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

Challenges for Updating 3D Cadastral Objects using LiDAR and Image-based Point Clouds

Nowadays due to the increasing complex and multifunctional building environment in the urban areas it is required an accurate geometry and proper legal registration of the cadastral objects including third dimension and time aspect. 2D land-parcel data seems insufficient to address the variety of problems in high density residential areas. This fact motivates scientists worldwide to work on 3D Cadastral Data models for representation of 3D legal and physical information. Third dimension is important in cases of space subdivision with different owners and used for various purposes which requires its accurate registration. However, it is of great importance to maintain the 3D information up to date. With the rapid development in the fields of photogrammetry, laser scanning and computer vision high accurate 3D data can be obtained. However, numerous challenges appear while processing, storing, transferring and visualizing. Currently, efficient management of “big data” is widely discussed. In this respect developed algorithms in support of automatization of data processing, segmentation and visualization can be very helpful. Current paper focuses on usage of photogrammetric data for updating 3D information. More specifically, we investigate the opportunities for updating 3D cadastral objects using precise multi epoch airborne laser scanning 3D data, point clouds derived from high resolution imagery from dense matching algorithms and maps used to provide semantic information about the land cover class and 2D special information of the boundary of the cadastral objects. In the paper we describe the type and size of uncertainties when updating 3D cadastral models. This includes the uncertainty of the initial model, caused by inaccuracies in the measurements when building the initial models. Next, a careful registration with the newly acquired dataset is necessary in order to better describe changes of objects, instead of changes in datasets. The benefits of the fourth dimension in cadastral information systems are also discussed in the paper. Different methods for detecting changes in time using airborne laser scanning (ALS) data have been used for various application such as map updating (Vosselman, et al, 2004), evaluation of damages as a result from a physical disasters (Murakami et al, 1999) etc. Usually change detection is done by segmentation, classification or implementation of specific mapping rules. In our paper we focus on detecting changes while comparing ALS dataset from different epochs and between point clouds obtained from ALS and high resolution images for same territory. We also discuss the difficulties in detecting changes for different types of 3D cadastral objects. The analysis is done for a common dataset located in Netherlands. In conclusion the opportunities of using high accurate point cloud data for keeping up to date 3D cadastral systems are presented and the challenges and problems are shown

Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

The difficulty associated with the Lidar data change detection method is lack of data, which is mainly caused by occlusion or pulse absorption by the surface material, e.g., water. To address this challenge, we present a new strategy for detecting buildings that are “changed”, “unchanged”, or “unknown”, and quantifying the changes. The designation “unknown” is applied to locations where, due to lack of data in at least one of the epochs, it is not possible to reliably detect changes in the structure. The process starts with classified data sets in which buildings are extracted. Next, a point-to-plane surface difference map is generated by merging and comparing the two data sets. Context rules are applied to the difference map to distinguish between “changed”, “unchanged”, and “unknown”. Rules are defined to solve problems caused by the lack of data. Further, points labelled as “changed” are re-classified into changes to roofs, walls, dormers, cars, constructions above the roof line, and undefined objects. Next, all the classified changes are organized as changed building objects, and the geometric indices are calculated from their 3D minimum bounding boxes. Performance analysis showed that 80%–90% of real changes are found, of which approximately 50% are considered relevant

Pole-Like Road Furniture Detection and Decomposition in Mobile Laser Scanning Data Based on Spatial Relations

Road furniture plays an important role in road safety. To enhance road safety, policies that encourage the road furniture inventory are prevalent in many countries. Such an inventory can be remarkably facilitated by the automatic recognition of road furniture. Current studies typically detect and classify road furniture as one single above-ground component only, which is inadequate for road furniture with multiple functions such as a streetlight with a traffic sign attached. Due to the recent developments in mobile laser scanners, more accurate data is available that allows for the segmentation of road furniture at a detailed level. In this paper, we propose an automatic framework to decompose road furniture into different components based on their spatial relations in a three-step procedure: first, pole-like road furniture are initially detected by removing ground points and an initial classification. Then, the road furniture is decomposed into poles and attachments. The result of the decomposition is taken as a feedback to remove spurious pole-like road furniture as a third step. If there are no poles extracted in the decomposition stage, these incorrectly detected pole-like road furniture—such as the pillars of buildings—will be removed from the detection list. We further propose a method to evaluate the results of the decomposition. Compared with our previous work, the performance of decomposition has been much improved. In our test sites, the correctness of detection is higher than 90% and the completeness is approximately 95%, showing that our procedure is competitive to state of the art methods in the field of pole-like road furniture detection. Compared to our previous work, the optimized decomposition improves the correctness by 7.3% and 18.4% in the respective test areas. In conclusion, we demonstrate that our method decomposes pole-like road furniture into poles and attachments with respect to their spatial relations, which is crucial for road furniture interpretation

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