Roof Type Classification Using Deep Convolutional Neural Networks on Low Resolution Photogrammetric Point Clouds From Aerial Imagery

Axelsson, Maria; Söderman, Ulf; Berg, Andreas; Lithen, Thomas

doi:10.1109/icassp.2018.8461740

Cited by 14 publications

(9 citation statements)

References 9 publications

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“…The dataset used by Partovi et al (2017) contains nearly 10,000 training images achieved a precision and recall of only 76%. Axelsson et al (2018) achieved the highest accuracy of 96.65% over two classes flat and ridge where ridge is equivalent to gable roof, these two classes are very distinct, the starting accuracy in Axlesson et al ( 2018) is 50%, much higher than other papers which start training at an accuracy of 12.5-14.3%. Alidoost and Arefi (2016) achieved a very high accuracy using 700 tiles with approximately 100 tiles per class, and it is the only paper where there was no class imbalance.…”

Section: Resultsmentioning

confidence: 86%

“…To tackle an imbalance in classes, both Axelsson et al (2018) and Partovi et al (2017) added more images to the dataset by simply copying the images and augmenting images to artificially inflate the dataset, respectively, this was done prior to training as opposed to during training as done in this paper. The accuracy, precision and recall obtained in this paper are greater than all papers with the exception of Axelsson et al (2018). However, due to the difference in the number of classes, resolution, geographical location and number of images, it is difficult to draw a fair comparison between the studies.…”

Section: Resultsmentioning

confidence: 99%

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Towards open-source LOD2 modelling using convolutional neural networks

Muftah

Rowan

Butler

2021

Model. Earth Syst. Environ.

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The aim of this paper is to classify and segment roofs using vertical aerial imagery to generate three-dimensional (3D) models. Such models can be used, for example, to evaluate the rainfall runoff from properties for rainwater harvesting and in assessing solar energy and roof insulation options. Aerial orthophotos and building footprints are used to extract individual roofs and bounding boxes, which are then fed into one neural network for classification and then another for segmentation. The approach initially implements transfer learning on a pre-trained VGG16 model. The first step achieves an accuracy of 95.39% as well as a F1 score of 95%. The classified images are segmented using a fully convolutional network semantic segmentation model. The mask of the segmented roof planes is used to extract the coordinates of the roof edges and the nexus points using the Harris corner detector algorithm. The coordinates of the corners are then used to plot a 3D Level of Detail 2 (LOD2) representation of the building and the roof height is determined by calculating the maximum and minimum height of a Digital Surface Model LiDAR point cloud and known building height data. Subsequently the wireframe plot can be used to compute the roof area. This model achieved an accuracy of 80.2%, 96.1%, 96.0%, 85.1% and 91.1% for flat, hip, gable, cross-hip and mansard roofs, respectively.

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Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Towards open-source LOD2 modelling using convolutional neural networks

Muftah

Rowan

Butler

2021

Model. Earth Syst. Environ.

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“…Compared to the improvement of DL methods in indoor scenes, the segmentation methods of high LoD buildings' point clouds are still at the initial stage of development. Most existing studies focus on LoD1 (Chen et al, 2014;Zhang, Zhang, 2017;Zhang, 2018;Griffiths, Böhm, 2019;Huang et al, 2019), LoD2 (Hensel, 2019Jarząbek-Rychard, Borkowski, 2016), or one category of building's element (Axelsson et al, 2018).…”

Section: Fully Supervised Methods On 3d Point Cloudsmentioning

confidence: 99%

Label-Efficient Deep Learning-Based Semantic Segmentation of Building Point Clouds at Lod3 Level

Cao

Scaioni

2021

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

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Abstract. In recent research, fully supervised Deep Learning (DL) techniques and large amounts of pointwise labels are employed to train a segmentation network to be applied to buildings’ point clouds. However, fine-labelled buildings’ point clouds are hard to find and manually annotating pointwise labels is time-consuming and expensive. Consequently, the application of fully supervised DL for semantic segmentation of buildings’ point clouds at LoD3 level is severely limited. To address this issue, we propose a novel label-efficient DL network that obtains per-point semantic labels of LoD3 buildings’ point clouds with limited supervision. In general, it consists of two steps. The first step (Autoencoder – AE) is composed of a Dynamic Graph Convolutional Neural Network-based encoder and a folding-based decoder, designed to extract discriminative global and local features from input point clouds by reconstructing them without any label. The second step is semantic segmentation. By supplying a small amount of task-specific supervision, a segmentation network is proposed for semantically segmenting the encoded features acquired from the pre-trained AE. Experimentally, we evaluate our approach based on the ArCH dataset. Compared to the fully supervised DL methods, we find that our model achieved state-of-the-art results on the unseen scenes, with only 10% of labelled training data from fully supervised methods as input.

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“…The authors used high-level features extracted from the last layer of a fine-tuned CNN as inputs to a second-stage SVM classifier. A similar strategy was proposed by Axelsson et al [24], who classified the patches of buildings from RGB aerial images into the two most common roof types, i.e., slope roofs and flat roofs, using transfer learning of a pre-trained CNN. Although patch-based classification with CNNs does not require feature pre-definition anymore, it needs an additional special training data preparation and, in most cases, does not contain the whole building as one element, but only parts of it.…”

Section: Pixel-wise Image Classificationmentioning

confidence: 99%

Multi-Task cGAN for Simultaneous Spaceborne DSM Refinement and Roof-Type Classification

et al. 2019

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Various deep learning applications benefit from multi-task learning with multiple regression and classification objectives by taking advantage of the similarities between individual tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models compared to separately trained models. In this paper, we make an observation of such influences for important remote sensing applications like elevation model generation and semantic segmentation tasks from the stereo half-meter resolution satellite digital surface models (DSMs). Mainly, we aim to generate good-quality DSMs with complete, as well as accurate level of detail (LoD)2-like building forms and to assign an object class label to each pixel in the DSMs. For the label assignment task, we select the roof type classification problem to distinguish between flat, non-flat, and background pixels. To realize those tasks, we train a conditional generative adversarial network (cGAN) with an objective function based on least squares residuals and an auxiliary term based on normal vectors for further roof surface refinement. Besides, we investigate recently published deep learning architectures for both tasks and develop the final end-to-end network, which combines different models, as using them first separately, they provide the best results for their individual tasks.which the object belongs. Mainly, building footprint extraction or roof type classification is one of the most challenging, but important problems. It is common to use DSMs as input data for classification tasks regarding buildings [6,7], as depth information provides geometrical silhouettes and allows a better understanding of building forms. Although a vast amount of attempts have already been made on accurate pixel-wise classification [8,9], it still remains a challenging task in practice due to the wide variety of building appearances.In most cases, each task, e.g., depth image generation and pixel-wise image classification, is tackled independently, although they are closely connected. Solving those multiple tasks jointly can enhance the performance of each independent task, as well as speed up computation time. This observation leads to the advantages of multi-task (MT) learning. The approach of simultaneously improving the generalization performance of multiple outputs from a single input was applied to numerous machine learning techniques. As a promising concept for convolutional neural networks (CNNs), MT learning has been proven to leverage a variety of problems successfully, like classification and semantic segmentation [10] or classification and object detection [11]. Due to the fact that different tasks may conflict, MT learning is regarded as the optimization of MT loss, which minimizes a linear combination of contributed single-task loss functions.In this work, we aim to produce good-quality LoD2-like DSMs with realistic building geometries together with dense pixel-wise rooftop classification masks, defining multiple classes, like ground, flat, and ...

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Roof Type Classification Using Deep Convolutional Neural Networks on Low Resolution Photogrammetric Point Clouds From Aerial Imagery

Cited by 14 publications

References 9 publications

Towards open-source LOD2 modelling using convolutional neural networks

Towards open-source LOD2 modelling using convolutional neural networks

Label-Efficient Deep Learning-Based Semantic Segmentation of Building Point Clouds at Lod3 Level

Multi-Task cGAN for Simultaneous Spaceborne DSM Refinement and Roof-Type Classification

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