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How to convert an image of a floor plan into data that I can work with?


This is more of a research question. I'm looking for aways of converting an image of a floor plant into data that I can work somehow. The end goal is to something similar to this: http://indoorosm.uni-hd.de/3d/.

I want something that allows me to store rooms and corridors informations, and then use it to trace routes between them.

It would be really helpful is someone can point me in the right direction, because the more I search the more different technologies I find, and I don't know which on would serve best my goals.


In JOSM you can use the PicLayer plugin an load your raster image in and digitise the data into vector indoorOSM format.

It will not be a 5 minute process you will have to plan a few hours into create the JOSM project and digitising will the time consuming part.

http://wiki.openstreetmap.org/wiki/IndoorOSM#PicLayer

JOSM is downloaded from https://josm.openstreetmap.de/


You can find a tutorial using qgis here: https://www.youtube.com/watch?v=DSU0kvXIaPQ The idea is to project the image of the floor plan on a map and then draw polygons on top of it.


I doubt there is a magic tool to turn floor plans into usable maps for routing.

Have a look at Seeking a GIS for indoor mapping and direction finding for pointers of how to create something that works.


Use Background Images in Your Views

Background images are images that you display underneath your data in order to add more context to the marks in the view. A common use of background images is adding custom map images that correspond to a coordinate system in your data.

For example, you might have data that corresponds to several floors in a building. You can use background images to overlay that data on the actual floor plan of the building to give more context. Other examples of using background images include showing a model of the sea floor, images of web pages for analyzing web logs, and even levels from video games to visualize player statistics.

While Tableau allows you to load dynamic maps from the online and offline provider, background images allow you to use your own custom images whether they are special maps or any other image that corresponds to your data.


The Challenge

Completing the project within 24 hours meant that there would be less time to fix any errors. This meant that Flatworld Solutions would have to be as accurate as possible with the first drafts. The website, FloorPlanner.com, is also not a common website or resource, which meant that the team at Flatworld had to familiarize themselves with the website and its functionality and train themselves on this uncommon tool.

There were also multiple sketches that were to be converted.


10 Ways to Alter Your Existing Floor Plan

Have you outgrown your family home? Do you need extra space for an office or an in-law suite? Or are you simply getting tired of the layout of your current space? If any of this rings true, it might be time for a renovation intervention. In other words, you should consider making some changes to your existing floor plan.

Before you dive into an alteration of your home's existing floor plan, it helps to get in touch with your inner architect. Would your ideal dwelling have defined functional spaces or an open layout? Would it have sleek, modern lines, or a classical, romantic feel?

Once you've figured out who you are, you can begin to plan an alteration to your existing home. The word "plan" is important since most changes to an existing floor plan are not weekend DIY projects. These tend to be bigger jobs that require professional help and, in many cases, a skilled architect. And since certain alterations may not be allowed or require a permit, it helps to consult with the experts.

Not sure where to begin? To get some ideas, take a look at our list of 10 ways to alter your existing floor plan. We'll start at the top.

For homeowners who need additional living space, the attic often offers the most in terms of bang for your renovation buck. Since attics tend to run the length of a home, they offer enough space for multiple rooms, including home offices, home theaters, bedrooms and bathrooms.

One downside to attic renovations is that they may be expensive. For example, adding a bedroom and bathroom to an attic averages more than $50,000 [source: Remodeling Magazine]. The plus side is that there is also a high amount of payoff in terms of recouping those costs in your home's resale value –- as much as 73 percent.

As with any home remodeling projects, there are some important points to consider before you begin an attic renovation. For starters, you'll need to be sure your home is strong enough to support the added weight of additional rooms, which requires architectural expertise, according to Omar Garcia, co-founder of SOGA Construction. Additional considerations include the installation of stairs for attic access, re-routing of mechanical systems, and whether the attic has a high enough ceiling to accommodate the intended living space.

An attic addition can potentially be a do-it-yourself project if you're quite handy, particularly if you've hired an architect to draw up the plans for the project. Those with two left thumbs should hire a professional builder, at least to help with the structural elements. The last thing you need is to have your awesome new loft come crashing down into the lower levels of the house.

9: Transform One Room into Two

There are many different architectural approaches to homes these days. Some are designed with many small rooms, while others have fewer, larger rooms. In the latter case, homeowners may want to carve out a functional nook within a larger room, perhaps for a nursery or home office. You might also want to turn one large bedroom into two smaller ones for kids who are tired of sharing a room.

To convert one room into two, we suggest one of two approaches. The first is to build a permanent partition wall, which is any wall designed to separate a room (i.e., not a load-bearing wall). This is the more common method of dividing a room, and it is a fairly easy do-it-yourself project or a low-cost contractor job. The main consideration is re-routing of electrical lines, if necessary, which should be done by a licensed electrician.

The second option for transforming one room into two is by using a movable wall. This is a great way to partition off a space at certain times while allowing the flexibility to open it up at others. Moveable walls have been around a long time, but only recently have tracks and materials become highly operable and aesthetically pleasing. These days, homeowners interested in movable walls have a variety of options to choose from, including glass or other transparent materials [source: Garcia].

Is your house feeling cramped or overcrowded? Evict your car and turn your garage into a spectacular party room or man cave! Garages are a great way to add square footage to your living space with relatively minor modifications. And they're also usually roomy enough for plenty of layout options.

Before diving into a garage renovation project, be sure to consider what it will take to make the space habitable. Is your garage currently wired with electricity? If so, space heaters may be all you need to keep warm in winter. Do you need a bathroom in your garage get-away? If so, adding a plumbing system will substantially increase the scope and cost of the project.

If you want to keep your car's parking spot, consider building a yoga space or art studio on top of the existing garage. In that case, you'd begin by removing the roof and building a floor that can accommodate the added level. Just be sure you've sealed the lower level ceiling so that carbon monoxide and other gases don't penetrate the space. You might still want to include fans and plenty of windows in your above-garage abode just to be sure the air is kept clean. Once you've framed out your new space, just replace the roof and voilá . you have a fabulous new room.

One potential problem with converting garages into living spaces is zoning. Cities vary greatly in their tolerance of such renovations, so be sure to check with your local zoning office.

Taking down a ceiling isn't technically a floor plan alteration, but it can certainly transform a room, making it appear larger and brighter. It's also a great way to make a mediocre living room or master suite magnificent without a lot of work. In fact, taking down a ceiling is probably one of the few jobs in this list that don't require the help of a licensed professional. According to Mike Fowler of Fowler Architects in Washington, D.C., this you can do yourself since you don't need architectural plans to take down a ceiling.

Tearing down ceiling drywall is fairly easy, though some homeowners may want to hire a professional to re-drywall a vaulted ceiling for a smooth finish. You might also choose to install new lighting fixtures, which may require professional help to re-route electrical lines.

If you decide to leave your roof beams exposed, there are lots of ways to artfully incorporate them into the space. Options include traditional wood-stained beams, beams that are painted the same color as the room, or beams with additional design features, such as a modern lighting scheme.

There are also a few important considerations. For starters, you may not know what you'll find when you tear down the drywall that hides the rafters and other structural supports that hold up the roof. These may great-looking architectural elements, or stained and pockmarked old beams. The condition of these elements may determine whether you cover them with paint or other material.

In the old days, residential architecture generally included several small rooms rather than one big one, mainly for ease of heating and cooling. These days the trend in floor plans is the open concept, in which one large space (i.e., a "great room") connects to smaller areas of a home. Having an open floor plan can add natural light, make a space feel bigger, and be great for entertaining. Each of these effects can also increase a home's resale value.

According to Tom Kavanagh, a realtor with the Capitol Realty Team, a lot of buyers prefer open concept floor plans, and homeowners who turn several smaller rooms into one large great room generally do well in terms of resale value, particularly if you can maintain the architectural character of the original home design.

"Having an open concept is great, but it may be valuable to retain some definition to your functional spaces," says Kavanagh. "It helps to work with a builder who will at least try to maintain original features of the home from a design perspective."

A general contractor is qualified to work with load bearing walls, installing beams to support upper floors where necessary. But some jobs require a structural engineer, especially when creating a very large great room from several smaller ones [source: Garcia]. In some cases, a structural engineer is needed in order to define what the structure needs in terms of support as well as the size and strength of each beam that will be bearing weight. You also need to consider the loss of walls for mechanical systems and making sure your heating and cooling systems can accommodate the new layout.

By creating an open floor plan, homeowners may find themselves suddenly on display if interior walls previously shielded the main living area of the home. Sheer curtains or window coverings that remain open on the top but closed on the bottom allow for privacy without the loss of natural light.

Interested in dining al fresco? There is perhaps no better way to merge indoors with outdoors than to build a deck off the side of a home. It's also a great way to increase your home's value. Adding a deck is one of the top five ways to stretch remodeling dollars, with most homeowners recouping about 70 percent of the cost of the deck in their resale value [source: Remodeling Magazine].

Fowler notes that adding a deck is particularly important for smaller homes with interiors that can seem cramped or dark, such as row homes and townhouses. Deck additions are also a great way to add natural light to a home since they are generally connected to the house by glass doors.

"We do lots of decks that have large French, sliding, or folding doors," he says. "They can really brighten up a home."

One of the most important considerations when building a deck is maintenance – most homeowners would prefer to keep this to a minimum. To keep your deck structurally sound and looking good over the years, choose a higher end building material such as Brazilian hardwood. You could also go with a wood alternative such as composite decking, which may last longer than wood [source: Donaldson].

For homes in warm weather areas, having a deck is essential in terms of your home's value. This is particularly true if most of the homes in your neighborhood offer an outdoor living space. If so, buyers in the area will come to expect it.

One of the best and easiest ways to increase the value of your home is to add a bathroom.

"A lot of older homes were built with only one bathroom despite having two floors and at least three bedrooms," says Kavanagh. "Newly constructed homes typically have at least two full baths upstairs and a powder room below, so having only one bathroom can limit the resale value of a home."

One important consideration when constructing a new bath or powder room is whether you can afford to give up the square footage. For many older homes, particularly in urban areas, you may need to sacrifice closet space or a small bedroom for an additional bathroom or powder room. These can be tough choices, but ones that are important from a resale perspective since buyers have come to expect multiple bathrooms, particularly those who are making the switch from a newer home to an older one.

Additional considerations for adding a bathroom include where to put it –- you'll save time and energy by choosing a location near existing plumbing lines. If you need to tear down walls in order to bring plumbing from another side of the house, the cost of the job goes up substantially [source: Garcia]. You should also find out if a building permit is required. Homeowners who bypass this requirement may face fines or risk having a job that's not up to local building codes, which can be a problem for resale purposes.

3: Build a Basement Bonus Room

Whether you're looking for an additional bedroom, home theater, or man cave, finishing a basement is a great way to add much-needed square footage to your home. Basements are also wonderful for guest rooms, workout areas, playrooms, or home offices for the increasing number of Americans who work at least part-time from home.

There are a several important things to consider before designing a basement space, including any zoning or permitting requirements as well as issues that arise when building below grade, such as air quality and susceptibility to mold [source: Garcia]. Safety is also an issue for basements – you need to be sure there are adequate escape routes in case of fire. Water is probably the biggest potential enemy in a basement. Before you build, make sure your underground lair is dry. Any water seeping in is bound to cause problems down the road.

Overall, basement renovations command a lot of bang for your renovation buck, even in a soft real estate market. According to Kavanagh, this is especially true if the basement re-do includes an extra bedroom, home office, or the potential for rental income from a self-contained flat.

"Having a basement apartment that could potentially be rented out adds a lot of value to a home," he says. "In some markets and depending on the size and finishes of the space, basement apartments can off-set a sizable portion of the homeowner's monthly mortgage."

Building laterally onto a home is a common way to alter a floor plan and gain additional living space. Examples include expanding a living room or kitchen or adding a sunroom or other entirely new space to an existing floor plan.

According to Mike Fowler of Fowler Architects, lateral additions are very common and can be a good alternative to building an additional level in historic districts or in areas where building up is not an option. Two important considerations are having the lot space to accommodate the addition and maintaining harmony between rooms.

"You have to consider the arrangement of windows, furniture placement, and the flow from room to room," says Fowler. "You want the addition to complement the existing space, not overpower it."

The best thing about building an addition onto an existing property is that it's relatively easy to construct and it adds substantial value to a home in terms of resale. Lateral additions also offer flexibility. You are limited only by your imagination. Well, that and your local zoning commission.

Hot spots and cold zones are common in floor plan alteration projects. When changing the layout of your home, be careful about cutting off an area of the house from the heating and cooling system. You want to ensure that the temperature stays constant throughout the area.

1: Add an Additional Floor

Do you need a lot of new space in your home, but don't want to move? Consider moving up in the world. Up a floor, that is. What's great about adding an additional floor to your existing home is that there are infinite possibilities for what to do with the added space, including extra bedrooms for your growing family or the master suite of your dreams.

Once you've decided to go vertical with your floor plan alteration, there are a number of important considerations to take into account. For starters, you need to know if your beams will carry the additional load of an added floor and whether the foundation of your home is strong enough to hold the extra weight [source: Garcia]. Also, you may need to consult an architect and a structural engineer about whether the interior walls will need to carry some of the additional weight and, if so, if they will need to be reinforced. Lastly, you may need a new roof to go with the additional level, though in some cases the old roof can be reused.

Additional considerations with a vertical addition include building the floor to withstand high winds in hurricane regions and seismic activity in earthquake zones. Finally, there may be zoning issues that would prevent the addition of an upper level. Every city is different in this respect, so be sure to check with your local zoning office.

"The addition of an upper level is a great option when you don't have the ability to expand laterally," says Omar Garcia of SOGA Construction. "More importantly, it's a blank canvas in terms of designing the space exactly how you want it, and that's extremely appealing for many homeowners."


Label internal data

One effective strategy we followed before we began labelling data, was to go through many example images and reach an agreement on how to label them. We were surprised by how much debate we had amongst ourselves on such a simple task. What came out of these discussions are concrete instructions on how to label each class with positive and negative examples for our in-house annotators. These instructions are critical for getting good annotations.

Even with that effort, it was impossible to capture all corner cases. Some examples are illustrated in Fig. 5. This ambiguity existed in almost all datasets and there were always multiple ways of labelling the same object. The key takeaway here was the need for consistency in annotation. For example, if we decided to label shower doors, then all shower doors need to be labelled throughout the dataset. As a result, we set up daily review sessions in the first week, where annotators presented cases they had doubts about, and we discussed and reached a consensus as a team on how to label these cases. Later on if annotators had questions, they would post them in the slack channel and we can quickly follow up.

Fig. 5 Corner cases for WDO annotations. (a) How do we label occluded doors? (b) Are shower doors counted as doors? (c) How do we handle objects on loop closure? (d) Do we annotate objects in a mirror?

Within two months, we have collected annotations for about 10,000 panoramas. On average, there are 6 bounding boxes in each panorama, including 2.7 doors, 2.3 windows and 1.0 openings. This unique dataset is the core asset of this project.


Introduction

A floor plan is the most fundamental architectural diagram used to show the layout of rooms in a building by the spatial configurations between its elements (e.g., windows, doors, walls) and room type texts. In the past decades, floor plan understanding remains an active research topic in the field of pattern recognition and document analysis.

Specifically, given an input floor plan image, not only the individual floor plan elements as well as their geometric properties (such as wall length, window size) are detected, but also the meaningful semantic units associated with high-level information (such as room function) can be identified. Early works focus on analyzing floor plans based on low-level image processing, such as line detection through Hough transform [1], graphical symbol recognition by a bag-of-words model [2]. However, the performance of these methods is largely limited by the representation power of the hand-crafted features.

Recently, several data-driven techniques [3], [4], [5] based on the convolutional neural networks (CNNs) have achieved promising results. However, the public datasets [6], [3], [7], [8] they used are collected from the apartments of urban residence, where the complexity of the floor plans is limited, e.g., the types of rooms are relatively few and the graphic elements are regular straight lines. By contrast, the architecture of rural residences is more complex, and their structure of rooms is related to the local environment, production mode, and residence lifestyle. Therefore, buildings in different rural areas have different geometric structural layouts and functional space rooms as shown in the Fig. 1 (top) for the two rural residence floorplans from different provinces in China. In general, these learning methods cannot achieve satisfactory prediction results on the rural dataset due to various irregular geometric elements (including arcs, diagonal walls) and a large number of room types.

In this paper, we propose a new deep learning-based recognition framework with an emphasis on better-understanding floor plans of rural residence. To this end, we have first built a new dataset containing 800 real rural residential floor plans from the China region, in which the annotation ground truth of each floor plan is manually labeled. Compared to previous work [4], [5], we not only labeled the geometric elements and the room semantic information but also labeled the room type text information. Because there are many room types in the rural floor plan, the room prediction accuracy is far beyond satisfactory. Our algorithm is based on two intuitive observations: first, walls and openings (doors and windows) are the graphical elements corresponding to the room boundary, and a closed 1D loop forms a certain room, where an adjacency between two rooms means they are connected by sharing walls or openings second, the semantic information (e.g., the room function) of room regions can be obtained through room type text detection and identification. In our approach, we first propose a joint deep neural network to address the graphical elements recognition and text detection of floor plans simultaneously. Then we recover the geometric and semantic structure of a floor plan by optimizing elements prediction results and using a MIQP-based room parametrization and segmentation method. Finally, we construct a room layout graph with room attributes and adjacent relationships from the input floor plan. In summary, our main contributions include: •

We design a joint neural network that simultaneously performs two tasks: recognizing basic graphical elements and detecting the room type text. Followed by room segmentation and layout generation, our framework is simple and effective to handle a variety of floor plan images. We also provide a splitting module to handle the input with arbitrarily large size.

To effectively train our networks, we provide a novel and high-quality image dataset, called RuralHomeData, containing 800 real-world floor plans of rural residence with man-made detailed annotations. To the best of our knowledge, we are the first to analyze the floor plans of rural residence.


How to Export High Quality Views from Revit for Reports

While I typically use a premium screen capture and image annotation tool on a daily basis. I’ll suggest anyone to try this tip to take higher quality image shots within Revit as a Render image. It will store the image in the Revit Project Browser, after which the image view in Revit is exported to a standalone file for creating reports.

First select a view be it plan, section, 3D or whichever you need. Set the view as needed to optimize how it is to be presented in the report. Shading and Sun settings can be set for best presentation. In my case, using a 3D view with a section box, I want to zoom to a portion of the model to illustrate a point in my report to the project’s stakeholders. From the image below, after I have spun the oriented the view as needed, I am ready to save the view as an Image.

Do a right-click on the view name in the project browser and right-click on the name to open the context based tools. Select the Save to Project as Image.

This will then bring up a setting dialog box.

Just follow the setup as per the image above, giving consideration on how the SAVE Image to Project would need to be optimized for your report.

After the image is saved, look in the Project Browser for the saved image under the Rendering Category, be mindful custom browser organization might not display the Renders category.

Before beginning to export the freshly created view(s), save the project in order that the saved view be visible in the list of views to export. Go to the Revit Start Icon at upper left corner of the Revit session. Just be aware that there are many export options and it’s necessary to scroll down to near the bottom to find the Export as Image option.

Just note though that the Export Image dialog box does offer a way to directly export the current view as opposed to the steps we went through to create the Render views beforehand. The process I showed lets us set up several views to save to the Renders category and be exported in one step.

After exporting browse to the folder the Project is in and find the files that were created in the Export procedure.

The quality might be a little better than that of a screen grab. Try and share with your colleagues on the project teams.

BONUS ROUND

I have to share this little gem.

We all know when placing a family component to the model that the spacebar option rotates the family along a 90 degree segments? Right?

Well try this option: When the non-hosted family must be rotated in respect to a previously placed line or reference plane of some unknown angle in the model. What most will do is place the family and then rotate it. A few more steps than this option presented.

Begin to place the component family and then hover the family over the angled reference (or arc), highlighting the reference (line, wall, etc). c. Click on the spacebar—the preview placements will flip between perpendicular or parallel to the reference object.

By David Metcalf, CADSoft Consulting

This article was originally posted on the CADSoft Consulting Revit blog and is reprinted here with kind permission.


Introduction

The motivation for improving the effectiveness of renovation designs comes from three simple facts. The first is that building stocks increase slowly (by 1% every year) and are therefore mainly composed of constructions [1]. The second is that developed countries face several environmental challenges that require drastic measures to dramatically enhance energy efficiency. The third is that building energy consumption represents between 20% and 40% of the total energy use in Europe and as such, building energy efficiency is one of the main levers to significantly impact global energy efficiency [2]. The combined consideration of these factors leads to the conclusion that only through significant enhancement of the renovation practices will we be able to reach the challenging goals set by contemporary environmental issues. This view is widely acknowledged and some research works have already demonstrated that advanced support such as decision–support systems for renovation action selection and assessment can bring significant benefits in terms of cost and energy efficiency [3].

However, some hurdles remain, among which one stands out clearly: the lack of digital information for existing buildings and, especially, of computable data, which prevents any intensive use of ICT tools (CAD tools, numerical simulation) that are so beneficial to building design practices. Most existing buildings have been designed and built following paper-based, 2D approaches, which result in few, if any, digital data. This particularly applies to 3D digital models (and more widely to Building Information Models) that, despite their importance to ICT-enabled design, are rarely available for existing buildings. One critical short term research challenge is therefore to devise effective and reliable methods and tools to reconstruct 3D digital models of existing buildings.

This diagnosis is not new: numerous research works have attempted to deal with the creation of existing buildings' 3D digital models. For instance, a recent literature survey [4] classified techniques into two groups, namely: non-contact techniques such as photogrammetry, videogrammetry, laser scanning, tagging and the use of available information, and a second group based on contact techniques such as tape measures or calipers. An alternative technique is to redraw 3D models manually using 3D modelling software tools but, in any case, all studies highlight that, regardless of the method chosen, the 3D model creation is a complex (requiring advanced skills) and time-consuming task.

The corollary of the above consideration is the impossibility of creating 3D models at reasonable costs. This explains the relatively low take-up of BIM (Building Information Modeling) and numerical simulation in the scope of renovation design practices and highlights the importance of the research area considered in this paper. Relying on extensive BIM and simulation design–support is, at the present time, particularly detrimental to the effectiveness of renovation design and construction. Indeed, BIM is widely acknowledged as the basis of modern building design [5] and brings significant benefits, far beyond visualization and CAD-based design: BIM embeds most of the building technical information about the building being designed and allows for seamless design data flow and management. The main difference between BIM and sole CAD tools is that digital models do not only include 3D geometrical and topological information, but also structured and semantic data, allowing for advanced query and analysis of design options [6]. One issue, however, is that BIM translates into expended but also more complex digital information. In the case of existing building digital model creation, this generates additional difficulties and requires more reliable model checking and validation techniques on top of 3D model generation tools.

The motivation for writing this paper can therefore be summarized as follows: (a) renovation can have a major impact on building stocks global energy efficiency (b) advanced software design–support (of which BIM is a key element) significantly enhances the building design effectiveness (c) enabling BIM-based, ICT-supported renovation design processes call for an effective and reliable approach to create and generate existing buildings' BIMs.

With these issues in mind, the objective of this paper is to pave the way for cost-effective generation of 3D BIM models through three key contributions: (i) the first is a critical review of a wide spectrum of techniques to generate 3D BIM, which highlights their strengths, weaknesses related applications and potential synergies. One important conclusion of this analysis is that there exists no universal solution i.e., choosing one approach over the other significantly depends on the intended application, and on project-specific constraints (ii) a focus on 3D BIM generation approaches based on scanned paper plans, including a step-by-step study of the generation process (image processing, building elements recognition, BIM generation and validation). This focus shows that the research results in this area are substantial, but fragmented – only limited parts of the generation process are usually addressed, without any real attempt to provide an exhaustive approach (iii) substantial discussion and a research roadmap outline the potential of combining existing 2D-drawing-based and 3D-model approaches with the available methods based on images-processing.

The paper is structured along the above objectives. Section 3 provides a critical review of the proposals in the literature that focus on creating 3D models of buildings. The strengths, weaknesses and preferential applications of each approach are highlighted and discussed. Then, the methods based on the automated processing of scanned paper plans are thoroughly reviewed in 4 Recognition of geometric primitives and preprocessing, 5 Recognition of building elements, 6 3D building model checking and validation. Section 4 focuses on image processing and geometric primitives recognition, Section 5 gives a detailed account of building element recognition methods and Section 6 addresses 3D model checking and validation. Finally, Section 7 provides a conclusive discussion and highlights the next steps to take in the area of 3D building model creation.


Dimensioning Techniques

Dimensions are placed on the floor plan. Note that the dimension lines are drafted lighter than wall lines and are generally done as a continuous group or string of numbers along a line. The extension line begins slightly away from the object (a minimum of Vi6 inch or 1.58 mm), never touching it. It extends about V8 inch (3.17 mm) beyond the dimension line. Arrows, dots, or 45-degree tick marks (most common) are used at the extension line and dimension line junction. The arrows, dots, or tick marks are drawn with a thicker and/or darker line to make them stand out graphically. The 45-degree tick marks are drawn in a consistent direction. However, some draftspersons slope the tick marks for vertically read dimensions from left to right and horizontally read dimensions from right to left. When using the computer, any of these three graphic symbols (arrows, dots, or ticks) can be called up and consistently inserted for all dimensions.

Dimensioning on a floor plan usually requires two or three continuous dimension lines to locate exterior walls, wall jogs, interior walls, windows, doors, and other elements. Exterior walls of a building are dimensioned outside the floor plan. The outermost dimension line is the overall building dimension. The next dimension line, moving toward the plan, indicates wall locations and centerlines to doors and windows. Other miscellaneous details in the plan (such as minor offsets, jogs, or cabinetry and fixtures) are located on a third dimension line. This hierarchy of line work allows the carpenters and other trades to quickly locate major framing elements and minor details by referring to the appropriate dimension line.

Dimensioning on a floor plan is grouped hierarchically, working from the overall dimension of the exterior walls to the smaller components of a building or space, such as wall jogs, interior walls, windows, doors, and other important elements. Dark tick marks at 45 degrees to a dimension's extension line are the most common technique for indicating junction points.

A leader is used to indicate the distance of 1'-3J'2" from a wall corner to the check-in shelf on this partial plan, as the space within the dimension line is too small to letter in. Floor plans in small residential projects often depict material finishes, such as this tiled floor in the entry, kitchen, breakfast area, and utility room.


Development of an Omnidirectional-Image-Based Data Model through Extending the IndoorGML Concept to an Indoor Patrol Service

Different indoor representation methods have been studied for their ability to provide indoor location-based services (LBS). Among them, omnidirectional imaging is one of the most typical and simple methods for representing an indoor space. However, a georeferenced omnidirectional image cannot be used for simple attribute searches, spatial queries, and spatial awareness analyses. To perform these functions, topological data are needed to define the features of and spatial relationships among spatial objects including indoor spaces as well as facilities like CCTV cameras considered in patrol service applications. Therefore, this study proposes an indoor space application data model for an indoor patrol service that can implement functions suited to linking indoor space data and service objects. In order to do this, the study presents a method for linking data between omnidirectional images representing indoor spaces and topological data on indoor spaces based on the concept of IndoorGML. Also, we conduct an experimental implementation of the integrated 3D indoor navigation model for patrol service using GIS data. Based on the results, we evaluate the benefits of using such a 3D data fusion method that integrates omnidirectional images with vector-based topological data models based on IndoorGML for providing indoor LBS in built environments.

1. Introduction

As time spent indoors increases, interest in different types of indoor-location-based services (indoor LBS) is also increasing. However, most indoor LBS, such as indoor navigation, are still at the level of simple viewer or attribute searches. There is demand for indoor LBS utilizing indoor spatial information that provide not only simple services but also indoor facilities management, indoor simulation, indoor monitoring, and so on. To provide services in large buildings with complex indoor structures, it is necessary to provide services based on spatial recognition and analyses of indoor spaces, rather than the simple services currently available. The fields that utilize indoor spatial information should also be considered in providing indoor LBS. The types of service and indoor spatial representation may vary depending on the field and scope of service utilization.

According to a questionnaire survey of indoor space specialists conducted by the IndoorGML Standard Working Group (SWG) in Open Geospatial Consortium (OGC) in 2015, the utilization of indoor spatial information would be most effective in disaster responses, indoor facility management, firefighting facilities, and equipment management [1]. This suggests that safety and facilities management should be considered first when utilizing indoor spatial information. Based on this, this study intends to focus on indoor patrol services for ensuring indoor safety. Such services are necessary not only for disaster prevention but also for crime reduction.

Conventional outdoor patrolling is a process of directly searching a targeted area to prevent crimes, disasters, and safety problems in advance, while indoor space patrol applications in GIS deal with not only this process but also real-time surveillance. This leads to the conclusion that indoor space patrol services conduct patrol and surveillance activities at the same time. A patrol in an indoor space is intended to record and update information, investigate a space along a certain route, and manage facilities in that space. To provide this service, it is necessary to recognize a patrol object and analyze a space that is a patrol object. In particular, in a case where not only a space but also facilities such as closed-circuit television (CCTV) cameras and fire extinguishers should be considered as service objects like patrol services, it is necessary to identify the spatial relationships between spaces as well as the relationships between objects in indoor spaces, such as spaces and facilities that are service objects. In order for users to receive various services, it should be possible to carry out integrated queries and analyses of users and service objects as well as spaces. So, indoor space modeling data should be linked so that not only the spatial relationships among spatial entities but also the relationships between indoor space and service objects can be considered simultaneously.

In order to resolve the requirements described in the above, this study proposes an indoor space application data model for an indoor patrol service that can implement functions suited to linking indoor space data and services. The study derives patrol service functions and establishes the relationships between indoor spatial information elements needed for the related functions. This study also presents a method for linking data between omnidirectional images representing indoor spaces and topological data on indoor spaces based on IndoorGML [2]. Finally, we conduct an experimental implementation of an integrated 3D indoor navigation model for a patrol service using GIS data on a building on the campus of the University of Seoul, South Korea. Based on the results, we evaluate the benefits of using such a 3D data fusion method that integrates omnidirectional images with vector-based topological data models based on IndoorGML for providing indoor LBS in built environments.

2. Previous Studies

Patrol services in indoor spaces have used one of two methods: human direct action methods or human indirect action methods. The human indirect action methods use an unmanned aerial vehicle (UAV) or robot to patrol or monitor indoor spaces that human beings cannot directly access, such as radiation-damaged areas [3]. In case where a robot or UAV is used, a patrol is carried out after determining the indoor position and computing the patrol route. Human direct patrol and surveillance using CCTV are the most widely used methods for anticrime efforts in indoor spaces such as shopping malls and public agencies. In recent years, surveillance in indoor spaces has moved beyond simply reviewing CCTV images to enhance its functioning. For more intelligent surveillance, systems have been developed to make 3D models of CCTV installation spaces [4] using Building Information Modeling (BIM) [5] and so on and to represent the location of CCTV cameras in a space so that a response can be made immediately in case of an emergency.

Indoor space models used in the implemented patrol service applications have been developed largely based on 2D representations and 3D representations of indoor spaces in built environments. The most typical methods of 2D representation include a 2D floor plan and a digital CAD drawing. 2D floor plans and digital CAD drawings represent an indoor space as a 2D object [6]. In this case, an indoor space is represented as a simple geometric object—a polygon. Although a digital CAD drawing can represent an indoor space as a solid 3D object, it cannot include topological information or various attributes, so the space is generally represented as a 2D object [6]. Although many systems still use 2D modeling data, 3D modeling data can be used to understand spatial characteristics and arrangements that cannot be identified by a 2D representation method alone this allows for a greater variety of analyses [7]. Representing an indoor space using a 3D approach usually involves portraying it as geometric primitives or images [8]. Various studies have attempted to develop methods for representing an indoor space as geometric primitives. 3D geometric modeling uses a more detailed picture of a geometric object than the 2D representation method and enhances the sense of reality by adding texturing to the object. Technologies for indoor high-precision laser surveying or 3D vector processing and terrestrial LiDAR are used to build the higher level of data (LoD). However, they are utilized in cases where service is only possible if an indoor space is represented precisely, like military simulations and games, because of problems such as higher construction costs and larger data volumes.

Meanwhile, omnidirectional imaging is used as one of the most typical and simple methods for representing an indoor space. An omnidirectional image is created by matching and postprocessing various image data [9]. The georeferencing of a created image makes it possible to determine the location of an object in an indoor space, so a simple attribute search or query can be carried out. As this method is more realistic than a 2D representation method, it is suitable for providing services such as virtual reality, which should portray an indoor space in the most realistic way possible. Google’s Art Project [10] and Daum Store View [11] are typical examples. The Art Project is a service that makes it possible to view works in a museum, obtain information about them, and look around inside the museum. It involves creating 3D indoor spatial information using a 3D scanner. As it has both images and geometric information, services are implemented in a geotagging style so that a work can be recognized through its location. Although this method has the advantage of obtaining realistic information through images, 3D scanners such as LiDAR have a serious limitation—their high construction cost. Store View is a service that makes it possible to look inside a store, and it provides simple information about the store. It has an advantage in that its construction cost is lower because it is constructed using omnidirectional images. However, in this case, it is impossible to recognize an indoor space like in Art Project, and it is only possible to provide a viewer-level service to search for simple attribute information.

The use of 3D geometric modeling improves the understanding of an indoor space and enables the acquisition of detailed information. However, it is difficult to provide services to Web applications or smartphones because of the large data size. So, this study intends to provide services using omnidirectional images with low data construction costs and small data that represent an indoor space more realistically than a 3D modeling method. Although the georeferencing of an omnidirectional image can be used to carry out a simple property search and query, it cannot be used to analyze a space or perform various query functions like an indoor representation method utilizing 3D geometric modeling. To perform this function, topological data (showing the topological relationship) are required to define a space in the image [12]. If the relationships between indoor spaces are identified, it is possible to conduct a neighborhood analysis and derive the shortest route through an indoor space using simple-level services such as an attribute search or viewer [13]. The spatial relationships between spaces, which refers to the adjacency and connectivity between objects, are also referred to as topological relationships. Data portraying topological relationships are created using three methods: topological primitives, a matrix-based approach, and a graph-based approach [14]. In particular, the graph-based approach is the most efficient for exploring topological relationships [15].

The most typical graph-based approach is node-relation structure (NRS) [15]. NRS is based on Poincaré duality, which converts 3D objects into zero-dimensional ones, 2D objects into one-dimensional ones, one-dimensional objects into 2D ones, and 2D objects into 3D ones. So, the basic concept of NRS is representing a 3D unit space expressed as one room as a zero-dimensional vertex and the adjacency and connectivity between rooms as a one-dimensional edge representing the connection between vertices. Based on this approach, IndoorGML, which is an indoor spatial information data model, was established through the OGC, which is the international standards organization for spatial information, in December 2014 [2]. IndoorGML represents an indoor space element such as a room as a cell, the smallest unit, and defines an indoor space as a cellular space [2]. The unit space can have semantic information representing the classification and meaning of the space, geometric information representing spaces, and topological information such as the adjacency and connectivity between spaces. Geometric and topological models of a 3D space are defined through a framework, which is a structure space model. A 3D object in Euclidean space and a 3D tropology in topology space are represented with a graph using NRS [2].

Topological data created by IndoorGML and omnidirectional images provide realistic visibility and enable various analyses of an indoor space without 3D modeling. For example, it is difficult to create a patrol route using only georeferenced omnidirectional images. To realize the visualization and recognition of indoor spaces and objects needed for patrol service and use an omnidirectional image representing a space and an object efficiently at the same time, it is necessary to devise a method to link IndoorGML, which defines the indoor topological relationships, and the omnidirectional image. Also, there are cases where services can be provided only with information about CCTV cameras (such as patrol services) and facilities (such as fire hydrants). So, to apply the service in an indoor space, the representation of indoor facilities is also important. In general, the easiest way to represent service objects is to use a point of interest (POI) [16]. A POI can also represent service objects indoors but generally only represents indoor facilities. A POI is not simply a point object that only represents a location but is used for linking with various data. A POI has a great feature where it is classified and created according to the services desired by people and the functions of those services. If the service objects not provided by IndoorGML can be represented through the linking of POI and topological data such as IndoorGML, they can be applied to various services other than the patrol service in an indoor space. Besides, it is even possible to provide services for different types of spatial query other than omnidirectional-image-based services that provide viewer-level services only.

3. Development of an Indoor Space Patrol Service Application Data Model

This section presents an indoor space application data model for patrol services that integrates an omnidirectional image, a POI, and IndoorGML. Various data (either 2D, 3D, or topology) are utilized to provide a patrol service in an indoor space. We focus on image data like omnidirectional images because of their low cost and the ease of updating and maintaining them. In order to provide indoor LBS like patrol services, omnidirectional images are used instead of 3D data modeling methods through the proposed data model.

The first part of Section 3 discusses the basic concept of linking the topological data model based on IndoorGML, omnidirectional images, and a POI representing facilities for indoor patrol services. The second part of the section presents the characteristics of the reference data, which are used to link the topological data and image datasets. The next section proposes an indoor space patrol service application data model, as well as the databased schema used to implement the proposed data model.

3.1. Basic Concept of the Data Linking Method

The proposed indoor space application data model is based on the international standard IndoorGML adopted by the OGC, which defines topological relationships among spatial entities in an indoor space. Not only the topological relationships between spaces but also the topological relationships between spatial entities and facilities are described in the proposed topological data model based on IndoorGML. The data model is based on a multilayered space model presented in IndoorGML. A multilayer spatial model is a model that represents a specific space using several layers, such as a topological space layer and a sensor space layer, which are then combined [17]. In this case, the layers are spatially divided into a nonoverlapping form, and the nodes in each layer exist independently. Nodes in different layers have features that can be connected to each other. For example, in a case where a sensor is located in a room, a node representing the room and a node representing the sensor exist in a topological space layer and a sensor space layer, respectively. These nodes in different layers can have composition relationships. This is called interlayer relations in IndoorGML. However, the representation of indoor facilities is not realized in interlayer relations. So, this study intends to add the consideration of indoor facilities, which are generally service objects, based on interlayer relations. This type of data is defined as “indoor space topological data” in this study.

The indoor space topological data presented in this study represent topological relationships, as shown in Figure 1. If the existing IndoorGML data consider only the connectivity or adjacency relationships between spaces, these data consider everything from the composition relationships to the facilities in a space. As shown in the figure, in a case where there are facilities such as a bulletin board (F1) and a fire extinguisher (F2) in spaces (R1, R2, R3), the data represent not only the connectivity between the spaces but also the adjacency and composition relationships between the spaces and facilities. Based on the application of the basic concept of IndoorGML, nodes become all objects that exist in the indoor space such as spaces and facilities, and the relationship between the indoor objects is represented as an edge. Also, like a multilayer spatial model, the relationships between spaces and between spaces and facilities are represented by respective layers, and spaces and facilities are defined as independent nodes. Based on this data, the application data model proposed in this study extends the topological relationship representation method by which the existing IndoorGML represents the relationship between spaces and has a form where the subjects to which services are provided consider all relationships between the interested spaces and facilities.

However, this method is based on the NRS of IndoorGML, where one geometric object converts into one node and the topological relationships among objects are represented by edges that connect these nodes. So, this method is difficult to apply in a case where an indoor space is not represented as a geometric object. The omnidirectional image selected as the space representation method in this study is a photograph composed of a set of pixels. As it does not exist as a geometric object (i.e., a geometric model representing a room), it needs to use pixels to form images. However, the procedure for converting pixel values into nodes can be complicated. Figure 2 shows this difference. IndoorGML data derive topological relationships through one-to-one matching of geometric objects and nodes, and so the procedure is very simple. However, as the image is composed of many pixels, one-to-one matching is difficult and only one-to-many matching, where one node corresponds to several pixels, can be done.

An image can be converted into a node using a recognized object composed of several pixels. However, the data processing method becomes complicated and difficult. When one-to-one matching between indoor spatial objects is represented based on images, the topological relationships among the objects can be easily identified and applied to indoor LBS. In order to derive a topological relationship from images efficiently, this study uses “reference data” instead of directly identifying the relationships between indoor spatial objects consisting in the pixels that form the image. The reference data presented in this study, which serve as the link between images and topological data, consist of geometric objects, not pixels. One-to-one matching between the objects in an image and topological data should be made possible using the reference data. Figure 3 shows this study’s method for deriving the topological relationships among the spatial objects represented in an image through reference data. In this method, an image is converted into a graph showing the topological relationships through the reference data having geometric objects. The relationships between the objects in an image are identified through a converted graph. The relationships in this case are defined as the indoor space topological data defined above.

3.2. Reference Data for Images

As mentioned previously, this study proposes a method for identifying the topological relationships among spatial entities represented in an omnidirectional image using reference data (Figure 4). The reference data for the omnidirectional image are geometric objects that represent spatial entities and facilities in an image and serve as a link to convert a recognized object in an image into a node on IndoorGML. When the omnidirectional image is georeferenced, each pixel has 3D coordinate values. When an object is selected from a georeferenced image, the coordinates of the selected pixel can be obtained. To connect an image and the reference data, 3D image coordinates of the selected point are projected onto the floor point of the object through the floor information of the image. The image and reference data are connected by matching the coordinate values of the projected point with the reference data that have geometric data. These reference data can be easily converted into indoor space topological data representing the relations between objects in an indoor space. So, it is possible to obtain information about an object by deriving the topological relationships in the omnidirectional image and recognizing the object through this process. The performance of the function of recognizing an object through an image is determined according to the method for designing the reference data. So, it is necessary to create it using a simple method where the object in an image is recognized correctly. As shown below, this study proposes a method for creating reference data based on 2D plan layout data for spaces and facilities represented by a plane-shaped geometric object.

The reference data for indoor facilities were created based on the locations of POIs. In general, POI means an object of interest that is a service object in an indoor space, and data are created in the form of points. However, in a case where the point object is used as it is, matching may not be done correctly as mentioned above. So, it is designed as a polygonal shape larger than the size of the actual object by giving an offset of a certain size. The size of polygons in reference data is adjusted by setting the offset value larger than the actual size of the facility. When a facility is selected through this reference data, it is determined whether the coordinate value selected from an image is included in the reference data for the facility. In a case where it is included, the facility object is recognized by matching an image and the reference data.

The reference data for an indoor space are designed to have a simplified floor layout form with a 2D polygonal object. This method also identifies an object by checking the composition relationships between the coordinate values of an image and the object of the reference data, as in the case of a facility. For this, the reference data for a spatial object establish an indoor spatial plan in a modified shape according to the following three methods. First, the space in the reference data features a polygon of the simplest shape. The reference data are used to recognize objects in the omnidirectional image without being actually visualized in services. So, a room does not need to represent the actual form in detail. A room is constructed as a simplified polygon in a range where its size is kept to the maximum. Second, tolerance should be given to the reference data of the objects and space. The reference data are designed so that the coordinate value of a point selected by a cursor can be included in the geometric object of the reference data. In this study, the reference data are based on a 2D plan, and in a case where these data are used, the coordinate value of the selected point in an image may not be completely included in an object on the plan. To prevent this, an appropriate offset value is set to adjust the size of a spatial object in the plan. Finally, spaces like long corridors or wide squares may have to be subdivided. When extracting topological relationships through reference data, one polygon is converted into one node. In this method, indoor space elements such as columns and obstacles in a moving space are not taken into consideration therefore, only simple connection information can be obtained [18]. For this reason, the photographed moving space on a 2D plan is divided based on the shooting point of the omnidirectional image.

Using the reference data, an image can be converted into an IndoorGML node, and the descriptive data of recognized objects in the images are connected to the information of the node. Although this method lacks in detail and accuracy by comparing to the direct use of 3D modeling, it enables a search for basic attribute information about a room as 3D modeling does and has the advantage in that the method of creating the reference data is also much simpler than 3D modeling. Besides, the process of deriving topological data based on an image is simplified as much as possible by setting the reference data as a 2D polygonal geometric object and matching the coordinate values of the selected point in an image with the coordinate values of the reference data. The reference data is represented as different layers for facilities and spaces and also independently for the relationships between objects. For the reference data layer proposed in this study, the interlayer relations were set and represented as an edge for the relationship between a space and a facility. The relationship between spaces is one independent layer generated from the reference data layer for spaces, and the relationship between spaces and facilities seems to be generated by the interlayer relations between two reference data layers.

3.3. Indoor Patrol Facility Data Model

Facilities in an indoor space are generally represented using POIs. A POI has a slightly different form depending on the service being utilized. In the case of car navigation, road facilities and objects that can be destinations become POI. For portal sites and map services, places such as shops and public agencies that people frequently search for are POIs. A POI in an indoor space is narrower than these services based on which utilization services are provided. The POIs in the case of providing indoor patrol service, as in this study, will be security facilities such as shops and CCTV cameras. So, this study intends to propose an indoor patrol facility data model that can be applied to various indoor LBS as well as patrol services (Figure 5).

An indoor patrol facility data model has Patrol_Fac_Basic, Patrol_Fac_Location, and Patrol_Fac_Properties classes. Patrol_Fac_Basic represents a facility object and has a self-aggregation relationship that represents a space-based hierarchical structure. A space-based hierarchical structure refers to a case where a facility can be managed through other facilities in a space and the relationship can be defined. In a case where a monitoring facility such as CCTV manages a facility such as a fire extinguisher, it can be said that it has a hierarchical structure because it can monitor and manage the fire extinguisher through CCTV. Patrol_Fac_Location represents the location of a POI object and has geometric information in the form of a 2D point. It has a 1 : 1 association relationship with Patrol_Fac_Basic. Patrol_Fac_Properties contain the properties of POI and have an aggregation relationship with Patrol_Fac_Basic. Figure 6 shows the data types and code lists of these class properties.

3.4. Indoor Space Patrol Service Application Data Model

The indoor space application data model for a patrol service proposed in this study is composed of the navigation module of IndoorGML, the indoor patrol facility data model presented in the previous chapter, the omnidirectional image feature class, and the reference data feature class. The proposed data model is proposed using a Unified Modeling Language (UML) package diagram, as shown in Figure 7.

The proposed data model is composed of three parts. The omnidirectional image class and Cell Space class corresponding to an image are elements related to images and represented in blue. The topological model is based on the navigation module of IndoorGML and represented in white in the data model. Finally, the parts related to the reference data are a facility-related class, the reference data class for a space, and a plane object class, and they are represented in pink.

Also, this data model was designed to implement two functionalities largely to provide an indoor patrol service. They are a function to recognize an indoor space object and a spatial cognition function to understand an indoor space situation based on the recognized indoor object. The objects to be served most basically in a patrol are indoor spaces and the patrol facilities in the indoor space. There is a need for a method for these service objects to be recognized as indoor spatial objects through the omnidirectional image. Also, even if the objects are recognized, a full patrol service can be provided only when a necessary patrol route is derived through the objects, or indoor spatial cognizance is conducted through various spatial analyses.

The smallest unit that represents a space in IndoorGML is a cell. A space is viewed as a set of cells and referred to as a Cell Space. In this study, as a space is represented by an omnidirectional image, a Cell Space is represented in the same way. It was found that a Cell Space represented in the UML of a data model is converted into a State represented by a node in IndoorGML. As the Cell Space presented in the existing IndoorGML is represented through a geometric object, the geometric object is directly converted into a State. However, the omnidirectional image used in this study represents Cell Space with a data structure, without the modeled geometric information of an indoor spatial object. So, the Cell Space is converted into a State by referring to the reference data as having the geometric information given in the previous chapter. An indoor patrol facility class has containment topological relationships included in Cell Space. Also, a class representing location information may have point-shaped geometric information for the actual location, but the reference data are represented by 2D plane-shaped information that can be recognized in an image.

This study uses topological information on a space to enable a spatial query and thus is based on IndoorGML, which is the standard for indoor topological information. In particular, when the navigation module of IndoorGML is used, a route can be derived, and so more different types of queries can be carried out. For example, it is also possible to derive routes that will enable close inspection of the objects in which patrols have been made most recently in a space. According to IndoorGML, when an omnidirectional image is converted into a State through the reference data, the relationship between them is represented through a Transition, represented by the edge, and this is used in creating a Route Node and Route Segment on the navigation module. A route is created through a combination of a Route Node and a Route Segment. The route class of IndoorGML is set as a superclass, and patrol and inspector routes are made to inherit. In this case, “patrol route” means a route used to carry out a patrol, and an “inspector route” is a route for inspecting whether the patrol has been performed properly.

3.5. Database Design for the Proposed Data Model

In order to actually apply an indoor space application data model to a service, it is necessary to design a database for the data model. In the case of IndoorGML (because only its conceptual model is currently presented), which is based on the proposed application data model, its application to an actual service and linkage with the omnidirectional image are designed through database schema. The designed schema is divided into three parts in the data model: an image, a topological model, and the reference data. The class of each part is composed of the schema class (Figure 7).

First, a Cell Space represents an indoor space in the image part. This Cell Space class contains information about images in the database, and it becomes data with contents about the space to be represented. In the case of database schema of the Cell Space class, the ID of the cell, the name of the image, the position of the camera, the ID of the building, and information on the floor are included as minimum attributes. When a desired point is selected from an image, not only the image information but also the position information of the camera through which images are acquired also becomes data so as to obtain 3D image coordinates. It is possible to access the omnidirectional image XML data created through image information and camera position coordinates and calculate the location on the image. So, the data on that part are needed. Also, in order to identify the height value of the selected point in the process of projecting the coordinates of the selected point on an image to a floor point, information about the building and the floor where the image was acquired become data altogether.

In this indoor spatial data model, topological data are made to be derived based on an image by designing the reference data. The reference data for a data model include four classes: one is the reference data class related to the reference data representing a space, and the other three are classes representing the facilities. As data are too complicated to create all classes as databases, the reference data for spaces and the reference data for facilities are recreated into two parts. “Class for facilities” is the reference data for facilities, and it needs at least three attributes, which represents node ID, facility type, and polygon-shaped geometric information. Also, “class for room” has a node ID, room name, polygon-shaped geometric information, building ID, and floor information. These two sets of data become the links with a class that has the information of the selected point in an image. In a case where the coordinate value of the selected point in a Cell Space is matched with polygon-shaped geometric information in the reference data, they are intermediaries that can retrieve the information of the point selected through the node ID of the matched data.

Reference data are related to the Cell Space class. The reference data on facilities are included in the Cell Space, and the reference data on the room are converted into the State class according to the duality in the process of the Cell Space class. The part showing the topological relationship of spaces represented by a Cell Space corresponds to a topology model in an indoor space application data model. The topology model is based on the relationship between the class name and class of IndoorGML but as a schema is not specified in IndoorGML this part is designed to match the indoor patrol service that this study focuses on.

Data was created for four classes out of the five represented in the data model. These are State, Transition, RouteNode, and RouteSegement. The State class can be seen as the topological data that the objects represented by Cell Space are converted into. The node IDs of the room and facilities classes are matched with the node IDs of the State. The matched nodes have the information about objects. They also contain the information about the edge connected to the nodes and about node types. The types of nodes are divided into facilities and spaces and connected to attribute information, which is nonspatial data, according to each type. Facilities have attributes related to the indoor service. In the case of a patrol service, the attribute information, which can be accessed through the node ID, contains information on the types of facility, the number of rooms adjoining facilities, the management, and the date of inspection. Spaces also have attributes related to the indoor service, and they are also accessible through the node ID. The attribute information about spaces consists of the name of the room, the ID of the building, floors, the room’s number, and the date of inspection.

These State classes represent the mutual relationship as a Transition class, like the nodes of the topology model. The Transition class has an edge ID, a starting node, an ending node, an edge type, and a weight. The types of edge represent the connections between spaces and between spaces and facilities. A weight can be a time, distance, and so forth, depending on the situation. In this study, distance was set as a weight. The State and Transition classes were also used as elements to form the RouteNode and RouteSegment classes, respectively. RouteNode serves as a node for a route by referring to the State, and RouteSegement has information about each edge connecting routes by referring to a Transition. Given information about a starting point and a destination, a route can be derived through the combination of these two classes.

4. Experiment

The study area for the experimental implementation was the 6th floor of the 21st Century Hall of the University of Seoul, South Korea (Figure 8). For the study area, images were captured at 22 locations and acquired in 6 directions at each location. The environment for the experiment is shown in Table 1. The functions developed in this study were implemented based on a prototype developed in a study called the “Development of 3D Indoor Space Information Technology and 3D Indoor and Outdoor Integrated Platform” funded by the Korea Agency for Infrastructure Technology Advancement, South Korea.