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Studies addressing the supervised extraction of geospatial elements from aerial imagery with semantic segmentation operations (including road surface areas) commonly feature tile sizes varying from 256 × 256 pixels to 1024 × 1024 pixels with no overlap. Relevant geo-computing works in the field often comment on prediction errors that could be attributed to the effect of tile size (number of pixels or the amount of information in the processed image) or to the overlap levels between adjacent image tiles (caused by the absence of continuity information near the borders). This study provides further insights into the impact of tile overlaps and tile sizes on the performance of deep learning (DL) models trained for road extraction. In this work, three semantic segmentation architectures were trained on data from the SROADEX dataset (orthoimages and their binary road masks) that contains approximately 700 million pixels of the positive “Road” class for the road surface area extraction task. First, a statistical analysis is conducted on the performance metrics achieved on unseen testing data featuring around 18 million pixels of the positive class. The goal of this analysis was to study the difference in mean performance and the main and interaction effects of the fixed factors on the dependent variables. The statistical tests proved that the impact on performance was significant for the main effects and for the two-way interaction between tile size and tile overlap and between tile size and DL architecture, at a level of significance of 0.05. We provide further insights and trends in the predictions of the extensive qualitative analysis carried out with the predictions of the best models at each tile size. The results indicate that training the DL models on larger tile sizes with a small percentage of overlap delivers better road representations and that testing different combinations of model and tile sizes can help achieve a better extraction performance.

Los estudios actuales de evaluación paisajística estiman la calidad y fragilidad del paisaje, empleando herramientas de análisis espacial para su valoración, caracterizando aspectos tanto ecológicos como estéticos y valorando óptimamente la vegetación como elemento crucial del paisaje. En esta línea, la presente investigación desarrolla una metodología de caracterización paisajística, diferenciando los elementos naturales y no naturales que lo conforman y valorándolos a través de dos escalas numéricas, una escala de adecuación y una escala de conservación, identificando qué elementos vulneran más o menos al paisaje y qué zonas del entorno natural deben ser remodeladas paisajísticamente. El resultado de este tipo de investigaciones puede ser utilizado por diferentes niveles de gestión pública: en primer lugar, los ayuntamientos pueden identificar cuáles son los elementos más conflictivos que presenta el área de estudio y, en segundo lugar, las comunidades autónomas podrían incluir esta información en los Planes de Ordenación de los Recursos Naturales.

The aim of personalized maps is to help individual users to read maps and focus on the most task-relevant information. Several approaches have been suggested to develop personalized maps for cities, but few consider the spatial knowledge of its users. We propose the design of “cognitively-aware” personalized maps, which take into account the previous experience of users in the city and how the urban space is configured in their minds. Our aim is to facilitate users’ mental links between maps and city places, stimulating users to recall features of the urban space and to assimilate new spatial knowledge. To achieve this goal, we propose the personalization of maps through a map design process based on user modeling and on inferring personalization guidelines from hand-drawn sketches of urban spaces. We applied this process in an experiment with tourists in Madrid, Spain. We categorized the participants into three types of tourists—“Guided”, “Explorer”, and “Conditioned”—according to individual and contextual factors that can influence their spatial knowledge of the city. We also extracted design guidelines from tourists’ sketches and developed map prototypes. The empirical results seem to be promising for developing personalized city maps that could be produced on-the-fly in the future.

This article sets forth the early phases of a methodological proposal for designing and developing Volunteered Geographic Information (VGI) initiatives based on a system perspective analysis in which the components depend and interact dynamically among each other. First, it focuses on those characteristics of VGI projects that present different goals and modes of organization, while using a crowdsourcing strategy to manage participants and contributions. Next, a tool is developed in order to design the central crowdsourced processing unit that is best suited for a specific project definition, associating it with a trend towards crowd-based or community-driven approaches. The design is structured around the characterization of different ways of participating, and the task cognitive demand of working on geo-information management, spatial problem solving and ideation, or knowledge acquisition. Then, the crowdsourcing process design helps to identify what kind of participants are needed and outline subsequent engagement strategies. This is based on an analysis of differences among volunteers’ participatory behaviors and the associated set of factors motivating them to contribute, whether on a crowd or community-sourced basis. From a VGI system perspective, this paper presents a set of guidelines and methodological steps in order to align project goals, processes and volunteers and thus successfully attract participation. This methodology helps establish the initial requirements for a VGI system, and, in its current state, it mainly focuses on two components of the system: project and participants.