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    Wyświetlanie 1-2 z 2
    Tytuł:
    Empirical methods of reducing the observations in geodetic networks
    Autorzy:
    Kadaj, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/145336.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    odwzorowanie kartograficzne
    obserwacja geodezyjna
    mapowanie
    cartographic mapping
    reducing of geodetic observations
    empirical methods of reducing observations
    mapping of long geodesics
    Opis:
    The paper presents empirical methodology of reducing various kinds of observations in geodetic network. A special case of reducing the observation concerns cartographic mapping. For numerical illustration and comparison of methods an application of the conformal Gauss-Krüger mapping was used. Empirical methods are an alternative to the classic differential and multi-stages methods. Numerical benefits concern in particular very long geodesics, created for example by GNSS vectors. In conventional methods the numerical errors of reduction values are significantly dependent on the length of the geodesic. The proposed empirical methods do not have this unfavorable characteristics. Reduction value is determined as a difference (or especially scaled difference) of the corresponding measures of geometric elements (distances, angles), wherein these measures are approximated independently in two spaces based on the known and corresponding approximate coordinates of the network points. Since in the iterative process of the network adjustment, coordinates of the points are systematically improved, approximated reductions also converge to certain optimal values.
    Źródło:
    Geodesy and Cartography; 2016, 65, 1; 13-40
    2080-6736
    2300-2581
    Pojawia się w:
    Geodesy and Cartography
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Using a GEOBIA framework for integrating different data sources and classification methods in context of land use/land cover mapping
    Autorzy:
    Osmólska, A.
    Hawryło, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/145304.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    mapa użytkowanych gruntów
    mapa pokrycia terenu
    mapa leśna
    data fusion
    random forest
    supervised classification
    Sentinel-2
    Opis:
    Land use/land cover (LULC) maps are important datasets in various environmental projects. Our aim was to demonstrate how GEOBIA framework can be used for integrating different data sources and classification methods in context of LULC mapping.We presented multi-stage semi-automated GEOBIA classification workflow created for LULC mapping of Tuszyma Forestry Management area based on multi-source, multi-temporal and multi-resolution input data, such as 4 bands- aerial orthophoto, LiDAR-derived nDSM, Sentinel-2 multispectral satellite images and ancillary vector data. Various classification methods were applied, i.e. rule-based and Random Forest supervised classification. This approach allowed us to focus on classification of each class ‘individually’ by taking advantage from all useful information from various input data, expert knowledge, and advanced machine-learning tools. In the first step, twelve classes were assigned in two-steps rule-based classification approach either vector-based, ortho- and vector-based or orthoand Lidar-based. Then, supervised classification was performed with use of Random Forest algorithm. Three agriculture-related LULC classes with vegetation alternating conditions were assigned based on aerial orthophoto and Sentinel-2 information. For classification of 15 LULC classes we obtained 81.3% overall accuracy and kappa coefficient of 0.78. The visual evaluation and class coverage comparison showed that the generated LULC layer differs from the existing land cover maps especially in relative cover of agriculture-related classes. Generally, the created map can be considered as superior to the existing data in terms of the level of details and correspondence to actual environmental and vegetation conditions that can be observed in RS images.
    Źródło:
    Geodesy and Cartography; 2018, 67, 1; 99-116
    2080-6736
    2300-2581
    Pojawia się w:
    Geodesy and Cartography
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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