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Abstract: This paper presents an evolutionary algorithm for wind speed reconstruction from synoptic pressure patterns. The algorithm operates in a search space formed by grids of pressure measures, and must classify the different situations into classes, in such a way that a measure of wind speed in a given point is minimized among patterns assigned to the same class. Then, each class is assigned a mean wind speed and direction, so the wind speed reconstruction is possible for a new grid of synoptic pressures. In this paper we present the problem model and the specific description of the evolutionary algorithm proposed to solve the problem. We also show the good performance of the proposed method in the reconstruction of the average wind speed in six wind towers in Spain. The proposed method is applicable to wind speed reconstruction or reconstruction of wind missing data of wind series, specially when there is no other variable or related measure available. [Copyright &y& Elsevier]

Urban researchers and planners are often interested in understanding how economic activities are distributed in urban regions, what forces influence their special pattern and how urban structure and functions are mutually dependent. In this paper, we want to show how an algorithm for ranking the nodes in a network can be used to understand and visualize certain commercial activities of a city. The first part of the method consists of collecting real information about different types of commercial activities at each location in the urban network of the city of Murcia, Spain. Four clearly differentiated commercial activities are studied, such as restaurants and bars, shops, banks and supermarkets or department stores, but obviously we can study other. The information collected is then quantified by means of a data matrix, which is used as the basis for the implementation of a PageRank algorithm which produces a ranking of all the nodes in the network, according to their significance within it. Finally, we visualize the resulting classification using a colour scale that helps us to represent the business network. [ABSTRACT FROM PUBLISHER]

Abstract: Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level. [Copyright &y& Elsevier]

Abstract: In this paper we present a segmentation algorithm, inspired from an image-processing region-merging algorithm, for the delineation of discrete contiguous management zones in agriculture. The algorithm is unique in that it is applicable to high- or low-density irregular data sets, such as yield data. The algorithm is described and a brief example presented using unprocessed sensor-derived grain yield data. A comparison between the segmentation algorithm and a common classification algorithm (k-means clustering) was done using an aerial normalised differences vegetation index (NDVI) image collected on a 200ha vineyard in Olite, Southern Navarre, Spain. Classification was performed as a univariate (NDVI) analysis and a spatially constrained analysis. Segmentation and classification were run to find 2, 4, 6, …, 24 levels and the effectiveness of the outputs determined by how well it explained the variance in vine trunk circumference, a correlated but independent measurement. The results obtained demonstrated that for a given number of manageable (effective) zones the segmentation outputs were equivalent or superior to the classification outputs for partitioning vine circumference variance. The segmentation output also generated more coherent management units that should facilitate differential management. The algorithm presented is a first generation segmentation algorithm and several aspects still need to be developed, in particular methods for eliminating edge effects and converting management zones into management (treatment) classes. The results of the segmentation algorithm presented here would indicate that with further development, segmentation might provide an alternative and possibly preferable approach to delineating management zones. [Copyright &y& Elsevier]

Abstract: This paper presents a decision support system for automatic keep-clear signs management. The system consists of several modules. First of all, an acquisition module obtains images using a vehicle equipped with two recording cameras. A recognition module, which is based on Support Vector Machines (SVMs), analyzes each image and decides if there is a keep-clear sign in it. The images with keep-clear signs are included into a Geographical Information System (GIS) database. Finally in the management module, the data in the GIS are compared with the council database in order to decide actions such as repairing or reposition of signs, detection of possible frauds etc. We present the first tests of the system in a Spanish city (Meco, Madrid), where the systems is being tested for its application in the near future. [Copyright &y& Elsevier]