Integration of different geospatial data in urban areas: a case of study

Franci, Francesca; Lambertini, Alessandro; Bitelli, Gabriele

doi:10.1117/12.2066614

Cited by 8 publications

(9 citation statements)

References 3 publications

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“…Another Contrast Split Segmentation has been carried out on nDSM slope, as the generated slope layer from LiDAR data has strong contrasts of brightness on the edge of the buildings and the steep and flat areas are easily recognizable: the flat areas are associated with a very dark color, whereas the steep areas are tending to white. As reported also in other studies [56,57], the segmentation is based on the information of slopes related to the real footprint of the buildings .…”

Section: Conflicts Of Interestmentioning

confidence: 69%

LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

Ullo

Zarro

Wojtowicz

et al. 2020

Sensors

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The aim of this paper is to highlight how the employment of Light Detection and Ranging (LiDAR) technique can enhance greatly the performance and reliability of many monitoring systems applied to the Earth Observation (EO) and Environmental Monitoring. A short presentation of LiDAR systems, underlying their peculiarities, is first given. References to some review papers are highlighted, as they can be regarded as useful guidelines for researchers interested in using LiDARs. Two case studies are then presented and discussed, based on the use of 2D and 3D LiDAR data. Some considerations are done on the performance achieved through the use of LiDAR data combined with data from other sources. The case studies show how the LiDAR-based systems, combined with optical Very High Resolution (VHR) data, succeed in improving the analysis and monitoring of specific areas of interest, specifically how LiDAR data help in exploring external environment and extracting building features from urban areas. Moreover the discussed Case Studies demonstrate that the use of the LiDAR data, even with a low density of points, allows the development of an automatic procedure for accurate building features extraction, through object-oriented classification techniques, therefore by underlying the importance that even simple LiDAR-based systems play in EO and Environmental Monitoring.

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Section: Conflicts Of Interestmentioning

confidence: 69%

LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

Ullo

Zarro

Wojtowicz

et al. 2020

Sensors

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“…Nevertheless, a clear trend cannot be inferred in this case study, probably because a direct comparison of the pairs only in terms of azimuth is not fully explanatory due to the simultaneous variation of all the other acquisition geometry parameters. All in all, the results of the experimentations suggest that elevation models from WV3 are sufficiently reliable for applications where the overall elevation of urban features is more important than the reconstruction of the exact shape (such as orthorectifying high-resolution multispectral images or enriching the information content of municipal technical cartographies about buildings [26]). On the other hand, WV3 models do not meet the quality requirements for more detailed 3D modelling of cities (targeted, for example, at the compilation of 3D cadastres or the development of emergency response plans [27]).…”

Section: Resultsmentioning

confidence: 85%

Metric Accuracy of Digital Elevation Models from WorldView-3 Stereo-Pairs in Urban Areas

2019

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WorldView-3 satellite is providing images with an unprecedented combination of high spatial and spectral resolution. The stereo capabilities and the very high resolution of the panchromatic band (0.31 m) have been fostering new applications in urban areas, where the complexity of the morphology requires a higher level of detail. The present technical note aims to test the accuracy of digital elevation models that can be obtained by WorldView-3 stereo-pairs in these particular contexts, with an operational state-of-the-art algorithm. Validation is performed using check points and existing models of the area (from LiDAR data and oblique aerial images). The experiments, conducted over the city of Bologna (Italy) with six images, proved that roof surfaces and open spaces can be reconstructed with an average error of 1–2 pixels, but severe discrepancies frequently occur in narrow roads and urban canyons (up to several metres in average). The level of completeness achievable with only one pair is extremely variable (ranging from 50% to 90%), due to the combined effect of the geometry of acquisition and the specific urban texture. Better results can be obtained by using more than one pair. Furthermore, smaller convergence angles can be beneficial for the reconstruction of specific urban structures, such as soaring towers.

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“…To automatically detect buildings and differentiate them depending on their roofing material, a new multi-level object-based procedure was developed as a process tree, starting from the approach proposed in [37]. The classification was applied to the WorldView-3 imagery and the derived DSM, using eCognition software (version 10.1, Trimble, Munich, Germany).…”

Section: Object-based Classificationmentioning

confidence: 99%

An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

et al. 2022

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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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Integration of different geospatial data in urban areas: a case of study

Cited by 8 publications

References 3 publications

LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

Metric Accuracy of Digital Elevation Models from WorldView-3 Stereo-Pairs in Urban Areas

An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

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