Trends in Geoinformatics Education

Trends in Geoinformatics Educatio

Comprehensive approach for building outline extraction from LiDAR data with accent to a sparse laser scanning point cloud

The method of building outline extraction based on segmentation of airborne laser scanning data is proposed and tested on a dataset comprising 1,400 buildings typical for residential and industrial urban areas. The algorithm starts with setting a special threshold to separate building from bare earth points and low objects. Next, local planes are fitted to each point using RANSAC and further refined by least squares adjustment. A normal vector is assigned to each point. Similarities among normal vectors are evaluated in order to assemble planar or curved roof segments. Finally, building outlines are formed from detected segments using the a-shapes algorithm and further regularized. The extracted outlines were compared with reference polygons manually derived from the processed laser scanning point cloud and orthoimages. Area-based evaluation of accuracy of the proposed method revealed completeness and correctness of 87 % and 97 %, respectively, for the test dataset. The influence of parameters like number of points per roof segment, complexity of the roof structure, roof type, and overlap with vegetation on accuracy was evaluated and discussed

Utilization of BEAM and NEST open source toolboxes in education and research

European Space Agency (ESA) provides several open source toolboxes for visualization, processing and analyzing satellite images acquired both in optical and microwave domains. Basic ERS & Envisat (A)ATSR and MERIS Toolbox (BEAM) was originally developed for easier handling ENVISAT optical data. Today this toolbox supports several raster data formats and datasets collected with other EO instruments such as MODIS, AVHRR, CHRIS/Proba. The NEXT ESA SAR Toolbox (NEST) has been created for processing radar data acquired from different satellites such as ERS 1&2, ENVISAT, RADARSAT or TerraSAR X. Both toolboxes are suitable for the education of the basic principles of data processing (geometric and radiometric corrections, classification, filtering of radar data) but also for research. Possibilities for utilization of these toolboxes in remote sensing courses based on two examples of practical exercises are described. Use of the NEST toolbox is demonstrated on a research project dealing with snow cover detection from SAR imagery

The use of UAV in cadastral mapping of the Czech Republic

The main challenge in the renewal and updating of the Cadastre of Real Estate of the Czech Republic is to achieve maximum efficiency but to retain the required accuracy of all points in the register. The paper discusses the possibility of using UAV photogrammetry and laser scanning for cadastral mapping in the Czech Republic. Point clouds from images and laser scans together with orthoimages were derived over twelve test areas. Control and check points were measured using geodetic methods (RTK-GNSS and total stations). The accuracy of the detailed survey based on UAV technologies was checked on hundreds of points, mainly building corners and fence foundations. The results show that the required accuracy of 0.14 m was achieved on more than 80% and 98% of points in the case of the image point clouds and orthoimages and the case of the LiDAR point cloud, respectively. Nevertheless, the methods lack completeness of the performed survey that must be supplied by geodetic measurements. The paper also provides a comparison of the costs connected to traditional and UAV-based cadastral mapping, and it addresses the necessary changes in the organisational and technological processes in order to utilise the UAV based technologies.Web of Science106art. no. 38

E-learningový kurz Staré mapy-digitalizace, katalogizace, kartometrické analýzy =E-learning Course: Old Maps-Digitizing, Cataloguing and Cartometric Analysis

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Comparison of Reflectance Measurements Acquired with a Contact Probe and an Integration Sphere: Implications for the Spectral Properties of Vegetation at a Leaf Level

Laboratory spectroscopy in visible and infrared regions is an important tool for studies dealing with plant ecophysiology and early recognition of plant stress due to changing environmental conditions. Leaf optical properties are typically acquired with a spectroradiometer coupled with an integration sphere (IS) in a laboratory or with a contact probe (CP), which has the advantage of operating flexibility and the provision of repetitive in-situ reflectance measurements. Experiments comparing reflectance spectra measured with different devices and device settings are rarely reported in literature. Thus, in our study we focused on a comparison of spectra collected with two ISs on identical samples ranging from a Spectralon and coloured papers as reference standards to vegetation samples with broadleaved (Nicotiana Rustica L.) and coniferous (Picea abies L. Karst.) leaf types. First, statistical measures such as mean absolute difference, median of differences, standard deviation and paired-sample t-test were applied in order to evaluate differences between collected reflectance values. The possibility of linear transformation between spectra was also tested. Moreover, correlation between normalised differential indexes (NDI) derived for each device and all combinations of wavelengths between 450 nm and 1800 nm were assessed. Finally, relationships between laboratory measured leaf compounds (total chlorophyll, carotenoids and water content), NDI and selected spectral indices often used in remote sensing were studied. The results showed differences between spectra acquired with different devices. While differences were negligible in the case of the Spectralon and they were possible to be modelled with a linear transformation in the case of coloured papers, the spectra collected with the CP and the ISs differed significantly in the case of vegetation samples. Regarding the spectral indices calculated from the reflectance data collected with the three devices, their mean values were in the range of the corresponding standard deviations in the case of broadleaved leaf type. Larger differences in optical leaf properties of spruce needles collected with the CP and ISs are implicated from the different measurement procedure due to needle-like leaf where shoots with spatially oriented needles were measured with the CP and individual needles with the IS. The study shows that a direct comparison between the spectra collected with two devices is not advisable as spectrally dependent offsets may likely exist. We propose that the future studies shall focus on standardisation of measurement procedures so that open access spectral libraries could serve as a reliable input for modelling of optical properties on a leaf level

České vysoké učení technické v Praze Fakulta stavební

Katedra mapování a kartografie Image matching and its applications in photogrammetr

Roof type determination from a sparse laser scanning point cloud

A method for determining a roof coverage type and a building height from a sparse laser scanning point cloud was introduced in Hofman (2008). This model driven approach utilizes 2D building outlines from the Digital Cadastral Map (DCM) and orthoimages in addition to an airborne laser scanning point cloud. Its results were unsatisfactory, since the determination of roof types was not reliable. Thus, a practical application of this method was not possible. While searching possibilities for its improvement, it was discovered that derivation of roof edges from orthoimages was the weakest point in the workflow. A new model driven method is presented, which is suitable for buildings with a rectangular plot. Based on four predefined roof types, a subset of a point cloud is divided into several groups corresponding to roof planes. The best fitting planes are found by means of least squares adjustment with an iterative exclusion of outliers. The most probable roof type is selected by an evaluation of a number of points excluded from the calculation. Results of this new approach were applied on datasets from three test sites (Brno, Sobotka and Pardubice- Polabiny) which are presented. in spite of a very low density of laser points (1.5 and 0.25 points per m2) the method reveals very good results. The success rate of correctly determined roof cover types is 91% and 80% for the point cloud density of 1.5 and 0.25 points per m2, respectively.353

Utilization of BEAM and NEST open source toolboxes in education and research

European Space Agency (ESA) provides several open source toolboxes for visualization, processing and analyzing satellite images acquired both in optical and microwave domains. Basic ERS & Envisat (A)ATSR and MERIS Toolbox (BEAM) was originally developed for easier handling ENVISAT optical data. Today this toolbox supports several raster data formats and datasets collected with other EO instruments such as MODIS, AVHRR, CHRIS/Proba. The NEXT ESA SAR Toolbox (NEST) has been created for processing radar data acquired from different satellites such as ERS 1&2, ENVISAT, RADARSAT or TerraSAR X. Both toolboxes are suitable for the education of the basic principles of data processing (geometric and radiometric corrections, classification, filtering of radar data) but also for research. Possibilities for utilization of these toolboxes in remote sensing courses based on two examples of practical exercises are described. Use of the NEST toolbox is demonstrated on a research project dealing with snow cover detection from SAR imagery

Roof type determination from a sparse laser scanning point cloud

A method for determining a roof coverage type and a building height from a sparse laser scanning point cloud was introduced in Hofman (2008). This model driven approach utilizes 2D building outlines from the Digital Cadastral Map (DCM) and orthoimages in addition to an airborne laser scanning point cloud. Its results were unsatisfactory, since the determination of roof types was not reliable. Thus, a practical application of this method was not possible. While searching possibilities for its improvement, it was discovered that derivation of roof edges from orthoimages was the weakest point in the workflow. A new model driven method is presented, which is suitable for buildings with a rectangular plot. Based on four predefined roof types, a subset of a point cloud is divided into several groups corresponding to roof planes. The best fitting planes are found by means of least squares adjustment with an iterative exclusion of outliers. The most probable roof type is selected by an evaluation of a number of points excluded from the calculation. Results of this new approach were applied on datasets from three test sites (Brno, Sobotka and Pardubice- Polabiny) which are presented. In spite of a very low density of laser points (1.5 and 0.25 points per m2) the method reveals very good results. The success rate of correctly determined roof cover types is 91% and 80% for the point cloud density of 1.5 and 0.25 points per m2, respectively. Určení typu střešního pláště budov z řídkého mračna laserových bodů Metoda představená v článku měla za cíl automatickým postupem detekovat typ střešního pláště i ve velmi řídkém mračnu laserových bodů. Aby bylo možné tyto požadavky splnit, byla zvolena metoda řízená modelem, která zpracovává pouze budovy s obdélníkovým tvarem. Na základě předem vybraných testovaných typů střešního pláště bylo mračno bodů podle polohy rohů rozděleno do několika souborů odpovídajících střešním rovinám. Těmito body byla pomocí metody nejmenších čtverců iteračně prokládána regresní rovina s postupným odstraňováním odlehlých bodů. Na základě podílu bodů ponechaných ve výpočtu byl zvolen nejpravděpodobnější typ střešního pláště. Postup byl testován celkem na 460 budovách z oblasti města Brna, Sobotky a Pardubice-Polabiny. Navzdory velmi nízké hustotě dat (1,5 a 0,25 bodů/m2) dává metoda velmi dobré výsledky. Podíl správně určených budov dosahuje 91 % při hustotě 1,5 bodů/m2 a 80 % při 0,25 bodech/m2. Nevýhodou uvedeného postupu je především nízká univerzálnost a detailnost výsledných modelů. Naopak velkou výhodou je možnost práce i s velmi nízkou hustotou vstupních laserových bodů a plně automatizované zpracování