Semi-automatic Building Research Articles

Semi-Automatic Building and Learning of a Multilingual Ontology

Most online platforms, applications, and Websites use a massive amount of heterogeneous evolving data. These data must be structured and normalized before integration to improve the search and increase the relevance of results. An ontology can address this critical task by efficiently managing data and providing structured formats through techniques such as the Web Ontology Language (OWL). However, building an ontology can be costly, primarily if conducted manually. In this context, we propose a new methodology for automatically building and learning a multilingual ontology using Arabic as the base language via a corpus collected from Wikipedia. Our proposed methodology relies on Finite-state transducers (FSTs). FSTs are regrouped into a cascade to reduce errors and minimize ambiguity. The produced ontology is extended to English and French and independent language images via a translator we developed using APIs. The rationale for starting with the Arabic corpus to extract terms is that entity linking is more convenient from Arabic to other languages. In addition, many Wikipedia articles in English and French (for instance) do not have associated Arabic articles, but the opposite is true. In addition, dealing with Arabic terms permits us to enrich the Arabic module of the free linguistic platform we use in dictionaries and graphs. To assess the efficiency of our proposed methodology, we conducted performance metrics. The reported results are encouraging and promising.

Semi-automatic building footprint using multirotor and fixed wing UAV

The current techniques to extract building footprints frequently involve Light Detection and Ranging (LiDAR), as well as satellite images. The UAV is proposed to acquire aerial imagery in this study as it is more efficient, time-efficient, and inexpensive, especially for small budget projects. Hence, this study evaluated the building footprints from different UAV platforms. The objectives of this study are to perform the different UAV image processing of building footprints using fixed-wing UAV and multi-rotor UAV, as well as to analyse the outcomes of building footprints from different UAV platforms with existing layout plans. The methodology begins with a preliminary study, and continued with acquisition of data via UAV, data processing, and lastly, analyses of the outcome from different platforms. Based on the analysis in terms of area, the accuracy gained by using multi rotor UAV had been better than that for fixed wing UAV with area difference of 13.648 <em>m² </em>compared to 18.091 <em>m²</em>. Lastly, it can be summarised that the area displayed better results with multi rotor UAV, in comparison to the area obtained by using fixed wing UAV. Thus, it is recommended for future studies to use other UAV platforms and other methods for extraction of building footprints, such as orthoimages and digital elevation model.

Semiautomatic right-angle building extraction from very high-resolution aerial images using graph cuts with star shape constraint and regularization

This paper proposes a method of semiautomatic right-angle building extraction from very high resolution (VHR) aerial images, based on graph cuts with star shape constraint and regularization. In the proposed method, first, the image block containing the target building is obtained by a seed line from humans. Next, the image block is preprocessed by bilateral filtering and then the simple linear iterative clustering oversegmentation method was used to segment the image into small regions. Then regions that contain the pixels belonging to the seed line are merged as the foreground area, and the background area is automatically composed by annular regions near the imagery boundary. With the obtained foreground and background areas, the graph cuts model with the star shape prior is used to acquire the building object. Finally, we defined a method of detecting the building orientation and proposed a regularizing building method based on the corners and the building orientation. The experiments performed on two VHR aerial images demonstrate the accuracy and stability of the proposed method.

Object-Based Rule Sets and Its Transferability for Building Extraction from High Resolution Satellite Imagery

With the advent of high spatial resolution satellite imagery, automatic and semiautomatic building extractions have turned into one of the outstanding research topics in the field of remote sensing and machine vision. To this date, various algorithms have been presented for extracting the buildings from satellite images. Such methods lend their bases to diverse criteria such as radiometric, geometric, edge detection, and shadow. In this paper, a novel object based approach has been proposed for automatic and robust detections as well as extraction of the building in high spatial resolution images. To fulfill this, we simultaneously made use of both stable and variable features. While the former can be derived from inherent characteristics of the buildings, the latter is extracted using a feature analysis tool. In addition, a novel perspective has been recommended to boost the automation degree of the segmentation part in the object based analysis of remote sensing imagery. The proposed method was applied to a QuickBird imagery of an urban area in Isfahan city and the results of the quantitative evaluation demonstrated that the proposed method could yield promising results. Moreover, in another section of this study, for assessing the algorithm transferability, the rule set was implemented to a part of the WorldView image of Yazd city, proving that the proposed approach is capable of transferability in different types of case studies.

SEMI-AUTOMATIC BUILDING MODELS AND FAÇADE TEXTURE MAPPING FROM MOBILE PHONE IMAGES

Research on 3D urban modelling has been actively carried out for a long time. Recently the need of 3D urban modelling research is increased rapidly due to improved geo-web services and popularized smart devices. Nowadays 3D urban models provided by, for example, Google Earth use aerial photos for 3D urban modelling but there are some limitations: immediate update for the change of building models is difficult, many buildings are without 3D model and texture, and large resources for maintaining and updating are inevitable. To resolve the limitations mentioned above, we propose a method for semi-automatic building modelling and façade texture mapping from mobile phone images and analyze the result of modelling with actual measurements. Our method consists of camera geometry estimation step, image matching step, and façade mapping step. Models generated from this method were compared with actual measurement value of real buildings. Ratios of edge length of models and measurements were compared. Result showed 5.8% average error of length ratio. Through this method, we could generate a simple building model with fine façade textures without expensive dedicated tools and dataset.

SEMI-AUTOMATIC BUILDING MODELS AND FAÇADE TEXTURE MAPPING FROM MOBILE PHONE IMAGES

Abstract. Research on 3D urban modelling has been actively carried out for a long time. Recently the need of 3D urban modelling research is increased rapidly due to improved geo-web services and popularized smart devices. Nowadays 3D urban models provided by, for example, Google Earth use aerial photos for 3D urban modelling but there are some limitations: immediate update for the change of building models is difficult, many buildings are without 3D model and texture, and large resources for maintaining and updating are inevitable. To resolve the limitations mentioned above, we propose a method for semi-automatic building modelling and façade texture mapping from mobile phone images and analyze the result of modelling with actual measurements. Our method consists of camera geometry estimation step, image matching step, and façade mapping step. Models generated from this method were compared with actual measurement value of real buildings. Ratios of edge length of models and measurements were compared. Result showed 5.8% average error of length ratio. Through this method, we could generate a simple building model with fine façade textures without expensive dedicated tools and dataset.

Semi-automatic Building Extraction from Quickbird Imagery

Semi-automatic Building Extraction from Quickbird Imagery

Optimal fusion of optical and SAR high-resolution images for semiautomatic building detection

Building detection from different high-resolution aerial and satellite images has been a notable research topic in recent decades. The primary challenges are occlusions, shadows, different roof types, and similar spectral behavior of urban covers. Integration of different data sources is a solution to supplement the input feature space and improve the existing algorithms. Regarding the different nature and unique characteristics of optical and radar images, there are motivations for their fusion. This paper is aimed to identify an optimal fusion of radar and optical images to overcome their individual shortcomings and weaknesses. For this reason, panchromatic, multispectral, and radar images were first classified individually, and their strengths and weaknesses were evaluated. Different feature-level fusions of these data sets were then assessed followed by a decision-level fusion of their results. In both the feature and decision levels of integration, artificial neural networks were applied as the classifiers. Several post-processing methods using normalized different vegetation index, majority filter, and area filter were finally applied to the results. Overall accuracy of 92.8% and building detection accuracy of 89.1% confirmed the ability of the proposed fusion strategy of optical and radar images for building detection purposes.

Low Resolution Refinement with ProSMART, COOT and REFMAC5

Membrane proteins and large assemblies are currently a major focus of molecular biology and molecular medicine. Due to their size and flexibility, these structures may only yield poor quality crystals for which diffraction intensities can be measured to merely mid-low resolution. Nevertheless, these data contain valuable structural information. Here, it will be shown how new features in COOT [1], REFMAC5 [2] and ProSMART [3] can help to exploit low resolution data for model building and refinement, as well as aid model validation. Refinement at low resolution can be stabilised with regularisers, such as jelly-body and external restraints. These allow to routinely obtain good quality models even in cases where only low-resolution data are available (e.g. >3Å). External restraints (available for protein and DNA/RNA) exploit structural prior knowledge, utilising the assertion that local interatomic distances should agree with previous observations. Sources for such prior knowledge include isomorphous and homologous structures, hydrogen bonding patterns, and typical conformations of secondary structure elements. Importantly, global rigidity is not enforced by these restraints – the approach presented allows for dramatic conformational differences between target and reference models. Consequently, restraints may be generated using homologous reference models resolved in different crystal forms. COOT facilitates model building at low resolution by removing degrees of freedom through so-called "backrub rotamers" and torsion angle restraints, as well as providing semi-automatic building options such as model morphing and jiggle fit . Map sharpening and blurring, now available in both COOT and REFMAC5, can be employed to provide further insight regarding the validity of a model, as well as aiding the model building process. General guidelines for the application of these features are provided, along with examples demonstrating their usage.

Semi-automatic Domain Ontology Construction from Spoken Corpus in Tunisian Dialect: Railway Request Information

In this paper, we present a hybrid method for semi-automatic building of domain ontology from spoken dialogue corpus in Tunisian Dialect for the railway request information domain. The proposed method is based on a statistical method for term and concept extraction and a linguistic method for semantic relation extraction. This method consists of three fundamental phases, namely the corpus construction and treatment, the ontology construction and the ontology evaluation. The proposed method is implemented through the ABDO system to generate the RIO ontology that contains 14 concepts, 25 semantic relations and 387 concepts instances. The generated domain ontology is used to semantically label Tunisian dialect utterances in spoken dialogue.

Providing metrics and automatic enhancement for hierarchical taxonomies

Taxonomies enable organising information in a human–machine understandable form, but constructing them for reuse and maintainability remains difficult. The paper presents a formal underpinning to provide quality metrics for a taxonomy under development. It proposes a methodology for semi-automatic building of maintainable taxonomies and outlines key features of the knowledge engineering context where the metrics and methodology are most suitable. The strength of the approach presented is that it is applied during the actual construction of the taxonomy. Users provide terms to describe different domain elements, as well as their attributes, and methodology uses metrics to assess the quality of this input. Changes according to given quality constraints are then proposed during the actual development of the taxonomy.

How do we measure and improve the quality of a hierarchical ontology?

Hierarchical ontologies enable organising information in a human–machine understandable form, but constructing them for reuse and maintainability remains difficult. Often supporting tools available lack formal methodological underpinning and their developers are not supported by any concomitant metrics. The paper presents a formal underpinning to provide quality metrics of a taxonomy hierarchical ontology and proposes a methodology for semi-automatic building of maintainable taxonomies. Users provide terms to be used to describe different ontological elements as well as their attributes and their ranges of values. The methodology uses the formalised metrics to assess the quality of the users input and proposes changes according to given quality constraints. The paper illustrates the metrics and the methodology in constructing and repairing two medium size well-known taxonomies.

Study on OWL-Based Construction and Inference Method of Vegetable Products Ontology

With the rapid development of information technology, ontology is gradually used for knowledge engineering and information science sphere. Agricultural Ontology mainly is composed of the concept and relationship between concepts in agricultural knowledge, as well as the computer can identify the composition of the formal description language. The goal of build the agricultural ontology is to form a common understanding of the agricultural information organizational structure, to known and analysis of knowledge in the field of agriculture so as to further establish the foundation for the agriculture semantic web. In this paper, we present a design principles and implementation process in vegetable products domain ontology. Through the collection and analysis of the field of vegetable products information as well as the use of inference engine, used to build vegetable domain ontology with OWL. In the information extraction prototype system, we achieved a semi-automatic filled with examples of vegetable products domain ontology. Through compared to the results before and after the use of inference engine, it show that vegetables and semi-automatic domain ontology building methods is feasible.

Semi-automatic building extraction in dense urban settlement areas from high-resolution satellite images

Recent availability of high-resolution satellite images provides a new data source for geospatial data acquisition. This high-resolution data source has made it possible to extract man-made features such as roads and buildings, which are required for varieties of applications including urban planning, creation of GIS databases, and environmental monitoring. In this paper, we have developed a new method to extract buildings in urban informal settlement areas using high-spatial resolution panchromatic imagery. The proposed method uses radial casting algorithm to initialize snakes contours, and the fine measurement of building outlines is automatically carried out using snakes models. The building extraction results are satisfactory with an extraction rate of 94 percent as demonstrated by examples over a variety of selected test areas. The potential and limitations of applying this method to extract buildings is also discussed.

Building facade reconstruction by fusing terrestrial laser points and images.

Laser data and optical data have a complementary nature for three dimensional feature extraction. Efficient integration of the two data sources will lead to a more reliable and automated extraction of three dimensional features. This paper presents a semiautomatic building facade reconstruction approach, which efficiently combines information from terrestrial laser point clouds and close range images. A building facade's general structure is discovered and established using the planar features from laser data. Then strong lines in images are extracted using Canny extractor and Hough transformation, and compared with current model edges for necessary improvement. Finally, textures with optimal visibility are selected and applied according to accurate image orientations. Solutions to several challenge problems throughout the collaborated reconstruction, such as referencing between laser points and multiple images and automated texturing, are described. The limitations and remaining works of this approach are also discussed.

Using XML data integration and ontology reuse to share agricultural data

We present a data integration approach to enable multi-agency partners to share their agricultural data sources. We perform the integration in three phases. First, a structural integration phase, based on the use of XML documents warehouses (called dataweb), allows to create a warehouse for each partner. A second step consists in the integration of these dataweb by associating an ontology-based knowledge to each warehouse. That is done by a semi-automatic building of OWL ontology starting from dataweb and by re-use of the Agricultural Ontology Service. A third mediation phase permits to query in a uniform manner these different semantic dataweb.

Semiautomatic Building Line Extraction from Ikonos Images Through Monoscopic Line Analysis

This paper proposes a new algorithm for extracting building lines from monoscopic high-resolution satellite images. We focus on extracting lines from rectangular-shaped building roofs with a relatively large size. We achieve this task by devising a unique approach using line voting and matching. Our algorithm works as follows. An input point on a building roof is given manually. A region of interest is defined centered on the input point and within the region lines extracted. Then, a line voting process is applied to estimate initial orientation and position of a building line. The orientation and position are refined by a least squares matching process. We assessed the performance of our algorithm against two Ikonos images. Our algorithm extracted building lines from over 83 percent of buildings tested with average angular accuracy of one or two degrees and average positional accuracy of approximately one pixel. This result supports the algorithm proposed in this paper. The major contribution of this paper is that we provided an alternative way to line grouping approaches when edge responses from buildings are not strong enough.

Synthesizing an integrated ontology

The Mediator Environment for Multiple Information Sources (Momis) supports semiautomatic building, annotation, and extension of domain ontologies.

Application of snakes and dynamic programming optimisation technique in modeling of buildings in informal settlement areas

This paper presents a novel approach to semiautomatic building extraction in informal settlement areas from aerial photographs. The proposed approach uses a strategy of delineating buildings by optimising their approximate building contour position. Approximate building contours are derived automatically by locating elevation blobs in digital surface models. Building extraction is then effected by means of the snakes algorithm and the dynamic programming optimisation technique. With dynamic programming, the building contour optimisation problem is realized through a discrete multistage process and solved by the “time-delayed” algorithm, as developed in this work. The proposed building extraction approach is a semiautomatic process, with user-controlled operations linking fully automated subprocesses. Inputs into the proposed building extraction system are ortho-images and digital surface models, the latter being generated through image matching techniques. Buildings are modeled as “lumps” or elevation blobs in digital surface models, which are derived by altimetric thresholding of digital surface models. Initial windows for building extraction are provided by projecting the elevation blobs centre points onto an ortho-image. In the next step, approximate building contours are extracted from the ortho-image by region growing constrained by edges. Approximate building contours thus derived are inputs into the dynamic programming optimisation process in which final building contours are established. The proposed system is tested on two study areas: Marconi Beam in Cape Town, South Africa, and Manzese in Dar es Salaam, Tanzania. Sixty percent of buildings in the study areas have been extracted and verified and it is concluded that the proposed approach contributes meaningfully to the extraction of buildings in moderately complex and crowded informal settlement areas.

Semiautomatic building extraction from stereo image pair based on lines grouping and least squares matching

The paper presents a general paradigm of semiautomatic building extraction from aerial stereo image pair. In the semiautomatic extraction system, the building model is defined by selected roof type through human-machine interface and input the approximation of area where the extracted building exists. Then under the knowledge of the roof type, low-level and mid-level processing including edge detection, straight line segments extraction and line segments grouping are used to establish the initial geometrical model of the roof-top. However, the initial geometrical model is not so accurate in geometry. To attain accurate results, a general least squares adjustment integrating the linear templates matching model with geometrical constraints in object-space is applied to refine the initial geometrical model. The adjustment model integrating the straight edge pattern and 3D constraints together is a well-studied optimal and anti-noise method. After gaining proper initial values, this adjustment model can flexibly process extraction of kinds of roof types by changing or assembling the geometrical constraints in object-space.