Geospatial Environments Research Articles

Incorporating mental imagery into geospatial environments for narrative visualizations

AbstractMethods for evaluating cognitively inspired geospatial interfaces have been important for revealing and helping solve their cognitive and usability issues. We argue that this is now true of interfaces in GIScience that deliver narrative visualizations, including 3D virtual narrative environments. These spaces allow for controlled conditions and realistic natural settings, where spatio‐temporal data can be collected and used to ascertain how well an interface design fulfilled a given narrative function. This study investigates the function of a cognitively inspired geospatial interface (Future Vision) that aimed to determine how mental images can be situated in geospatial environments and used to convey narratives that improve user cognition and decision‐making. The results of a two‐alternative forced‐choice (2AFC) decision‐making task showed that participants using future thinking guidance (mental images as a split‐second display of correct path choice) had statistically significant improvements in their task completion times, movement speeds and 2AFC decision‐making, compared to the unguided control group. Implications of the results include benefits for cue‐based navigation of real and conceptual spaces in GIScience. Future research can improve the interface design by modifying the interface code to reduce visual loss caused by eye blinks and saccades.

A Lightweight Processing Method Towards Integrated Multi-demand in BIM

Abstract In recent years, the rapid advancement of Building Information Modeling (BIM) technology has led to an increasing variety of application scenarios, necessitating diverse demands on the technology. Among these requirements, the lightweighting of BIM models is of paramount importance. However, conventional approaches primarily focus on the models themselves, neglecting the varying needs of users. This study presents a novel lightweighting method for BIM models, starting from user interests or specific scenario requirements. It employs optimized data structures and organization techniques to streamline source data, thereby solving the problem of lightweighting BIM models while catering to the diverse needs of users. A comparison of studies using apartment buildings, office buildings, and commercial complexes as examples show that this method works to greatly enhance the efficiency of visualizing different-sized BIM models in geospatial environments. This research provides a new approach to lightweighting BIM models and holds the promising potential to offer strong technical support for future advancements in BIM technology.

Feature selection for a continental-scale geospatial model of environmental sound levels.

Modeling environmental sound levels over continental scales is difficult due to the variety of geospatial environments. Moreover, current continental-scale models depend upon machine learning and therefore face additional challenges due to limited acoustic training data. In previous work, an ensemble of machine learning models was used to predict environmental sound levels in the contiguous United States using a training set composed of 51 geospatial layers (downselected from 120) and acoustic data from 496 geographic sites from Pedersen, Transtrum, Gee, Lympany, James, and Salton [JASA Express Lett. 1(12), 122401 (2021)]. In this paper, the downselection process, which is based on factors such as data quality and inter-feature correlations, is described in further detail. To investigate additional dimensionality reduction, four different feature selection methods are applied to the 51 layers. Leave-one-out median absolute deviation cross-validation errors suggest that the number of geospatial features can be reduced to 15 without significant degradation of the model's predictive error. However, ensemble predictions demonstrate that feature selection results are sensitive to variations in details of the problem formulation and, therefore, should elicit some skepticism. These results suggest that more sophisticated dimensionality reduction techniques are necessary for problems with limited training data and different training and testing distributions.

Predicting dynamic spectrum allocation: a review covering simulation, modelling, and prediction

The advent of the Internet of Things and 5G has further accelerated the growth in devices attempting to gain access to the wireless spectrum. A consequence of this has been the commensurate growth in spectrum conflict and congestion across the wireless spectrum, which has begun to impose a significant impost upon innovation in both the public and private sectors. One potential avenue for resolving these issues, and improving the efficiency of spectrum utilisation can be found in devices making intelligent decisions about their access to spectrum through Dynamic Spectrum Allocation. Changing to a system of Dynamic Spectrum Allocation would require the development of complex and sophisticated inference frameworks, that would be able to be deployed at a scale able to support significant numbers of devices. The development and deployment of these systems cannot exist in isolation, but rather would require the development of tools that can simulate, measure, and predict Spectral Occupancy. To support the development such tools, this work reviews not just the available prediction frameworks for networked systems with sparse sensing over large scale geospatial environments, but also holistically considers the myriad of technological approaches required to support Dynamic Spectrum Allocation.

Systematic Comparison of Objects Classification Methods Based on ALS and Optical Remote Sensing Images in Urban Areas

Geographical object classification and information extraction is an important topic for the construction of 3D virtual reality and digital twin cities in urban areas. However, the majority of current multi-target classification of urban scenes uses only a single source data (optical remote sensing images or airborne laser scanning (ALS) point clouds), which is limited by the restricted information of the data source itself. In order to make full use of the information carried by multiple data sources, we often need to set more parameters as well as algorithmic steps. To address the above issues, we compared and analyzed the object classification methods based on data fusion of airborne LiDAR point clouds and optical remote sensing images, systematically. Firstly, the features were extracted and determined from airborne LiDAR point clouds and high-resolution optical images. Then, some key feature sets were selected and were composed of median absolute deviation of elevation, normalized elevation values, texture features, normal vectors, etc. The feature sets were fed into various classifiers, such as random forest (RF), decision tree (DT), and support vector machines (SVM). Thirdly, the suitable feature sets with appropriate dimensionality were composed, and the point clouds were classified into four categories, such as trees (Tr), houses and buildings (Ho), low-growing vegetation (Gr), and impervious surfaces (Is). Finally, the single data source and multiple data sources, the crucial feature sets and their roles, and the resultant accuracy of different classifier models were compared and analyzed. Under the conditions of different experimental regions, sampling proportion parameters and machine learning models, the results showed that: (1) the overall classification accuracy obtained by the feature-level data fusion method was 76.2% compared with the results of only a single data source, which could improve the overall classification accuracy by more than 2%; (2) the accuracy of the four classes in the urban scenes can reach 88.5% (Is), 76.7% (Gr), 87.2% (Tr), and 88.3% (Ho), respectively, while the overall classification accuracy can reach 87.6% with optimal sampling parameters and random forest classifiers; (3) the RF classifier outperforms DT and SVM for the same sample conditions. In this paper, the method based on ALS point clouds and image data fusion can accurately classify multiple targets in urban scenes, which can provide technical support for 3D scene reconstruction and digital twin cities in complex geospatial environments.

Integrating Indigenous Knowledge and Geographical Information System in mapping flood vulnerability in informal settlements in a South African context: a critical review

ABSTRACT Climate change and the multidimensional nature of flood vulnerability, necessitate an in-depth consideration of experiential knowledge in reducing flood vulnerability in geospatial environments such as informal settlements. The objective of this paper is to provide a critical review of literature and scholarship in the context of the integration of Indigenous Knowledge and Geographical Information System in mapping flood vulnerability in South Africa. Keywords were searched in two databases for empirical studies that integrated Indigenous Knowledge and Geographical Informal System in mapping flood vulnerability in South Africa. The search extended to literature cited in the papers identified from the databases. The studies were thematically analysed and synthesized. The findings reveal that flood vulnerability in South Africa is mainly considered from an integrated perspective. Approaches used to integrate Indigenous Knowledge and Geographical Information Systems in mapping flood vulnerability are fragmented. There is a lack of sensitivity analysis and map validation, limited use of Indigenous Knowledge as well as inadequate community participation in the mapping of flood vulnerability in informal settlements. This critical review demonstrates the need for a comprehensive, explicit and participatory approach for integrating Indigenous Knowledge and Geographical Information System that genuinely utilize the special characteristics that each of them possesses.

Models of cultural heritage buildings in a procedurally generated geospatial environment

AbstractThis article presents a methodology combining building information modelling, CAD, procedural modelling, 3D GIS, and digital cartography to create information models of cultural heritage buildings and their geospatial environments both in history and nowadays. The results of the workflow are 3D web scenes where the most historically significant buildings are presented as detailed 3D textured information models. These models consist of 3D representations of specific architectural elements, which contain semantic information and attributes. Other valuable buildings are modelled classically in CAD and they can be identified as one complete object each, and thus the users can view concise information about the history and important aspects of the buildings. Finally, the common conurbation is generated procedurally. The 3D visualizations can be viewed in a web browser without any plugins needed, and can familiarize users from the general public with the architectural structure of the objects of interest and their environment.

Geospatial Environments for Hurricane Risk Assessment: Applications to Situational Awareness and Resilience Planning in New Jersey

Mitigation of losses due to coastal hazards has become an increasingly urgent and challenging problem in light of rising seas and the continued escalation of coastal population density. Unfortunately, stakeholders responsible for assuring the safety of these coastal communities are not equipped with the engineering research community’s latest tools for high-fidelity risk assessment and geospatial decision support. In the event of a hurricane or nor’easter, such capabilities are exceptionally vital to project storm impacts on critical infrastructure and other municipal assets and to inform preemptive actions that can save lives and mitigate property damage. In response, a web-based visualization environment was developed using the GeoNode content management system, informed by the needs of municipal stakeholders. Within this secure platform, registered users with roles in planning, emergency management and first response can simulate the impact of hurricanes and nor’easters using the platform’s storm Hazard Projection (SHP) Tool. The SHP Tool integrates fast-to-compute windfield models with surrogate models of high-fidelity storm surge and waves to rapidly simulate user-defined storm scenarios, considering the effects of tides, sea level rise, dune breaches and track uncertainty. In the case of a landfalling hurricane, SHP tool outputs are automatically loaded into the user’s dashboard to visualize the projected wind, storm surge and wave run-up based on the latest track information published by the National Hurricane Center. Under either use case, outputs of the SHP Tool are visualized within a robust collaborative geospatial environment supporting the seamless exploration of centralized libraries of geographic information system (GIS) data from federal, state, county and local authorities, with tools to add user-supplied annotations such as notes or other geospatial mark-ups. This paper will overview the development and deployment of this platform in the State of New Jersey, detailing the cyberinfrastructure design and underlying computational models, as well as the user stories that inspired the platform’s functionalities and interfaces. The study concludes with reflections from the process of piloting this project with stakeholders at the state and municipal level to support more risk-responsive and data-informed decision making.

Augmenting a deep-learning algorithm with canal inspection knowledge for reliable water leak detection from multispectral satellite images

Maintenance planning of groundwater delivery infrastructure, such as canals, requires labor-intensive field inspection for properly allocating maintenance resources to sections of water infrastructure based on their deterioration conditions. Defective canal sections have cracks where the water delivery performance degrades. In practice, canals can be tens or even hundreds of miles long. Manual canal inspections could take weeks, while could hardly achieve comprehensive water leakage assessment. Another difficulty is that most cracks are developing under the water. Without drying up the canals, inspectors could not observe underwater conditions. They would have to assess visible parts of water facilities and environments (e.g., humidity changes and vegetation growths nearby) for prioritizing canal sections in terms of leaking risks. Even experienced inspectors need much time to complete a reliable canal condition assessment.This paper presents a deep-learning approach augmented by canal inspection knowledge to achieve automated and reliable water leak detection of canal sections from Landsat 8 satellite images. Such integration utilizes the domain knowledge of experienced inspectors in augmenting the deep-learning methods for more reliable image pattern classification that supports rapid canal condition assessment. Compared with machine learning algorithms trained by raw satellite images manually labeled as leaking, domain-knowledge-augmented deep learning algorithms use satellite image augmented by pixel-level land surface temperature (LST), fractional vegetation coverage (FVC) and Temperature Vegetation Dryness Index (TVDI) as training samples. Specifically, LST, FVC, and TVDI for each pixel are physical parameters derived from Landsat 8 satellite images by remote sensing methods. The “leaking” or “no-leaking” labels of the training samples are from the concrete surface inspection records collected during annual dry-ups of the canal from 2016 to 2019. Testing results on data sets collected for canals flowing through both urban and rural areas show that the proposed approach can achieve recall at 86%, precision at 86%, and accuracy at 85%. The precision, recall, and accuracy of the proposed approach are similar to a conventional deep learning algorithm that uses raw images for training while being more computationally efficient. The reason is that the new approach only processes three channels rather than the 11 channels in raw images. The authors also tested how different combinations of environmental features influence the performance of the algorithm. The results showed that two feature combinations: (LST, FVC) and (LST, FVC, TVDI) achieve the most robust performance in diverse geospatial environments.

Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval

Geospatial information is gaining immense interest and importance as we enter the era of highly developed transportation and communication. Despite the proliferation of cellular network and WiFi, on some occasions, users still face barriers to accessing geospatial data. In this paper, we design and implement a distributed prototype system with a delay/disruption tolerant network (DTN), named Geo-DMP, for cooperatively and opportunistically sharing and exchanging named geospatial contents in a device-to-device fashion. First of all, we construct a lightweight “content agent” module to bridge the gap between the application layer and the underlying DTN protocol stack. Afterwards, to profile the mobility history of users in practical geospatial environments, we present a map segmentation scheme based on road network and administrative subdivision information. Subsequently, we associate the regional movement history information with the content retrieval process to devise a hierarchical and region-oriented DTN routing scheme for both requests and responses. Finally, we conduct extensive experiments with real-world trajectories and complete implementations on the emulation platform composed of virtual machines. The experiments corroborate that Geo-DMP has the capability of successfully retrieving geospatial contents for users for most of the time under mobile circumstances with episodic connectivity. Moreover, en-route caches can be efficiently exploited to provision contents from multiple sources with less network resource consumption and shorter user-perceived latencies.

The application of geometric network models and building information models in geospatial environments for fire-fighting simulations

This study was motivated by the need to develop a micro-geographic information system (GIS) to represent and analyze 3D spatial data for fire-fighting simulations. The proposed method involved exploration of a 3D geometric network model (GNM)-based, building information model (BIM)-supported framework for fire-fighting simulation. The operations of ladder trucks were simulated in a virtual 3D environment. Using the method described in this paper, the best position for the deployment of the ladder trucks can be determined even before the arrival of firefighters at the scene. A real fire-fighting drill was also conducted as part of this study. The proposed method can assist firefighters quickly locate their ladder trucks, thereby reducing response time after an incident.

Human-Robot Teams Collaborating Socially, Organizationally, and Culturally

We describe an approach examining multi-level collaboration challenges by integrating social, organizational, and cultural factors for human-robot teams operating in the real world. We discuss the research at three levels of social interaction: within a team, within a social environment, and within a culture. We first describe research exploring psychologically and biologically inspired models of behavior to extend the capabilities of heterogeneous multi-human, multi-robot teams. We then discuss research issues that must be addressed to provide insights on how robots can correctly vary actions in response to cultural populations and geospatial environments by recognizing and properly interpreting human configurations, cultural artifacts and behaviors. The goal is to make it possible for robots to function effectively within dynamic operational and social situations.

Geospatial encountering: Opportunistic information discovery in web-based GIS environments

Web-based geographical information systems have become ubiquitous in daily life. They include such familiar tools as Google Earth, Google Maps, and a plethora of geolocational mobile applications for smart phones. These geospatial web systems present new opportunities for users to discover information opportunistically, in unexpected and non-intentional ways. This paper examines one specific type of incidental information acquisition - information encountering (Erdelez, 1997) - as it may occur in geospatial web environments. Accordingly, this preliminary research brings together findings and frameworks developed within two diverse fields: a) human information behavior, and b) geographic information systems. The Information Encountering model serves as a conceptual framework for understanding how the distinctive characteristics of geospatial web applications impact the nature of information encountering episodes. Exploratory research was conducted via a focus group session. Users were found to engage regularly with a variety of geolocational systems and report frequent episodes of information encountering, but they also display difficulty recalling details of individual episodes. Findings suggest the need to develop tools to capture information encountering experiences in the field, at or close to the time they occur.

GeoScope: Full 3D geospatial information system case study

Aiming at the integrative management and comprehensive applications of large-scale 3D geospatial information covering the full 3D space of a city, this paper briefly introduces the design and implementation of a full 3D GIS platform: GeoScope, which provides a professional solution for the massive full three-dimensional geospatial data integration, management, analysis, visualization, and applications. GeoScope is characterized by: (1) extendible software architecture based on the hierarchical message bus, facilitates multimodal integrative applications of 2D GIS and 3D GIS; (2) unified 3D city models, support multiscale semantic representation of outdoor & indoor and aboveground & underground 3D objects; (3) high-efficient 3D geospatial database engine, supports integrated management of massive 3D geospatial data for real-time applications; and (4) high-performance visualization engine exploiting the massively parallel computation architecture of modern GPUs, supports real-time realistic rendering of large-scale complicated 3D geospatial environments. The successful pilot application of GeoScope is also illustrated with the 3D city models of 8494 km2 of the whole Wuhan City, the largest city in middle China.

Naïve Realism: Limits of Realism as a Display Principle

Display designers are often called upon to create visualizations of complex geo-spatial environments for users engaged in tasks such as civil emergency, air traffic, or military operations. What visualization principles exist to guide them? One principle is to strive towards realism, on the belief that realistic depictions result in near effortless comprehension. We think this faith in realism is misplaced and term this misplaced faith Naïve Realism. Naïve Realism appears to stem from the folk belief that scene perception is simple, accurate, and rich, when, in fact, perception is remarkably complex, error-prone, and sparse. It results in the development of realistic displays that give users flawed, imprecise representations. Therefore, Naïve Realism offers a new account of why users sometimes prefer displays that subsequently under-perform. We review the evidence for Naïve Realism, its origins, why it persists, and conclude with a discussion of how good design can counteract it

Building a three‐dimensional geospatial virtual environment on computer gaming technology

Three‐dimensional representations of geographic information on computer are known as Geospatial Virtual Environments or GeoVEs. Previous work in the display of GeoVEs utilises a conventional hypermedia/multimedia approach. However, in recent times, large technological advances have seen computer games technology offer a fitting environment for serious academic study (Laird, 2001). Much literature has argued that GeoVEs lack meaningful interaction and realism, especially as compared to games (Champion 2002). Therefore, research into applying games technology to GeoVEs may be satisfactorily used to create more interactive, realistic learning environments for ‘non‐expert’ users, therefore providing high levels of engagement and knowledge formation. This paper examines the development of a desktop, three‐dimensional GeoVE built on computer gaming technology. It will explain the current processes undertaken and those that need to be completed to visualise the full model. The gaming environment is a non‐traditional approach to geographical information provision and may provide different images of reality, fulfil the need for accurate representations of the world and aid in catering to the user's specific mental perceptions of place and space. This research will also explore methods to extend the user's geographical knowledge through the use of multimodal metaphors, as proposed by Cartwright (1997; 1999).