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An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs. REMOTE SENSING 2022. [DOI: 10.3390/rs14040849] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The availability of multispectral images, with both high spatial and spectral resolution, makes it possible to obtain valuable information about complex urban environment, reducing the need for more expensive surveying techniques. Here, a methodology is tested for the semi-automatic extraction of buildings and the mapping of the main roofing materials over a urban area of approximately 100 km2, including the entire city of Bologna (Italy). The methodology follows an object-oriented approach and exploits a limited number of training samples. After a validation based on field inspections and close-range photos acquired by a drone, the final map achieved an overall accuracy of 94% (producer accuracy 79%) regarding the building extraction and of 91% for the classification of the roofing materials. The proposed approach proved to be flexible enough to catch the strong variability of the urban texture in different districts and can be easily reproducible in other contexts, as only satellite imagery is required for the mapping.
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A Critical Review of Unmanned Aerial Vehicles (UAVs) Use in Architecture and Urbanism: Scientometric and Bibliometric Analysis. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11219966] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In recent years the use of UAVs (Unmanned aerial vehicles) have proliferated in the civil sector for purposes such as search and rescue, remote sensing or real-time monitoring of road traffic, among others. In the architecture, engineering and construction fields (AEC) UAVs have demonstrated to be an ideal technology due to their optimal performance in terms of time, precision, safety and cost. Given the rapid growth of interest in this technology, this research presents a critical review of the literature on the use of UAVs in architecture and urbanism to define the most widely used techniques and delimit the fields of application based on the experimentation published by the scientific community. A scientific mapping was carried out in two stages using the VOSviewerTM software: a scientometric and a bibliometric analysis. This technique allowed us to analyse a large body of literature and bibliographic data to obtain trends, patterns and directions of this domain of knowledge. Then, a literature review was presented, highlighting the relevant information identified in the previous analysis. The fields of application of UAVs were delimited and the most commonly used payload types and the most appropriate post-processing techniques were specified, depending on the aerial mission objective. The fields of application identified included different techniques related to the generation of 3D models, land mapping, construction site monitoring, building surveying to detect structural damage and energy losses and urban remote sensing. The literature review showed that UAVs provide a useful multi-tasking tool at any stage of an architectural project. These techniques can be applied to buildings or public spaces from the design and construction processes when the project is initiated to the later stages of maintenance and inspection of the building during its life cycle.
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Shin YS, Kim A. Sparse Depth Enhanced Direct Thermal-Infrared SLAM Beyond the Visible Spectrum. IEEE Robot Autom Lett 2019. [DOI: 10.1109/lra.2019.2923381] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Integration of Aerial Thermal Imagery, LiDAR Data and Ground Surveys for Surface Temperature Mapping in Urban Environments. REMOTE SENSING 2016. [DOI: 10.3390/rs8100880] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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