Intelligent open data 3D maps in a collaborative virtual world

Three-dimensional (3D) maps have many potential applications, such as navigation and urban planning. In this article, we present the use of a 3D virtual world platform Meshmoon to create intelligent open data 3D maps. A processing method is developed to enable the generation of 3D virtual environments from the open data of the National Land Survey of Finland. The article combines the elements needed in contemporary smart city concepts, such as the connection between attribute information and 3D objects, and the creation of collaborative virtual worlds from open data. By using our 3D virtual world platform, it is possible to create up-to-date, collaborative 3D virtual models, which are automatically updated on all viewers. In the scenes, all users are able to interact with the model, and with each other. With the developed processing methods, the creation of virtual world scenes was partially automated for collaboration activities.Peer reviewe

Automated 3D scene reconstruction from open geospatial data sources: airborne laser scanning and a 2D topographic database

Open geospatial data sources provide opportunities for low cost 3D scene reconstruction. In this study, based on a sparse airborne laser scanning (ALS) point cloud (0.8 points/m2) obtained from open source databases, a building reconstruction pipeline for CAD building models was developed. The pipeline includes voxel-based roof patch segmentation, extraction of the key-points representing the roof patch outline, step edge identification and adjustment, and CAD building model generation. The advantages of our method lie in generating CAD building models without the step of enforcing the edges to be parallel or building regularization. Furthermore, although it has been challenging to use sparse datasets for 3D building reconstruction, our result demonstrates the great potential in such applications. In this paper, we also investigated the applicability of open geospatial datasets for 3D road detection and reconstruction. Road central lines were acquired from an open source 2D topographic database. ALS data were utilized to obtain the height and width of the road. A constrained search method (CSM) was developed for road width detection. The CSM method was conducted by splitting a given road into patches according to height and direction criteria. The road edges were detected patch by patch. The road width was determined by the average distance from the edge points to the central line. As a result, 3D roads were reconstructed from ALS and a topographic database

Comparison of area-based and individual tree-based methods for predicting plot-level forest attributes

Peer reviewe

THE FUSION OF INDIVIDUAL TREE DETECTION AND VISUAL INTERPRETATION IN ASSESSMENT OF FOREST VARIABLES FROM LASER POINT CLOUDS

In this study we searched the obtainable accuracy of forest inventory based on the individual tree detection (ITD) by using fusion of automatic ITD (ITDauto) and visual interpretation of laser point clouds. Current ITD algorithms, mostly based on segmentation of canopy height models (CHMs), are not able to utilize the whole information included in three-dimensional point clouds. We hypothesized that visual interpretation of the point cloud could provide so-called "best case" tree detection that could be achievable automatically. We refer to this method consisting of ITDauto and visual interpretation as ITDvisual. We assessed the plot level accuracies of the ITDauto and ITDvisual in boreal managed forest conditions using 322 plots. Based on the results the accuracy of ITD can be improved with visual interpretation. Omission trees are mainly missing from both ITD-methods. ITDvisual produced more accurate estimates for all forest variables compared to ITDauto, e.g. RMSE% in volume decreased from 33.3% to 27.8% and bias% in volume from 4.1% to 2.3%. Area-based approach (ABA) is becoming more general for operational forest inventories with sparser laser data. ITDvisual would be justified if it could replace expensive field work in plot-wise measurements needed for ABA. Further research is needed in the use of ITD results as a reference for ABA

Matching persistent scatterers to buildings

Persistent Scatterer Interferometry (PSI) is by now a mature technique for the estimation of surface deformation in urban areas. In contrast to the classical interferometry a stack of interferograms is used to minimize the influence of atmospheric disturbances and to select a set of temporarily stable radar targets, the so called Persistent Scatterers (PS). As a result the deformation time series and the height for all identified PS are obtained with high accuracy. The achievable PS density depends thereby on the characteristics of the scene at hand and on the spatial resolution of the used SAR data. This means especially that the location of PS cannot be chosen by the operator and consequently deformation processes of interest may be spatially undersampled and not retrievable from the data. In case of the newly available high resolution SAR data, offering a ground resolution around one metre, the sampling is potentially dense enough to enable a monitoring of single buildings. However, the number of PS to be found on a single building highly depends on its orientation to the viewing direction of the sensor, its facade and roof structure, and also the surrounding buildings. It is thus of major importance to assess the PS density for the buildings in a scene for real world monitoring scenarios. Besides that it is interesting from a scientific point of view to investigate the factors influencing the PS density. In this work, we fuse building outlines (i.e. 2D GIS data) with a geocoded PS point cloud, which consists mainly in estimating and removing a shift between both datasets. After alignment of both datasets, the PS are assigned to buildings, which is in turn used to determine the PS density per building. The resulting map is a helpful tool to investigate the factors influencing PS density at buildings

Evaluation of a smartphone app for forest sample plot measurements

We evaluated a smartphone app (TRESTIMA(TM)) for forest sample plot measurements. The app interprets imagery collected from the sample plots using the camera in the smartphone and then estimates forest inventory attributes, including species-specific basal areas (G) as well as the diameter (D-gM) and height (H-gM) of basal area median trees. The estimates from the smartphone app were compared to forest inventory attributes derived from tree-wise measurements using calipers and a Vertex height measurement device. The data consist of 2169 measured trees from 25 sample plots (32 m x 32 m), dominated by Scots pine and Norway spruce from southern Finland. The root-mean-square errors (RMSEs) in the basal area varied from 19.7% to 29.3% and the biases from 11.4% to 18.4% depending on the number of images per sample plot and image shooting location. D-gM measurement bias varied from -1.4% to 3.1% and RMSE from 5.2% to 11.6% depending on the tree species. Respectively, H-gM bias varied from 5.0% to 8.3% and RMSE 10.0% to 13.6%. In general, four images captured toward the center of the plot provided more accurate results than four images captured away from the plot center. Increasing the number of captured images per plot to the analyses yielded only marginal improvement to the results.Peer reviewe

The potential of dual-wavelength terrestrial lidar in early detection of Ips typographus (L.) infestation – Leaf water content as a proxy

Climate change is causing novel forest stress around the world due to changes in environmental conditions. Forest pest insects, such as Ips typographus (L.), are spreading toward the northern latitudes and are now able to produce more generations in their current range; this has increased forest disturbances. Timely information on tree decline is critical in allowing forest managers to plan effective countermeasures and to forecast potential infestation areas. Field-based infestation surveys of bark beetles have traditionally involved visual estimates of entrance holes, resin flow, and maternal-gallery densities; such estimates are prone to error and bias. Thus, objective and automated methods for estimating tree infestation status are required.In this study, we investigated the feasibility of dual-wavelength terrestrial lidar in the estimation and detection of I. typographus infestation symptoms. In addition, we examined the relationship between leaf water content (measured as gravimetric water content and equivalent water thickness) and infestation severity. Using two terrestrial lidar systems (operating at 905 nm and 1550 nm), we measured 29 mature Norway spruce (Picea abies [L.] Karst.) trees that exhibited low or moderate infestation symptoms. We calculated single and dual-wavelength lidar intensity metrics from stem and crown points to test these metrics' ability to discriminate I. typographus infestation levels using regressions and linear discriminant analyses.Across the various I. typographus infestation levels, we found significant differences (p </p