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Tapia-McClung R.,Research Center En Geografia omatica Ing Jorge mayo
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

This paper presents two case studies of volunteered geographic information processes in two different neighborhoods in Mexico City. Both cases deal with citizen empowerment and actions directed for the improvement of their local surroundings. They are constructed in a bottom-up fashion: from the citizens towards the local authorities. A digital platform was developed to support usergenerated data collection for both cases; the second being an evolution of the first that incorporates several enhancements. The collection of enough citizen data is useful to focus efforts to negotiate with the authorities in detected regions and matters that need attention. Citizen-generated maps are useful communication tools to convey messages to the authorities, as the identification of these locations and situations provide a better picture of what, from the citizens’ perspective, is significantly deviated from the government’s point of view. The platform incorporates a way to validate official data, a voting strategy as a first approach to assess the credibility of citizen-contributed observations and crowdsourced information on parcel records. © Springer International Publishing Switzerland 2016. Source


Lopez-Caloca A.A.,Research Center En Geografia omatica Ing Jorge mayo
2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images, Multi-Temp 2015 | Year: 2015

This article presents a procedure to identify and extract urban areas in medium resolution satellite images. At present, we have and continue to study various methodologies to process and extract information on urban surfaces, since urban growth is having environmental impacts on the involved ecological systems. The proposed method takes advantage of the fact that data fusion allows us to combine in an optimal manner, multiple sources of classifiers and to generate a single source of information. In this context, we propose the use of data fusion algorithms, by multiple classifiers, taking into account the spectral and spatial characteristics of the satellite data, which in our case are the Landsat ETM+ and the ENVISAT-ASAR. The developed system includes an ensemble fusion architecture and the use of algorithms such as Fuzzy K-mean and Markov Random Field (MRF). The study case is the Guadalajara metropolitan area, in Jalisco, Mexico, which has great growth and sprawl; in its surrounding areas there are regions which are interesting in terms of geothermal exploitation and with great ecological value. The experimental results, using the multiple classifier system (MCS) show the urban characteristics at the regional scale, offering results that are potentially significant at this scale and the direction of changes in urban growth. © 2015 IEEE. Source


Sanchez del Rio J.,Rey Juan Carlos University | Moctezuma D.,Research Center En Geografia omatica Ing Jorge mayo | Conde C.,Rey Juan Carlos University | Martin de Diego I.,Rey Juan Carlos University | Cabello E.,Rey Juan Carlos University
Computers and Security | Year: 2016

A fast automated biometric solution has been proposed to satisfy the future border control needs of airports resulting from the rapid growth in the number of passengers worldwide. Automated border control (ABC) systems handle the problems caused by this growth, such as congestion at electronic gates (e-gates) or delays in the planned arrival schedules. Different modalities, such as face, fingerprint, or iris recognition, will be used in most of the ABC systems located at airports in the European/Schengen areas. Because facial recognition is the modality that travelers consider most acceptable, it was decided to include this modality in all second generation passports. Face recognition systems, installed in small kiosks inside the e-gates, require high quality facial images to allow high performance and efficiency. Accurate face recognition algorithms, which should be invariant to non-idealities, such as changes in pose and expression, occlusions, and changes in lighting, are also required for these systems. In this paper, a review of the most important face recognition algorithms described in the literature that are invariant to these non-idealities and that can be used in ABC e-gates is presented. A comparative analysis of the most common ABC e-gates located at the different airports is provided. In addition, the results of an experimental evaluation of a face recognition system when halogen, white LEDs, near infra-red, or fluorescence illumination was used, which was conducted in order to determine which type of illumination is optimal for use in ABC e-gates, are presented. To conclude, improvements that could be implemented in the near future in ABC face recognition systems are described. © 2016 The Authors Source


Bermeo A.,National Autonomous University of Mexico | Couturier S.,National Autonomous University of Mexico | Galeana Pizana M.,National Autonomous University of Mexico | Galeana Pizana M.,Research Center En Geografia omatica Ing Jorge mayo
Applied Geography | Year: 2014

The ancient milpa agricultural system has been regarded as a resilient agro-biodiverse practice within the context of recent global land-use changes. The conservation of the traditional smallholder cultivation system (TSCS) has been the focus of an extended civil network that is based on an alliance between agro-ecological science and indigenous knowledge. Collaborative rural planning has recently been supported in cartographic representations of indigenous territories in Mexico at the intercommunity scale. However, spatial assessments of multicropping milpa practice are largely lacking, and reduced access to land by smallholders is reported to be one of the key constraints on this practice.This research employs a method for the intercommunity-scale mapping of land availability for milpa cultivation (LAMC). This method combines information on land use derived from remote sensing data with information on agrarian structures deduced from census data and it takes into account the concentration of land ownership associated with capital-intensive coffee and pasture-based production systems. We applied this method to explore trends in LAMC between 1970 and 2010 in a rural sub-district of the State of Puebla, which is an indigenous, densely populated, and highly agro-biodiverse area of Mexico. The results were successfully assessed against conventional analysis based on an agricultural census. The method is replicable to any rural setting, provides an alternative approach to delineating peasant-indigenous territories of Mexico and contributes to baseline cartography for the global conservation of TSCS. © 2014 Elsevier Ltd. Source


Silvan-Cardenas J.L.,Research Center En Geografia omatica Ing Jorge mayo | Almazan-Gonzalez J.A.,Research Center En Geografia omatica Ing Jorge mayo | Couturier S.A.,National Autonomous University of Mexico
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Identifying housing buildings from afar is required for many urban planning and management tasks, including population estimations, risk assessment, transportation route design, market area delineation and many decision making processes. High-resolution remote sensing provides a cost-effective method for characterizing buildings and, ultimately, determining its most likely use. In this study we combined high-resolution multispectral images and LiDAR point clouds to compute building characteristics at the parcel level. Tax parcels were then classified in one of four classes (three residential classes and one non-residential class) using three classification methods: Maximum likelihood classification (MLC), Suport Vector Machines (SVM) with linear kernel and SVM with non-linear kernel. The accuracy assessment from a random sample showed that the maximum MLC was the most accurate method followed by SVM with linear kernel. The best classification method was then applied to the whole study area and the residential class was used to mask-out non-residential buildings from a building footprint layer. © 2014 Springer International Publishing. Source

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