Spatial ETL for 3D Building Modelling Based on Unmanned Aerial Vehicle Data in Semi-Urban Areas

Drešček, Urška; Fras, Mojca Kosmatin; Tekavec, Jernej; Lisec, Anka

doi:10.3390/rs12121972

Cited by 21 publications

(14 citation statements)

References 44 publications

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“…Their proposed algorithm performed reconstruction on the ISPRS benchmark dataset with RMSE of 1.31m and completeness level of 98.9%, respectively. Drešček et al (2020) presented an approach for 3D building reconstruction using an unmanned aerial vehicle (UAV) photogrammetric point cloud based on an extract, transform, load (ETL) solution. A data-driven and algorithmic solution to the automatic reconstruction of 3D buildings at LoD2 from UAV point clouds was presented by Murtiyoso et al (2020).…”

Section: Conventional Methodsmentioning

confidence: 99%

Deep Learning for 3d Building Reconstruction: A Review

Buyukdemircioglu

Kocaman

Kada³

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 3D building reconstruction using Earth Observation (EO) data (aerial and satellite imagery, point clouds, etc.) is an important and active research topic in different fields, such as photogrammetry, remote sensing, computer vision and Geographic Information Systems (GIS). Nowadays 3D city models have become an essential part of 3D GIS environments and they can be used in many applications and analyses in urban areas. The conventional 3D building reconstruction methods depend heavily on the data quality and source; and manual efforts are still needed for generating the object models. Several tasks in photogrammetry and remote sensing have been revolutionized by using deep learning (DL) methods, such as image segmentation, classification, and 3D reconstruction. In this study, we provide a review on the state-of-the-art machine learning and in particular the DL methods for 3D building reconstruction for the purpose of city modelling using EO data. This is the first review with a focus on object model generation based on the DL methods and EO data. A brief overview of the recent building reconstruction studies with DL is also given. We have investigated the different DL architectures, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and the combinations of conventional approaches with DL in this paper and reported their advantages and disadvantages. An outlook on the future developments of 3D building modelling based on DL is also presented.

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Section: Conventional Methodsmentioning

confidence: 99%

Deep Learning for 3d Building Reconstruction: A Review

Buyukdemircioglu

Kocaman

Kada³

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Integration of aerial and ground images [20,21] is beneficial to enhance the surface reconstruction in urban environments. Additional approaches [22][23][24][25][26] have been developed to augment 3D building models. However, most of them focus on building façades and surface optimization via feature matching in urban areas but not in the context of street objects, such as road signs.…”

Section: Oblique Photogrammetry-based Modeling and 3d Scene Augmentationmentioning

confidence: 99%

“…We split the training dataset into two groups by randomly selecting 70% of the ima for training and the remaining 30% for testing and altered the obtained images 24 ) The number of road signs belonging to each category before after data synthesis. For detector training, we implement experiments in the PyTorch framework with the Ubuntu 16.04 operating system.…”

Section: Datamentioning

confidence: 99%

Deep Neural Networks for Road Sign Detection and Embedded Modeling Using Oblique Aerial Images

et al. 2021

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Oblique photogrammetry-based three-dimensional (3D) urban models are widely used for smart cities. In 3D urban models, road signs are small but provide valuable information for navigation. However, due to the problems of sliced shape features, blurred texture and high incline angles, road signs cannot be fully reconstructed in oblique photogrammetry, even with state-of-the-art algorithms. The poor reconstruction of road signs commonly leads to less informative guidance and unsatisfactory visual appearance. In this paper, we present a pipeline for embedding road sign models based on deep convolutional neural networks (CNNs). First, we present an end-to-end balanced-learning framework for small object detection that takes advantage of the region-based CNN and a data synthesis strategy. Second, under the geometric constraints placed by the bounding boxes, we use the scale-invariant feature transform (SIFT) to extract the corresponding points on the road signs. Third, we obtain the coarse location of a single road sign by triangulating the corresponding points and refine the location via outlier removal. Least-squares fitting is then applied to the refined point cloud to fit a plane for orientation prediction. Finally, we replace the road signs with computer-aided design models in the 3D urban scene with the predicted location and orientation. The experimental results show that the proposed method achieves a high mAP in road sign detection and produces visually plausible embedded results, which demonstrates its effectiveness for road sign modeling in oblique photogrammetry-based 3D scene reconstruction.

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“…The images acquired by drones are used to achieve very high levels of detail in 3D models. This use allows the reconstruction of the geometry and the texture of the studied objects (Drešček et al, 2020;Achille et al, 2015;Chen et al, 2019). Drones have become widely used for data acquisition and for 3D reconstruction purposes.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of 3d Building Model Using Terrestrial Laser Scanning and Drone Photogrammetry

Bouziani

Chaaba

Elmir

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The objective of our study is the evaluation of the 3D modeling of buildings and the extraction of structural elements from point clouds obtained using two acquisition techniques (drone and terrestrial laser scanner), as well as the evaluation of the usefulness of their integration. The drone shooting mission was carried using the DJI Phantom 3 Professional and the Sony EXMOR 1/2.3" CMOS RGB camera. For the TLS scanning mission, 9 scanning stations were performed using the FARO Focus S350 laser scanner.To allow the fusion of the two point clouds obtained from drone imagery and TLS, an alignment step is applied. This step was performed using the Iterative Closest Point algorithm. Segmentation was performed using the adapted RANSAC algorithm on point clouds obtained from the drone mission and the TLS mission as well as on the merged point cloud in order to extract structural elements of the building such as windows, doors and stairs. Analysis of the results emphasizes the importance of TLS and drone in 3D modeling. TLS gave better results than the drone in extracting structural elements. This work confirms the importance of complementarity between these two technologies to produce detailed, complete and precise 3D models.

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Spatial ETL for 3D Building Modelling Based on Unmanned Aerial Vehicle Data in Semi-Urban Areas

Cited by 21 publications

References 44 publications

Deep Learning for 3d Building Reconstruction: A Review

Deep Learning for 3d Building Reconstruction: A Review

Deep Neural Networks for Road Sign Detection and Embedded Modeling Using Oblique Aerial Images

Evaluation of 3d Building Model Using Terrestrial Laser Scanning and Drone Photogrammetry

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