Point clouds to direct indoor pedestrian pathfinding

Increase in building complexity can cause difficulties orienting people, especially people with reduced mobility. This work presents a methodology to enable the direct use of indoor point clouds as navigable models for pathfinding. Input point cloud is classified in horizontal and vertical elements according to inclination of each point respect to n neighbour points. Points belonging to the main floor are detected by histogram application. Other floors at different heights and stairs are detected by analysing the proximity to the detected main floor. Then, point cloud regions classified as floor are rasterized to delimit navigable surface and occlusions are corrected by applying morphological operations assuming planarity and taking into account the existence of obstacles. Finally, point cloud of navigable floor is downsampled and structured in a grid. Remaining points are nodes to create navigable indoor graph. The methodology has been tested in two real case studies provided by the ISPRS benchmark on indoor modelling. A pathfinding algorithm is applied to generate routes and to verify the usability of generated graphs. Generated models and routes are coherent with selected motor skills because routes avoid obstacles and can cross areas of non-acquired data. The proposed methodology allows to use point clouds directly as navigation graphs, without an intermediate phase of generating parametric model of surfacesUniversidade de Vigo | Ref. 00VI 131H 641.02Xunta de Galicia | Ref. ED481B 2016/079-0Xunta de Galicia | Ref. ED431C 2016-038Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158-C4-2-RMinisterio de Economía, Industria y Competitividad | Ref. RTC-2016-5257-

Scan planning and route optimization for control of execution of as-designed BIM

Abstract. Scan-to-BIM systems have been recently proposed for the dimensional and quality assessment of as-built construction components with planned works. The procedure is generally based on the geometric alignment and comparison of as-built laser scans with as-designed BIM models. A major concern in Scan-to-BIM procedures is point cloud quality in terms of data completeness and consequently, the scanning process should be designed in order to obtain a full coverage of the scene while avoiding major occlusions. This work proposes a method to optimize the number and scan positions for Scan-to-BIM procedures following stop & go scanning. The method is based on a visibility analysis using a ray-tracing algorithm. In addition, the optimal route between scan positions is formulated as a travelling salesman problem and solved using a suboptimal ant colony optimization algorithm. The distribution of candidate positions follows a grid-based structure, although other distributions based on triangulation or tessellation can be implemented to reduce the number of candidate positions and processing time.Xunta de Galicia | Ref. ED481B 2016/079-0Xunta de Galicia | Ref. ED431C 2016- 038Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158- C4-2-RMinisterio de Economia, Industria y Competitividad | Ref. RTC-2016-5257-

Scan planning optimization for outdoor archaeological sites

The protection and management of archaeological sites require from a deep documentation and analysis, and although hand measuring and documentation is the cheapest way for collecting data, laser scanner has been gradually integrated for the geometrical data capture since point clouds have a high quality in terms of accuracy, precision and resolution. Although acquisition with laser scanner is considered a quick process, scan planning is of high relevance when considering outdoor archaeological sites because of their large size and complexity. In this paper, an automatic methodology to optimize the number and position of scans in order to obtain a point cloud of high quality in terms of data completeness is proposed. The aim of the methodology is to minimize the number of scans, minimizing at the same time the estimated surveying time and the amount of repetitive acquired data. Scan candidates are generated by using a grid-based and a triangulation-based distribution, and results show a faster analysis when triangulation is implemented. The methodology is tested into two real case studies from Italy and Spain, showing the applicability of scan planning in archaeological sitesXunta de Galicia | Ref. ED481B 2016/079-0Xunta de Galicia | Ref. ED431C 2016-038Universidade de Vigo | Ref. 00VI 131H 641.02Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158-C4-2-RMinisterio de Economía, Industria y Competitividad | Ref. RTC-2016-5257-7European Cooperation in Science and Technology (COST) | Ref. CA1520

3D mapping of indoor and outdoor environments using apple smart devices

Recent integration of LiDAR into smartphones opens up a whole new world of possibilities for 3D indoor/outdoor mapping. Although these new systems offer an unprecedent opportunity for the democratization in the use of scanning technology, data quality is lower than data captured from high-end LiDAR sensors. This paper is focused on discussing the capability of recent Apple smart devices for applications related with 3D mapping of indoor and outdoor environments. Indoor scenes are evaluated from a reconstruction perspective, and three geometric aspects (local precision, global correctness, and surface coverage) are considered using data captured in two adjacent rooms. Outdoor environments are analysed from a mobility point of view, and elements defining the physical accessibility in building entrances are considered for evaluation.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. RYC2020-029193-

Analysis of sun glare on roundabouts with aerial laser scanning data

Road geometry and sun glares play an important role concerning road safety. In this research, the direct sunlight in a roundabout sited in Ávila (Spain) is analysed using Aerial Laser Scanning (ALS) point clouds. First, the roundabout is divided in 8 sections, obtaining the driver bearing vectors of the roundabout. Entrances and exits driver bearing vectors of the roundabout are also considered. Then, sun rays are generated for a specific location of the roundabout and in a specific day and time. The incidence of the sun rays with the driver’s vision angle is analysed based on human vision model. Finally, intersections of sun rays with obstacles are calculated utilizing ALS point clouds. ALS data is processed (removing outliers, reducing point density, and computing a Delaunay Triangulation) in order to obtain accurate intersection results with obstacles and optimise the computational time. The method was tested in a roundabout, considering different driver bearings, the slope of the road and the elevation of the terrain. The results show that sun glares are detected at any day and time of the year, therefore areas with risk of direct sun glare within the roundabout are identified. The sun ray’s incidence in the vision angle of the driver is higher during winter solstice, and intersections with obstacles occur mainly during sunrise and sunset. In roundabout vector 7, during winter solstice there is direct sun glare for 7 hours 30 minutes, at the equinoxes for 6 hours 15 minutes and during summer solstice there is no direct sun glare.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. TIN2016-77158 -C4-2-RMinisterio de Ciencia e Innovación | Ref. FJC2018-035550-

A deep learning approach for the recognition of urban ground pavements in historical sites

Urban management is a topic of great interest for local administrators, particularly because it is strongly connected to smart city issues and can have a great impact on making cities more sustainable. In particular, thinking about the management of the physical accessibility of cities, the possibility of automating data collection in urban areas is of great interest. Focusing then on historical centres and urban areas of cities and historical sites, it can be noted that their ground surfaces are generally characterised by the use of a multitude of different pavements. To strengthen the management of such urban areas, a comprehensive mapping of the different pavements can be very useful. In this paper, the survey of a historical city (Sabbioneta, in northern Italy) carried out with a Mobile Mapping System (MMS) was used as a starting point. The approach here presented exploit Deep Learning (DL) to classify the different pavings. Firstly, the points belonging to the ground surfaces of the point cloud were selected and the point cloud was rasterised. Then the raster images were used to perform a material classification using the Deep Learning approach, implementing U-Net coupled with ResNet 18. Five different classes of materials were identified, namely sampietrini, bricks, cobblestone, stone, asphalt. The average accuracy of the result is 94%.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. RYC2020-029193-

Spatial analysis of tree species before forest fires

Spain is included in the top five European countries with the highest number of wildfires. The occurrence and magnitude of forest fires involves aspects of a very diverse nature, from those of a socio-economic, climatic, or physiographic nature, to those concerning fuel or the availability and quantity of resources and means of extinction. The distribution of wildfires in Galicia is not random and that fire occurrence may depend on ownership conflicts also a spatial dependence between productive or non-productive area exists. Satellite data play a major role in providing knowledge about fires by delivering rapid information to map fire-damaged areas precisely and promptly. In addition, the availability of large-scale data and the high temporal resolution offered by the Sentinel-2 satellite enables to classify and determine the land cover changes with high accuracy. This study describes a methodology to detect burned areas and analyse the Land Cover and Land Use (LCLU) classes present in these areas during the period of 5 years (2016–2021) by Sentinel-2 images. The training areas were obtained by photointerpretation and the image classification was performed using the Random Forest algorithm which shows an overall accuracy range between 80–85%. The methodology concluded that Lobios and Muiños were the most affected municipalities by wildfires. Additionally, the spatial analysis determined that the Deciduous Forest mainly composed by Quercus sp. were the most affected in 2017 followed by Coniferous Forest mainly composed by Pinus sp.in 2016. Although, Scrub and Rock are the classes more affected for wildfire during 2016–2020 period.Universidade de Vigo | Ref. 00VI 131H 6410211Agencia Estatal de Investigación | Ref. PCI2020-120705-2Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/0

A discordance analysis in manual labelling of urban mobile laser scanning data used for deep learning based semantic segmentation

Labelled point clouds are crucial to train supervised Deep Learning (DL) methods used for semantic segmentation. The objective of this research is to quantify discordances between the labels made by different people in order to assess whether such discordances can influence the success rates of a DL based semantic segmentation algorithm. An urban point cloud of 30 m road length in Santiago de Compostela (Spain) was labelled two times by ten persons. Discordances and its significance in manual labelling between individuals and rounds were calculated. In addition, a ratio test to signify discordance and concordance was proposed. Results show that most of the points were labelled accordingly with the same class by all the people. However, there were many points that were labelled with two or more classes. Class curb presented 5.9% of discordant points and 3.2 discordances for each point with concordance by all people. In addition, the percentage of significative labelling differences of the class curb was 86.7% comparing all the people in the same round and 100% comparing rounds of each person. Analysing the semantic segmentation results with a DL based algorithm, PointNet++, the percentage of concordance points are related with F-score value in R2 = 0.765, posing that manual labelling has significant impact on results of DL-based semantic segmentation methods.Xunta de Galicia | Ref. ED481B-2019-061Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministère de l’Economie of the G. D. of Luxembourg | Ref. SOLSTICE 2019-05-030-24Universidade de Vigo/CISU

Realistic correction of sky-coloured points in Mobile Laser Scanning point clouds

The enrichment of the point clouds with colour images improves the visualisation of the data as well as the segmentation and recognition processes. Coloured point clouds are becoming increasingly common, however, the colour they display is not always as expected. Errors in the colouring of point clouds acquired with Mobile Laser Scanning are due to perspective in the camera image, different resolution or poor calibration between the LiDAR sensor and the image sensor. The consequences of these errors are noticeable in elements captured in images, but not in point clouds, such as the sky. This paper focuses on the correction of the sky-coloured points, without resorting to the images that were initially used to colour the whole point cloud. The proposed method consists of three stages. First the region of interest where the erroneously coloured points are accumulated, is selected. Second, the sky-coloured points are detected by calculating the colour distance in the Lab colour space to a sample of the sky-colour. And third, the colour of the sky-coloured detected points is restored from the colour of the nearby points. The method is tested in ten real case studies with their corresponding point clouds from urban and rural areas. In two case studies, sky-coloured points were assigned manually and the remaining eight case studies, the sky-coloured points are derived from the acquisition errors. The algorithm for sky-coloured points detection obtained an average F1-score of 94.7%. The results show a correct reassignment of colour, texture, and patterns, while improving the point cloud visualisation.Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGXunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Agencia Estatal de Investigación | Ref. PID2019-108816RB-I0

Multi feature-rich synthetic colour to improve human visual perception of point clouds

Although point features have shown their usefulness in classification with Machine Learning, point cloud visualization enhancement methods focus mainly on lighting. The visualization of point features helps to improve the perception of the 3D environment. This paper proposes Multi Feature-Rich Synthetic Colour (MFRSC) as an alternative non-photorealistic colour approach of natural-coloured point clouds. The method is based on the selection of nine features (reflectance, return number, inclination, depth, height, point density, linearity, planarity, and scattering) associated with five human perception descriptors (edges, texture, shape, size, depth, orientation). The features are reduced to fit the RGB display channels. All feature permutations are analysed according to colour distance with the natural-coloured point cloud and Image Quality Assessment. As a result, the selected feature permutations allow a clear visualization of the scene's rendering objects, highlighting edges, planes, and volumetric objects. MFRSC effectively replaces natural colour, even with less distorted visualization according to BRISQUE, NIQUE and PIQE. In addition, the assignment of features in RGB channels enables the use of MFRSC in software that does not support colorization based on point attributes (most commercially available software). MFRSC can be combined with other non-photorealistic techniques such as Eye-Dome Lighting or Ambient Occlusion.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431F 2022/08Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Universidade de Vigo/CISU