Augmenting the landscape scene: students as participatory evaluators of mobile geospatial technologies

This paper provides a two-phase study to compare alternative techniques for augmenting landscape scenes on geography fieldtrips. The techniques were: a pre-prepared acetate overlay; a custom-designed mobile field guide; locative media on a smartphone; virtual globe on a tablet PC; a head-mounted virtual reality display, and a geo-wand style mobile app. In one field exercise the first five techniques were compared through analysis of interviews and student video diaries, combined with direct observation. This identified a particular challenge of how to direct user attention correctly to relevant information in the field of view. To explore this issue in more detail, a second field exercise deployed ‘Zapp’, a bespoke geo-wand-style app capable of retrieving information about distant landscape features. This was evaluated using first-person video and spatial logging of in-field interactions. This paper reflects upon the relative merits of these approaches and highlights particular challenges of using technology to mimic a human field guide in pointing out specific aspects of the landscape scene. We also explore the role of students acting as design informants and research co-participants, which can be mutually beneficial in promoting a critical appreciation of the role of technology to support learning about the landscape

Collaborative geographic visualization

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas AmbientaisThe present document is a revision of essential references to take into account when developing ubiquitous Geographical Information Systems (GIS) with collaborative visualization purposes. Its chapters focus, respectively, on general principles of GIS, its multimedia components and ubiquitous practices; geo-referenced information visualization and its graphical components of virtual and augmented reality; collaborative environments, its technological requirements, architectural specificities, and models for collective information management; and some final considerations about the future and challenges of collaborative visualization of GIS in ubiquitous environment

Volunteered Drone Imagery: Challenges and constraints to the development of an open shared image repository

Orthorectified imagery is valuable for a wide range of initiatives including environmental change detection, planning, and disaster response. Obtaining aerial imagery at high temporal and spatial scale has traditionally been expensive. Due to lower costs and improved ease of use, unmanned aerial vehicles (UAVs) have been increasingly prevalent. This presents an opportunity to share images as part of participatory geographic information systems initiatives similar to OpenStreetMap. We outline a workflow to generate maps from UAV aerial images. We then present a characterization of software platforms currently available to aid the development of maps from UAV imagery, defined by type of service, whether imagery hosting or data processing. From this analysis, we identify existing barriers to imagery sharing, including data licensing, data quality, and user engagement

Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset

Vehicle classification is a hot computer vision topic, with studies ranging from ground-view up to top-view imagery. In remote sensing, the usage of top-view images allows for understanding city patterns, vehicle concentration, traffic management, and others. However, there are some difficulties when aiming for pixel-wise classification: (a) most vehicle classification studies use object detection methods, and most publicly available datasets are designed for this task, (b) creating instance segmentation datasets is laborious, and (c) traditional instance segmentation methods underperform on this task since the objects are small. Thus, the present research objectives are: (1) propose a novel semi-supervised iterative learning approach using GIS software, (2) propose a box-free instance segmentation approach, and (3) provide a city-scale vehicle dataset. The iterative learning procedure considered: (1) label a small number of vehicles, (2) train on those samples, (3) use the model to classify the entire image, (4) convert the image prediction into a polygon shapefile, (5) correct some areas with errors and include them in the training data, and (6) repeat until results are satisfactory. To separate instances, we considered vehicle interior and vehicle borders, and the DL model was the U-net with the Efficient-net-B7 backbone. When removing the borders, the vehicle interior becomes isolated, allowing for unique object identification. To recover the deleted 1-pixel borders, we proposed a simple method to expand each prediction. The results show better pixel-wise metrics when compared to the Mask-RCNN (82% against 67% in IoU). On per-object analysis, the overall accuracy, precision, and recall were greater than 90%. This pipeline applies to any remote sensing target, being very efficient for segmentation and generating datasets.Comment: 38 pages, 10 figures, submitted to journa

El paisaje a través de la representación gráfica: la Realidad Aumentad como herramienta de interpretación

Within a sociocultural context where visual components are becoming increasingly important and new technologies are spreading, geography must adapt to new demands, avoid trivialization in the use of images, and approach the task of creating graphic elements based on an effective and rigorous transmission of knowledge. Considering Augmented Reality as an advantageous technology due to the interactive, self-guided and dynamic nature of its tools, this research seeks to prove its effectiveness and determine the main benefits derived from its application in the representation of landscapes. The methodology takes as its starting point basic graphic materials, most of which are already known in landscape studies. Procedures based on new computer techniques are applied to these materials in order to obtain digital resources compatible with Augmented Reality and Virtual Reality. These resources can be integrated into more complex tools that help explain the composition and dynamics of landscapes. Thus, the figures presented in this article are accompanied by a web link and also incorporate a hyperlink, so that by clicking on them, the aforementioned resources are accessed. And those figures with the Observatorio del Territorio (OT) logo are image markers in themselves that allow Augmented Reality content to be opened on devices. The results are obtained by testing various forms of multimedia representation in the context of an R&D project with application in various urban, rural and natural areas of the Principality of Asturias. These are hosted on the server of the Observatorio del Territorio at the University of Oviedo. The conclusions indicate the beneficial use of dynamic sequences (animations, sliders…) for a better understanding of diachronic changes; the interactive third dimension for the representation of complex elements; or the general added value of combining information on media (audio, video, 360º panoramas, etc.) in the understanding of shapes and structures.En un contexto sociocultural donde los componentes visuales cobran cada vez más importancia y las nuevas tecnologías se difunden, la Geografía debe adaptarse a las nuevas exigencias, evitar la banalización en el uso de las imágenes y abordar la tarea de crear elementos gráficos que permitan una transmisión eficaz y rigurosa del conocimiento. Considerando la Realidad Aumentada como una tecnología ventajosa por el carácter interactivo, auto-guiado y dinámico de sus herramientas, esta investigación tiene como objetivo probar su efectividad y mostrar los principales beneficios derivados de su aplicación en la representación del paisaje. La metodología toma como punto de partida materiales gráficos de base, la mayor parte de los cuales son ya conocidos en los estudios del paisaje. Sobre estos materiales se aplican procedimientos basados en nuevas técnicas informáticas que permiten obtener recursos digitales compatibles con la Realidad Aumentada y la Realidad Virtual. Estos recursos pueden ser integrados en herramientas más complejas que ayudan a explicar la composición y dinámica de los paisajes. Así, las figuras presentadas en este artículo se acompañan de un enlace web e incorporan, además, un hipervínculo, de manera que al hacer clic sobre ellas se accede a los recursos mencionados. Aquellas figuras con el logo del Observatorio del Territorio (OT) constituyen marcadores de imagen en sí mismos que permiten abrir contenidos de Realidad Aumentada en los dispositivos. Los resultados provienen de probar varias formas de representación multimedia en el contexto de un proyecto de I+D con aplicación en diversas áreas urbanas, rurales y de dominante natural del Principado de Asturias. Estos se alojan en el servidor del Observatorio del Territorio de la Universidad de Oviedo. Las conclusiones subrayan el uso beneficioso de secuencias dinámicas (animaciones, comparadores de fechas con cortinilla deslizante...) para una mejor comprensión de los cambios; la tercera dimensión interactiva para la representación de elementos complejos; o el valor añadido general de combinar información de soportes (audio, vídeo, panorámicas 360º, etc.) en la comprensión de formas y estructuras.This work was supported by Spanish Ministerio de Economía y Competitividad under Grant CSO2017-84623-R. It is part of the results from the research project La Realidad Aumentada como herramienta para la explicación del paisaje. Aplicaciones a la docencia y al turismo

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Location-based technologies for learning

Emerging technologies for learning report - Article exploring location based technologies and their potential for educatio

Drone Journalism as Visual Aggregation: Toward a Critical History

The use of unmanned aerial vehicles (UAVs—commonly referred to as drones) in journalism has emerged only recently, and has grown significantly. This article explores what makes drone imagery as an instance of what scholars of visual culture call an aerial view so compelling for major news organizations as to warrant such attention and investment. To do this, the concept ‘visual aggregation’ is introduced to theorize the authority of drone imagery in conventional journalistic practice. Imagery produced through drone journalism is a visual analogy to statistical summary and, more recently, of what is referred to as data journalism. Just as these combine an aggregate of cases to produce an understanding of an overall trend, drone imagery aggregates space visually, its broad visual field revealing large-scale spatial patterns in ways analogous to the statistical capture/analysis of large bodies of data. The article then employs a cultural and historical approach to identify key points in the emergence of visual aggregation as authoritative truth. The aerial view as a claim to truth is manifest in a wide range of antecedent social formations, devices and practices prior to their amalgamation in what has today become drone journalism. This analysis aids understanding of how drone journalism is a response to the institutional crises of journalism today

Low-cost navigation and guidance systems for unmanned aerial vehicles - part 1: Vision-based and integrated sensors

In this paper we present a new low-cost navigation system designed for small size Unmanned Aerial Vehicles (UAVs) based on Vision-Based Navigation (VBN) and other avionics sensors. The main objective of our research was to design a compact, light and relatively inexpensive system capable of providing the Required Navigation Performance (RNP) in all phases of flight of a small UAV, with a special focus on precision approach and landing, where Vision Based Navigation (VBN) techniques can be fully exploited in a multisensor integrated architecture. Various existing techniques for VBN were compared and the Appearance-Based Approach (ABA) was selected for implementation. Feature extraction and optical flow techniques were employed to estimate flight parameters such as roll angle, pitch angle, deviation from the runway and body rates. Additionally, we addressed the possible synergies between VBN, Global Navigation Satellite System (GNSS) and MEMS-IMU (Micro-Electromechanical System Inertial Measurement Unit) sensors, as well as the aiding from Aircraft Dynamics Models (ADMs)