The proposed method provides an effective way of extracting urban built-up areasįrom Landsat series images and could be applicable to other applications. Inclusion of multivariate variogram textures with spectral angle distance achieved the best Resultsĭemonstrated that the proposed method outperformed the use of spectral information aloneĪnd the joint use of the spectral information and the GLCM texture. Using bi-temporal Landsat TM/ETM+ data of two megacity areas in China. Matrix (GLCM) is also used to extract image texture. For comparison, the classical gray-level co-occurrence Textures and the spectral bands are then combined for urban built-up area extraction.īecause the urban built-up area is the only target class, a one-class classifier, one-class Separately extracted from multispectral data using a multivariate variogram with differentĭistance measures, i.e., Euclidean, Mahalanobis and spectral angle distances. Spectral information and multivariate texture is proposed. In this paper, a new method that combines Spectral confusion with other land cover types. Series data, is still a challenging task due to significant intra-urban heterogeneity and Urban built-up area extraction using moderate resolution satellite data, such as Landsat Urban built-up area information is required by various applications. Furthermore, such a collaborative approach can be used for collecting a global coverage of LU information specifically in countries in which temporal and monetary efforts could be minimized. The findings strength the potential of collaboratively collected LU features for providing temporal LU maps as well as updating/enriching existing inventories. Moreover, the results identify which land types preserve high/moderate/low accuracy across cities for urban LU mapping. The empirical findings suggest OSM as an alternative complementary source for extracting LU information whereas exceeding 50 % of the selected cities are mapped by mappers. Kappa index analysis along with per-class user’s and producers’ accuracies are used for accuracy assessment. The main objective of this paper is to comparatively assess the accuracy of the contributed OSM-LU features in four German metropolitan areas versus the pan-European GMESUA dataset as a reference. In this regard, the OpenStreetMap (OSM) project has been one of the most successful representatives, providing LU features. But recently, Web 2.0 technologies and the wide dissemination of GPS-enabled devices boosted public participation in collaborative mapping projects (CMPs). However, both data gathering approaches are financially expensive and time consuming. Remote sensing images and signal processing techniques, as well as land surveying are the prime sources to map LU features. A large amount of effort and monetary resources are spent on mapping LU features over time and at local, regional, and global scales. Land use (LU) maps are an important source of information in academia and for policy-makers describing the usage of land parcels. However, a low population density does not necessarily imply a low strength of agreement. Additionally, the results indicate that a high population density, as present in urbanized areas, seems to denote a higher strength of agreement between OSM and the DLM (Digital Landscape Model). In contrast, farmland also covers a large area, but for this class OSM shows a low completeness value (45.9%) due to unmapped areas. Forest covers a large area and shows both a high OSM completeness (97.6%) and correctness (95.1%). Nonetheless, for our study region, there are clear variations between the LULC classes. The results show that the kappa value indicates a substantial agreement between the OSM and the authoritative dataset. Two spatial data quality elements, thematic accuracy and completeness are addressed by comparing the OSM data with an authoritative German reference dataset. In this study, the quality of OSM land use and land cover (LULC) data is investigated for an area in southern Germany. This fact is frequently a cause of concern regarding the quality and usability of such data. Normally, untrained contributors gather these data. Volunteered Geographic Information (VGI) such as data derived from the OpenStreetMap (OSM) project is a popular data source for freely available geographic data.
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