Automatic modelling of 3D trees using aerial LIDAR point cloud data and deep learning

3D tree objects can be used in various applications, like estimation of physiological equivalent temperature (PET). During this project, a method is designed to extract 3D tree objects from a country-wide point cloud. To apply this method on large scale, the algorithm needs to be efficient. Extraction of trees is done in two steps: point-wise classification using the PointNet deep learning network, and Watershed segmentation to split points into individual trees. After that, 3D tree models are made. The method is evaluated on 3 areas, a park, city center and housing block in the city of Deventer, the Netherlands. This resulted into an average accuracy of 92% and a F1-score of 0.96

UAVPal:A New Dataset for Semantic Segmentation in Complex Urban Landscape with Efficient Multiscale Segmentation

Semantic segmentation has recently emerged as a prominent area of interest in Earth observation. Several semantic segmentation datasets already exist, facilitating comparisons among different methods in complex urban scenes. However, most open high-resolution urban datasets are geographically skewed toward Europe and North America, while coverage of Southeast Asia is very limited. The considerable variation in city designs worldwide presents an obstacle to the applicability of computer vision models, especially when the training dataset lacks significant diversity. On the other hand, naively applying computationally expensive models leads to inefficacies and sometimes poor performance. To tackle the lack of data diversity, we introduce a new UAVPal dataset of complex urban scenes from the city of Bhopal, India. We complement this by introducing a novel dense predictor head and demonstrate that a well-designed head can efficiently take advantage of the multiscale features to enhance the benefits of a strong feature extractor backbone. We design our segmentation head to learn the importance of features at various scales for each individual class and refine the final dense prediction accordingly. We tested our proposed head with a state-of-the-art backbone on multiple UAV datasets and a high-resolution satellite image dataset for LULC classification. We observed improved intersection over union (IoU) in various classes and up to 2$\%$ better mean IoU. Apart from the performance improvements, we also observed nearly 50$\%$ reduction in computing operations required when using the proposed head compared to the traditional segmentation head.</p

An Automatic Procedure For Mobile Laser Scanning Platform 6DOF Trajectory Adjustment

In this paper, a method is presented to improve the MLS platform’s trajectory for GNSS denied areas. The method comprises two major steps. The first step is based on a 2D image registration technique described in our previous publication. Internally, this registration technique first performs aerial to aerial image matching, this issues correspondences which enable to compute the 3D tie points by multiview triangulation. Similarly, it registers the rasterized Mobile Laser Scanning Point Cloud (MLSPC) patches with the multiple related aerial image patches. The later registration provides the correspondence between the aerial to aerial tie points and the MLSPC’s 3D points. In the second step, which is described in this paper, a procedure utilizes three kinds of observations to improve the MLS platform’s trajectory. The first type of observation is the set of 3D tie points computed automatically in the previous step (and are already available), the second type of observation is based on IMU readings and the third type of observation is soft-constraint over related pose parameters. In this situation, the 3D tie points are considered accurate and precise observations, since they provide both locally and globally strict constraints, whereas the IMU observations and soft-constraints only provide locally precise constraints. For 6DOF trajectory representation, first, the pose [R, t] parameters are converted to 6 B-spline functions over time. Then for the trajectory adjustment, the coefficients of B-splines are updated from the established observations. We tested our method on an MLS data set acquired at a test area in Rotterdam, and verified the trajectory improvement by evaluation with independently and manually measured GCPs. After the adjustment, the trajectory has achieved the accuracy of RMSE X&thinsp;=&thinsp;9&thinsp;cm, Y&thinsp;=&thinsp;14&thinsp;cm and Z&thinsp;=&thinsp;14&thinsp;cm. Analysing the error in the updated trajectory suggests that our procedure is effective at adjusting the 6DOF trajectory and to regenerate a reliable MLSPC product

Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory

State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases

Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80% of the doors and 85% of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scene