Fully Automated Segmentation of 2D and 3D Mobile Mapping Data for Reliable Modeling of Surface Structures Using Deep Learning

Reiterer, Alexander; Wäschle, Katharina; Störk, Dominik; Leydecker, Achim; Gitzen, Niko

doi:10.3390/rs12162530

Cited by 16 publications

(10 citation statements)

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“…More precisely, the influence of the initial (manual) segmentation of a point cloud is investigated. Thus, the model also provides the basis for describing an automatic (e.g., ML-based) semantic segmentation process, as considered in many works, such as [91][92][93][94]. For the evaluative use, the suitability of a point cloud for an application should be assessed.…”

Section: Ssr =mentioning

confidence: 99%

Evaluating the Quality of Semantic Segmented 3D Point Clouds

Barnefske

Sternberg

2022

Remote Sensing

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Recently, 3D point clouds have become a quasi-standard for digitization. Point cloud processing remains a challenge due to the complex and unstructured nature of point clouds. Currently, most automatic point cloud segmentation methods are data-based and gain knowledge from manually segmented ground truth (GT) point clouds. The creation of GT point clouds by capturing data with an optical sensor and then performing a manual or semi-automatic segmentation is a less studied research field. Usually, GT point clouds are semantically segmented only once and considered to be free of semantic errors. In this work, it is shown that this assumption has no overall validity if the reality is to be represented by a semantic point cloud. Our quality model has been developed to describe and evaluate semantic GT point clouds and their manual creation processes. It is applied on our dataset and publicly available point cloud datasets. Furthermore, we believe that this quality model contributes to the objective evaluation and comparability of data-based segmentation algorithms.

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Section: Ssr =mentioning

confidence: 99%

Evaluating the Quality of Semantic Segmented 3D Point Clouds

Barnefske

Sternberg

2022

Remote Sensing

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“…This study's deep learning workflow was based on a multi-view approach originally developed for the classification of urban surface textures [31]. Its key component was a convolutional neural network (CNN) for image segmentation trained in a supervised fashion on a large set of mobile mapping images.…”

Section: Deep Learning Workflowmentioning

confidence: 99%

Object Semantic Segmentation in Point Clouds—Comparison of a Deep Learning and a Knowledge-Based Method

Ponciano

Roetner

Reiterer

et al. 2021

IJGI

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Through the power of new sensing technologies, we are increasingly digitizing the real world. However, instruments produce unstructured data, mainly in the form of point clouds for 3D data and images for 2D data. Nevertheless, many applications (such as navigation, survey, infrastructure analysis) need structured data containing objects and their geometry. Various computer vision approaches have thus been developed to structure the data and identify objects therein. They can be separated into model-driven, data-driven, and knowledge-based approaches. Model-driven approaches mainly use the information on the objects contained in the data and are thus limited to objects and context. Among data-driven approaches, we increasingly find deep learning strategies because of their autonomy in detecting objects. They identify reliable patterns in the data and connect these to the object of interest. Deep learning approaches have to learn these patterns in a training stage. Knowledge-based approaches use characteristic knowledge from different domains allowing the detection and classification of objects. The knowledge must be formalized and substitutes the training for deep learning. Semantic web technologies allow the management of such human knowledge. Deep learning and knowledge-based approaches have already shown good results for semantic segmentation in various examples. The common goal but the different strategies of the two approaches engaged our interest in doing a comparison to get an idea of their strengths and weaknesses. To fill this knowledge gap, we applied two implementations of such approaches to a mobile mapping point cloud. The detected object categories are car, bush, tree, ground, streetlight and building. The deep learning approach uses a convolutional neural network, whereas the knowledge-based approach uses standard semantic web technologies such as SPARQL and OWL2to guide the data processing and the subsequent classification as well. The LiDAR point cloud used was acquired by a mobile mapping system in an urban environment and presents various complex scenes, allowing us to show the advantages and disadvantages of these two types of approaches. The deep learning and knowledge-based approaches produce a semantic segmentation with an average F1 score of 0.66 and 0.78, respectively. Further details are given by analyzing individual object categories allowing us to characterize specific properties of both types of approaches.

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“…An overview of sensor platforms for remote sensing can be found in [8]. Vehiclebased, multi-sensor measurement systems of research institutions are described in [9] and [10], to name just two examples. Prominent examples of commercial measurement vehicles are the ones used by data companies like Alphabet or Here to collect data for their geodata or map services.…”

Section: Related Workmentioning

confidence: 99%