Participatory texture documentation (PTD) is a geospatial data collection process in which a group of users (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative collection of urban texture information. PTD enables inexpensive, scalable and high resolution data collection for urban texture mapping. In this paper, we introduce GeoSIM (Geosp...

We investigate the use of co-occurrence texture measures to provide information on different building densities inside a town structure. We try to improve the pixel-by-pixel classification of an urban area by considering texture measures as a means for block analysis and classification. We find some interesting hints concerning the optimal dimension of the window to be considered for texture me...

An urban heat island (UHI) is a climate phenomenon that results in an increased air temperature in cities when compared to their rural surroundings. In this Letter, the dependence of an UHI on urban geometry is studied. Multiyear urban-rural temperature differences and building footprints data combined with a heat radiation scaling model are used to demonstrate for more than 50 cities worldwide...

SPOT panchromatic imagery of Beijing has been studied to capture the unique spatial pattern of the city. Texture analysis, which reveals spatial variations, was adopted to interpret the urban spatial patterns. Statistical and structural texture features were extracted from the SPOT image and evaluated for their capability of detailed mapping of urban structures. Supervised image classifications...

This paper explores the effects of urban texture on building energy consumption. It is based on the analysis of digital elevation models (DEMs)—raster models of cities which have proven to be very effective in the urban context. Different algorithms are proposed and discussed, including the calculation of the urban surface-to-volume ratio and the identification of all building areas that are wi...

Attempts to analyze urban features and to classify land use and land cover directly from high-resolution satellite data with traditional computer classification techniques have proven to be inefficient for two primary reasons. First, urban landscapes are composed of complex features. Second, traditional classifiers employ spectral information based on single pixel value and ignore a great amoun...

Urban built-up area information is required by various applications. However, urban built-up area extraction using moderate resolution satellite data, such as Landsat series data, is still a challenging task due to significant intra-urban heterogeneity and spectral confusion with other land cover types. In this paper, a new method that combines spectral information and multivariate texture is p...