**3. Data and methods**

The study area for this chapter is Ladyville Village, Belize, Central America. Ladyville was once a small coastal settlement separated from other communities, but over the years it has seen an increase in development and in population. Development has caused the village to become a sizable town and is sometimes considered a suburb of Belize City. Belize City, the largest city in Belize is only a few minutes' drive away from Ladyville with the Belize River separating the two settlements. Ladyville is north of Belize City, along the Belize River, along the coast, and along the Philip Goldson Highway and it is in the lower reach of the Belize River watershed. The study area map is provided in **Figure 1**.

**167**

**Figure 2.**

*OBIA building extraction workflow diagram.*

*High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos*

The topography of Ladyville is mostly flat. It is part of Belize's coastal lowland and it is a part of the Belize River natural floodplain. Its natural vegetation is mostly broadleaf lowland forests and marshlands with meandering creeks, lagoons, and mangrove forest along the coast. The Ladyville area was also a location where excavations were done to gather fill for sites in Belize City. Ladyville was chosen as a study area because it is one of the most vulnerable communities to natural disasters and because of its strategic importance. Ladyville is located between the Belize River and the Caribbean Sea. This means it is highly vulnerable from both river flooding from the Belize River and storm surge flooding from the Caribbean Sea

The datasets used in this study are LiDAR and aerial photos. LiDAR point cloud

To complete the semi-automated building extraction process, the workflow was developed. The workflow shown in **Figure 2** below includes the following steps: *LiDAR Pre-processing*, *PCA, OBIA, Segmentation, Feature Extraction, Training Sites, Image Classification, Rule base Classification, Accuracy Assessment and Regularize* 

LiDAR pre-processing involves the filtering of ground points from non-ground

points. As a result, two files with the digital elevation model (DEM) ground points only and the digital surface model (DSM) non-grounds points were created with a spatial resolution of 1 m. From the DEM and DSM, an nDSM was created by subtracting the DEM from the DSM. The normalized digital surface model (nDSM) represents the absolute height of objects in the study area such as buildings and trees. Then LiDAR nDSM as a separate band was combined with four aerial photos bands. LiDAR height information from the nDSM was added to the aerial photos which is an essential building characteristic for extraction from

data was provided in LAS format version 1.4. LAS is a standard data exchange format for LiDAR point cloud data established by (ASPRS) the American Society for Photogrammetry and Remote Sensing. The data has a point average spacing of 0.3 m and it was classified into ground, low vegetation, medium vegetation, high vegetation, buildings and noise. Aerial photos were taken within the same time period of LiDAR airborne surveys in August of 2017. The image has a high spatial resolution of 0.1 m (10 cm). Aerial photos have four bands, Red, Green, Blue and

*DOI: http://dx.doi.org/10.5772/intechopen.92640*

from hurricanes or oceanic events.

**3.1 LiDAR nDSM pre-processing**

NIR (Near Infrared).

*Building Outline.*

other features.

**Figure 1.** *Study area location map.*

### *High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos DOI: http://dx.doi.org/10.5772/intechopen.92640*

The topography of Ladyville is mostly flat. It is part of Belize's coastal lowland and it is a part of the Belize River natural floodplain. Its natural vegetation is mostly broadleaf lowland forests and marshlands with meandering creeks, lagoons, and mangrove forest along the coast. The Ladyville area was also a location where excavations were done to gather fill for sites in Belize City. Ladyville was chosen as a study area because it is one of the most vulnerable communities to natural disasters and because of its strategic importance. Ladyville is located between the Belize River and the Caribbean Sea. This means it is highly vulnerable from both river flooding from the Belize River and storm surge flooding from the Caribbean Sea from hurricanes or oceanic events.

The datasets used in this study are LiDAR and aerial photos. LiDAR point cloud data was provided in LAS format version 1.4. LAS is a standard data exchange format for LiDAR point cloud data established by (ASPRS) the American Society for Photogrammetry and Remote Sensing. The data has a point average spacing of 0.3 m and it was classified into ground, low vegetation, medium vegetation, high vegetation, buildings and noise. Aerial photos were taken within the same time period of LiDAR airborne surveys in August of 2017. The image has a high spatial resolution of 0.1 m (10 cm). Aerial photos have four bands, Red, Green, Blue and NIR (Near Infrared).

To complete the semi-automated building extraction process, the workflow was developed. The workflow shown in **Figure 2** below includes the following steps: *LiDAR Pre-processing*, *PCA, OBIA, Segmentation, Feature Extraction, Training Sites, Image Classification, Rule base Classification, Accuracy Assessment and Regularize Building Outline.*

#### **3.1 LiDAR nDSM pre-processing**

*Spatial Variability in Environmental Science - Patterns, Processes, and Analyses*

technical details have thus far been published [14].

watershed. The study area map is provided in **Figure 1**.

applications [6].

**3. Data and methods**

approaches [13]. Although promising results have been obtained from these 2D information-based methods, shadows and occlusions leading to significant errors, especially in densely developed areas, cannot be avoided. Consequently, these methods are considered to be insufficiently automated and reliable for practical

The third category of building extraction is using a combination of LiDAR data with multispectral images in an image fusion technique. This third approach exploits the mutual benefits of both datasets for accurate building extraction. By fusing 2D images and 3D information from LiDAR, complementary information can be exploited to improve automatic building extraction processing and the accuracy of the building roof outline [1]. This method has been widely studied in [14–16]; Building detection techniques integrating LIDAR data and imagery can be divided into two groups. Firstly, there are techniques that use the LIDAR data as the primary cue for building detection and those which use both the LIDAR data and the imagery as the primary cues to delineate building outlines [15]. In this approach LiDAR height and intensity are usually used along with aerial imagery to improve the classification of buildings. However, the challenges are how to integrate the two data sources for building boundary extraction still arises; few approaches with

The study area for this chapter is Ladyville Village, Belize, Central America. Ladyville was once a small coastal settlement separated from other communities, but over the years it has seen an increase in development and in population. Development has caused the village to become a sizable town and is sometimes considered a suburb of Belize City. Belize City, the largest city in Belize is only a few minutes' drive away from Ladyville with the Belize River separating the two settlements. Ladyville is north of Belize City, along the Belize River, along the coast, and along the Philip Goldson Highway and it is in the lower reach of the Belize River

**166**

**Figure 1.**

*Study area location map.*

LiDAR pre-processing involves the filtering of ground points from non-ground points. As a result, two files with the digital elevation model (DEM) ground points only and the digital surface model (DSM) non-grounds points were created with a spatial resolution of 1 m. From the DEM and DSM, an nDSM was created by subtracting the DEM from the DSM. The normalized digital surface model (nDSM) represents the absolute height of objects in the study area such as buildings and trees. Then LiDAR nDSM as a separate band was combined with four aerial photos bands. LiDAR height information from the nDSM was added to the aerial photos which is an essential building characteristic for extraction from other features.

**Figure 2.**

*OBIA building extraction workflow diagram.*
