Change detection is an important step for the characterization of object dynamics at the earth’s surface. In multi-temporal point clouds, the main challenge is to detect true changes at different granularities in a scene subject to significant noise and occlusion. To better understand new research perspectives in this field, a deep review of recent advances in 3D change detection methods is needed. To this end, we present a comprehensive review of the state of the art of 3D change detection approaches, mainly those using 3D point clouds. We review standard methods and recent advances in the use of machine and deep learning for change detection. In addition, the paper presents a summary of 3D point cloud benchmark datasets from different sensors (aerial, mobile, and static), together with associated information. We also investigate representative evaluation metrics for this task. To finish, we present open questions and research perspectives. By reviewing the relevant papers in the field, we highlight the potential of bi- and multi-temporal point clouds for better monitoring analysis for various applications.

With rapid population growth, there is an increasing demand for vertical use of space. The wide spread of complex and high-rise buildings, as well as the increasing number of infrastructure above or underground, requires new methods for efficient management of land property. 3D cadastre has, thus, become a necessity for land administration. However, the success of 3D cadastral systems relies on the definition of legal and institutional frameworks and requires implementing performant technical solutions. The potential of BIM and 3D GIS in this field has been demonstrated by several authors. However, cadastral development is strongly related to the national context of each country in terms of laws, institutions, etc. In this paper, an integrated approach based on BIM and 3D GIS for the implementation of a 3D cadastre in Morocco is presented. This approach demonstrates the relevance of such integration for the efficient management of cadastral information. First, a Conceptual Data Model (CDM) based on an extension of CityGML, was proposed for the management of cadastral information in Morocco. Then, a BIM modeling process was developed according to the model’s specifications and then translated to CityGML format. After that, a 3D Geodatabase was implemented in ArcGIS based on the proposed CDM. Our method was applied to a case of co-ownership building, showing several difficulties and limits in terms of 2D representation. The results show several advantages in terms of representation and management of 3D cadastral objects. In addition, some improvements are proposed to extend the concept of co-owner share to a volumetric calculation.

Augmented reality (AR) is a relevant technology, which has demonstrated to be efficient for several applications, especially in the architecture, engineering, construction and operation (AECO) domain, where the integration of building information modeling (BIM) and AR has proved to be optimal in handling construction projects. However, the main challenge when integrating a virtual 3D model in an AR environment is the lack of precision and accuracy of placement that can occur between the real and the virtual environments. Although methods for placement via AR have been reported in the literature, there is a lack of investigations addressing their evaluation. Therefore, this paper proposes a methodology to perform a quantitative and qualitative assessment of several AR placement methods and a discussion about their usability in the specific context of AECO. We adopt root mean square error (RMSE) to quantify the placement accuracy of a 3D model and standard deviation to examine its stability (jittering). The results revealed that the AR placement error range is extremely wide (from a few centimeters up to meters). In marker-based methods, the results showed centimeter-range in both indoor and outdoor environments, compared to other methods (Inertial, Marker-less, etc.), while marker-less methods have widely varying error range from centimeters to a few meters. Other commercial solutions based on placement-sensors (GNSS and IMU), such as Trimble SiteVision, have proven placement performance in manual mode with centimeter order, while for the automatic mode, the order of placement and stability is metric, due to the low coverage of RTX (real time extended) in the study area.

Railway scene understanding is crucial for various applications, including autonomous trains, digital twining, and infrastructure change monitoring. However, the development of the latter is constrained by the lack of annotated datasets and limitations of existing algorithms. To address this challenge, we present Rail3D, the first comprehensive dataset for semantic segmentation in railway environments with a comparative analysis. Rail3D encompasses three distinct railway contexts from Hungary, France, and Belgium, capturing a wide range of railway assets and conditions. With over 288 million annotated points, Rail3D surpasses existing datasets in size and diversity, enabling the training of generalizable machine learning models. We conducted a generic classification with nine universal classes (Ground, Vegetation, Rail, Poles, Wires, Signals, Fence, Installation, and Building) and evaluated the performance of three state-of-the-art models: KPConv (Kernel Point Convolution), LightGBM, and Random Forest. The best performing model, a fine-tuned KPConv, achieved a mean Intersection over Union (mIoU) of 86%. While the LightGBM-based method achieved a mIoU of 71%, outperforming Random Forest. This study will benefit infrastructure experts and railway researchers by providing a comprehensive dataset and benchmarks for 3D semantic segmentation. The data and code are publicly available for France and Hungary, with continuous updates based on user feedback.

Three-dimensional digital models play a pivotal role in city planning, monitoring, and sustainable management of smart and Digital Twin Cities (DTCs). In this context, semantic segmentation of airborne 3D point clouds is crucial for modeling, simulating, and understanding large-scale urban environments. Previous research studies have demonstrated that the performance of 3D semantic segmentation can be improved by fusing 3D point clouds and other data sources. In this paper, a new prior-level fusion approach is proposed for semantic segmentation of large-scale urban areas using optical images and point clouds. The proposed approach uses image classification obtained by the Maximum Likelihood Classifier as the prior knowledge for 3D semantic segmentation. Afterwards, the raster values from classified images are assigned to Lidar point clouds at the data preparation step. Finally, an advanced Deep Learning model (RandLaNet) is adopted to perform the 3D semantic segmentation. The results show that the proposed approach provides good results in terms of both evaluation metrics and visual examination with a higher Intersection over Union (96%) on the created dataset, compared with (92%) for the non-fusion approach.