Integration of GIS and Orthophoto to Enhance Road-Network Screening – A 3GR Approach



2 Dataset
Three different sources of data were used in this study: (1) digital orthophoto images for target area, (2) roads database records (e.g. collisions records, traffic volume, etc.) and (3) single line road network (SLRN) in GIS, ArcView, format. The data and images used in this study were obtained from the Regional Municipality of Durham.

3 Methodology

3.1 Challenge
The main challenge in this research is to recognize and extract the streetlight poles, which are narrow, vertical objects that have very limited width in orthophoto images. The poles appear as only a few pixels in the orthophoto. They are very difficult to locate or recognize using direct extracting methods. An indirect method is developed to extract the streetlight poles. The method is based on a unique idea that uses the image’s filters in an unusual way. As streetlight poles cannot be easily recognized on the image, a semi-automatic method has been developed to help users to recognize the streetlight poles types and location. The method is based on observing the streetlight poles and their shadow as the shadow makes the streetlight pole easier to recognize. To make the streetlight poles’ shadow more clear, filters were used.

The technique is designed to be user-friendly for road agencies and safety analysts and achieves accurate results without the need for a strong background in photogrammetry and orthophoto images.

3.2 Semi-Automatic Methodology
The semi-automatic method consists of two main steps: (1) extracting streetlight pole locations (2) identifying the illuminated rural highway road segments and update the GIS database.

3.2 .1 Identifying Pole’s Types and Locations
The Semi-automatic method for extracting streetlight poles locations and types can be summarized as follows (Fig.1):
  • The orthophotos are linked with SLRN by using ArcView.
  • Template windows for streetlight poles are chosen as signatures. These templates are chosen from the segments where no conflicts are recorded.
  • To enhance the shadow of the streetlight poles, a custom filter called the “Minimum Filter” is applied. The “Minimum Filter” changes the brightness value of the pixels. In this study, the brightness value of each pixel is changed in the image according to a predefined mathematical operation. Each pixel is reassigned a value based on the values of the surrounding pixels. The “Minimum Filter” assesses individual pixels in a selection. Within a specified radius, the “Minimum Filter” replaces the current pixel's brightness value with the least brightness value of the surrounding pixels. The “Minimum Filter” has the effect of spreading out black areas and shrinking white areas.
  • To enhance the objects in the image, the “Find Edges” filter is applied. The “Find Edge” filter is not applied to find the object edge as normal, but to make the object image more recognizable visually and mathematically when cross correlation is applied.
  • The cross correlation technique is applied to identify streetlight pole types and locations. Template windows for each pole type are chosen and used as a signature. To achieve accurate results, the template windows are chosen from the nearest road segments having similar bearing. The area chosen to extract the streetlight poles is limited to a specific distance from the street centerline. In this study, this distance equals the road width plus the road’s 17 meters buffer. Any results obtained form beyond the buffer are discarded. The width of the buffer can be changed depending on the angle of the sun and the length of the pole’s shadow. The buffer is recommended to minimize computation time and to improve the results of the cross correlation operation.
  • To improve the result of the cross correlation operation, the cross correlation parameters are pre-determined as follows: First, the clearest target is chosen from the nearest recognised road segment; this target is used as a template. Second, the cross correlation is applied for this segment. The results are evaluated, and the best minimum value of the cross correlation parameter is chosen. This is called supervised selection.
  • The results are merged with the GIS system.
3.2.2 Identifying Road Segment Types
In this study, the data were classified into three different types: digital orthophotos, road network database records and SLNR. Each one had its own format and structure. Microsoft Access was used to link the different database files. The ArcView GIS system was used to link the digital images, network screening database, collision database, and SLRN (Fig.2).

The linking procedure was used to identify which road segments are illuminated (Type 1) and un-illuminated (Type 2). Three tasks are required to make this distinction:
  • In the first task, the collisions database, accident locations records, and SLRN are linked together using ArcView. Collisions are classified into two main categories: (1) nighttimes collisions in low visibility on roads without illumination, and (2) nighttimes collisions on the roads with illumination. From this classification, “Type 1” and “Type 2” are identified.
  • In the second task, road database records other than the collision database and SLRN are linked together. From this new task, the road-segments (Type1/Type2) are also identified.
  • The results from the first and second tasks are compared. If there are no conflicts, the results are stored. Parts of these results are used to choose the best comparison template windows for the cross correlation operation. If conflicts are found, the segments are marked for further investigation and checking. The conflicts can be classified into the following groups: (1) the illumination records are missing from the database, (2) the illumination data recodes as unknown, and (3) there are different records for the same segment (e.g. the segment is recorded as un-illuminated and as unknown or illuminated in other database).
  • If conflicts are found, the segments are marked for further investigation and checking. The conflicts can be classified into the following groups: (1) the illumination records are missing from the database, (2) the illumination data recodes as unknown, and (3) there are different records for the same segment (e.g. the segment is recorded as un-illuminated and as unknown or illuminated in other database).
  • The semi-automatic method, which described in section 3.2.1, is used to identify the streetlight poles and to verify the road segments. To achieve accurate results, comparison windows are chosen from the nearest road segments having a similar bearing. The semi-automatic method is used to clarify the conflicts in the existing database; this can be used to create GIS data for street poles as well.


Fig. 1. General procedure for identifying pole type and location
Fig. 2 Scheme for the proposed methodology to enhance the illumination database and extract the poles location

4 Validation of The Methodology
To evaluate the semi- automatic technique performance, the technique was applied to the route, which consists of 62 different road segments with a total length of 39,886 meters. The site images were explored by using the semi-automatic technique.

The results were recorded in the GIS system. The results were checked on a site trip for the target route, which found that:
  • Fifty-seven out of sixty two segments (91 %) had matching results.
  • Five segments cannot be identified clearly. Three of the five segments were located in the core of downtown of the target city (Uxbridge city, Greater Toronto Area, Canada) where the view of the street poles was obstructed by the shadows of high buildings. The remaining two segments had illuminated and un-illuminated components. These segments should be subdivided to match the difference in illumination. Road agencies should note changes in road illumination as well carefully as they note physical changes in the road design
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