∞ϵ ¼ 0, where each Nð Þ ϵn, Xn denotes the minimal cardinality of the open cover of X consisting of open ϵn-ball for the compact metric space Xn.

Proof. It suffices to show that hb,Pð Þ¼ F 0 for any cover P of X by Theorem 5.2. Let Pmax ¼ Pi, max : i≥ 1 be the cover of X in which each Pi, max cover Xi consisting to singletons of Xi, i.e., Pi, max ¼ f g f gz : z∈Xi . It is easy to see that hb,Pð Þ F ≤hb,Pmax ð Þ F for any cover P of X. So from Theorem 5.2, it follows that what we want to prove is hb,Pmax ð Þ¼ F 0.

For each n ≥2, by condition (1), there exists a δn�1 > 0 such that

$$d\_n(f\_{n-1}(\mathfrak{x}), f\_{n-1}(\mathfrak{y})) \ge d\_{n-1}(\mathfrak{x}, \mathfrak{y}),$$

for any x, y∈Xn�1 whenever dn�1ð Þ x, y ≤δn�1. Also, by condition (2) and the compactness of Xn, there exists an ϵn >0 such that the ϵn-ball B xð Þ n, ϵn about any point xn ∈Xn has preimage f �1 n�1ð Þ B xð Þ n, ϵ equals the union of disjoint open sets of

## Partial Entropy and Bundle-Like Entropy for Topological Dynamical Systems DOI: http://dx.doi.org/10.5772/intechopen.89021

diameter less than δn�1. Then we get a sequence f g ϵn . Furthermore, we can take ϵn such that f g ϵn is monotonic decreasing sequence and lim n!∞ ϵn ¼ 0.

Now, given yn ∈Xn and x~ ¼ ð Þ x1, x2, ⋯, xn ∈ Wn B yn, ϵn, � � � � , we want to find a point y~ ¼ y1, y2, ⋯, yn � �∈ On with ~ dnð Þ¼ x, ~ y~ dn xn, yn � � and then ~ dnð Þ x, ~ y~ <ϵn. In fact, for 1<k<n, we can easily find a point yk, ⋯, yn � �∈ Qn i¼kXi with dj xj, yj � �≤ ϵj and djþ1 xjþ1, yjþ1 � �≥dj xj, yj � �, for j ¼ n � 1, n � 2, ⋯, k.  Since yk ∈B xð Þ k, ϵk , there is a unique point yk�1 ∈V∩f �1 k�1 yk � � such that dk�1 xk�1, yk�1 � �<δk�1. Then we have

$$d\_{k-1}(\chi\_{k-1}, \mathcal{y}\_{k-1}) \le d\_k(\chi\_k, \mathcal{y}\_k) \le d\_n(\chi\_n, \mathcal{y}\_n) < \epsilon\_n < \epsilon\_{k-1}.$$

This argument shows that r n, ϵn, On,comma max � �≤ Nð Þ ϵn, Xn . Thus, by condition (3), we get

$$\limsup\_{n \to \infty} \frac{1}{n} \log r(n, \epsilon\_n, O\_{n, \text{comm}} \max) \le \limsup\_{n \to \infty} \frac{1}{n} \log N(\epsilon\_n, X\_n) = 0.$$

For any sufficiently small ϵ >0, there exists N >0 such that ϵn <ϵ for any n≥ N. Then we have r n, ϵ, On,Pn, max � �≤r n, ϵn, On,Pn, max � � and hence hb,Pmax ð Þ¼ F 0. This completes the proof. □


**Simulation of Power Converters Using Matlab-Simulink**

Christophe Batard, Frédéric Poitiers, Christophe Millet and Nicolas Ginot

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/46419