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.

Resource consumption in residential areas requires novel contributions in the field of consumer information management and collaborative mechanisms for the exchange of resources, in order to optimize the overall consumption of the community. We propose an authorization system to facilitate access to consumer information and resource trading, based on blockchain technology. Our proposal is oriented to the Smart communities, an evolution of Community Energy Management Systems, in which communities are involved in the monitoring and coordination of resource consumption. The proposed environment allows a more reliable management of monitoring and authorization functions, with secure data access and storage and delegation of controller functions among householders. We provide the definition of virtual assets for energy and water resource sharing as an auction, which encourages the optimization of global consumption and saves resources. The proposed solution is implemented and validated in application scenarios that demonstrate the suitability of the defined consensus mechanism, trustworthiness in the level of provided security for resource monitoring and delegation and reduction on resource consumption by the resource trading contribution.

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Secondary roads represent the largest part of the road network. However, due to the absence of clearly defined edges, presence of occlusions, and differences in widths, monitoring and mapping them represents a great effort for public administration. We believe that recent advancements in machine vision allow the extraction of these types of roads from high-resolution remotely sensed imagery and can enable the automation of the mapping operation. In this work, we leverage these advances and propose a deep learning-based solution capable of efficiently extracting the surface area of secondary roads at a large scale. The solution is based on hybrid segmentation models trained with high-resolution remote sensing imagery divided in tiles of 256 × 256 pixels and their correspondent segmentation masks, resulting in increases in performance metrics of 2.7–3.5% when compared to the original architectures. The best performing model achieved Intersection over Union and F1 scores of maximum 0.5790 and 0.7120, respectively, with a minimum loss of 0.4985 and was integrated on a web platform which handles the evaluation of large areas, the association of the semantic predictions with geographical coordinates, the conversion of the tiles’ format and the generation of geotiff results compatible with geospatial databases.

Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. In addition, the road network plays an important part in transportation, and currently one of the main related challenges is detecting and monitoring the occurring changes in order to update the existent cartography. This task is challenging due to the nature of the object (continuous and often with no clearly defined borders) and the nature of remotely sensed images (noise, obstructions). In this paper, we propose a novel framework based on convolutional neural networks (CNNs) to classify secondary roads in high-resolution aerial orthoimages divided in tiles of 256 × 256 pixels. We will evaluate the framework’s performance on unseen test data and compare the results with those obtained by other popular CNNs trained from scratch.