Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data
Abstract
:1. Introduction
2. Previous Work
2.1. Buildings Extraction from ALS Data
2.2. Ontology Approaches in GIScience and Remote Sensing
3. Methodology
3.1. Preprocessing Step: Automatic Extraction of Buildings from ALS Data
3.2. Classification of Building Types Data Using Ontology and Random Forest Classifier
3.2.1. Knowledge Acquisition and Conceptualization
3.2.2. Feature Selection—Rejecting Irrelevant Features and Ranking the Feature Relevance
3.2.3. Ontology Formalization and Classification of the Building Types Using Fact++ Reasoner
3.3. Accuracy Assessment
4. Results and Discussion
4.1. Buildings Extraction from ALS Data
4.2. Feature Importance Results
4.3. Results of the Ontology-Based Classification of the Building Types
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4.4. Ontology Considerations
- (i)
- The logical consistency of the developed ontology can be automatically evaluated by the existing reasoner [19].
- (ii)
- Ontology represents a declarative knowledge model that can be subject to community scrutiny and can be easily extended or adapted to new application scenarios [20].
- (iii)
- Data provenance can be easily identified [43] as the class definitions are explicitly formulated into a machine and human understandable format. Therefore, the users can assess whether the generated thematic information fits the purpose of their application.
- (iv)
- The semantics of the evaluated categories is explicitly specified and therefore, it is possible to infer implicit knowledge by running a reasoner.
5. Summary
Acknowledgments
Author Contributions
Conflict of Interest
References
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Variables | Variables | Feature Value Range | Explanations Provided by [45] |
---|---|---|---|
Extent | Area | [0, scene size] | The area of the identified object |
Shape Features | Radius 1 | [0, ∞] | Similarity of an object to an ellipse (totally enclosing the image object) |
Radius 2 | [0, ∞] | Similarity of an object to an ellipse (totally enclosed by the image object) | |
Rectangular Fit | [0, 1] | Objects squareness | |
Elliptic Fit | [0, 1] | Explains how well an object fits an ellipse | |
Asymmetry | [0, 1] | Relative length of an object compared to a regular polygon | |
Border Index * | [1, ∞] | Describes how jagged an object is; the more jagged, the higher its border index | |
Main Direction | [0, 180] | Defined as the direction of the eigenvector belonging to the larger of the two eigenvalues | |
Shape Index | [1, ∞] | Describes the smoothness of buildings boundaries the smoother the border of an image object, the lower its shape index | |
Compactness | [0, ∞] | The more compact, the smaller its border appears. Similar to Border Index, but it is based on area | |
Roundness | [0, ∞] | How similar an image is to an ellipse by the difference of enclosing and the enclosed ellipse | |
Density | [0, depending on the shape of image object] | The most dense shape is a square | |
Height | Mean Height | 2–25 m | Calculated from nDSM |
Slope | Slope | [0, 80°] | Calculated from nDSM |
Buildings Class | Natural Language Description |
---|---|
Residential/Small Buildings | High building density, small, rectangular building form (simple form) |
Apartments/Block Buildings | Rectangular or elongated form, higher than industrial and factory buildings |
Industrial and Factory Buildings | Low density building areas, larger dimensions, complex and compact building form, diverse main directions |
Residential/Small Buildings | Relevant | Not Relevant |
---|---|---|
Retrieved | 551 | 16 |
Not Retrieved | 9 | 109 |
Recall (%) | 98.3 | |
Precision (%) | 97.1 | |
F-Measure (%) | 97.7 | |
Total | 687 |
Apartment Buildings | Relevant | Not Relevant |
---|---|---|
Retrieved | 37 | 13 |
Not Retrieved | 36 | 711 |
Recall (%) | 50.6 | |
Precision (%) | 74.0 | |
F-Measure (%) | 60.1 | |
Total | 73 |
Industrial and Factory Buildings | Relevant | Not Relevant |
---|---|---|
Retrieved | 22 | 47 |
Not Retrieved | 5 | 723 |
Recall (%) | 81.4 | |
Precision (%) | 37.2 | |
F-Measure (%) | 51.1 | |
Total | 27 |
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Belgiu, M.; Tomljenovic, I.; Lampoltshammer, T.J.; Blaschke, T.; Höfle, B. Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data. Remote Sens. 2014, 6, 1347-1366. https://doi.org/10.3390/rs6021347
Belgiu M, Tomljenovic I, Lampoltshammer TJ, Blaschke T, Höfle B. Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data. Remote Sensing. 2014; 6(2):1347-1366. https://doi.org/10.3390/rs6021347
Chicago/Turabian StyleBelgiu, Mariana, Ivan Tomljenovic, Thomas J. Lampoltshammer, Thomas Blaschke, and Bernhard Höfle. 2014. "Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data" Remote Sensing 6, no. 2: 1347-1366. https://doi.org/10.3390/rs6021347