Geospatial Domain Research Articles

Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future

All over the world, organizations are increasingly considering the adoption of open source software and open data. In the geospatial domain, this is no different, and the last few decades have seen significant advances in this regard. We review the current state of open source geospatial software, focusing on the Open Source Geospatial Foundation (OSGeo) software ecosystem and its communities, as well as three kinds of open geospatial data (collaboratively contributed, authoritative and scientific). The current state confirms that openness has changed the way in which geospatial data are collected, processed, analyzed, and visualized. A perspective on future developments, informed by responses from professionals in key organizations in the global geospatial community, suggests that open source geospatial software and open geospatial data are likely to have an even more profound impact in the future.

Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins

Artificial intelligence (AI) is changing fundamentally the way how IT solutions are implemented and operated across all application domains, including the geospatial domain. This contribution outlines AI-based techniques for 3D point clouds and geospatial digital twins as generic components of geospatial AI. First, we briefly reflect on the term “AI” and outline technology developments needed to apply AI to IT solutions, seen from a software engineering perspective. Next, we characterize 3D point clouds as key category of geodata and their role for creating the basis for geospatial digital twins; we explain the feasibility of machine learning (ML) and deep learning (DL) approaches for 3D point clouds. In particular, we argue that 3D point clouds can be seen as a corpus with similar properties as natural language corpora and formulate a “Naturalness Hypothesis” for 3D point clouds. In the main part, we introduce a workflow for interpreting 3D point clouds based on ML/DL approaches that derive domain-specific and application-specific semantics for 3D point clouds without having to create explicit spatial 3D models or explicit rule sets. Finally, examples are shown how ML/DL enables us to efficiently build and maintain base data for geospatial digital twins such as virtual 3D city models, indoor models, or building information models.

Rescaling smart destinations: The growing importance of smart geospatial services during and after COVID-19 pandemic

The COVID-19 pandemic has imposed numerous, lasting adverse effects on the global tourism industry. At the same time, it exposed the competitive advantages that existing smart tourism infrastructure could provide for addressing urgent health issues and providing meaningful smart services. This paper initially provides examples of smart geospatial services based on COVID-19 pandemic-related data, such as algorithms for measuring social distancing through CCTV and proximity contract tracing protocols and applications. Indeed, smart destinations, as an evolutionary step of smart cities, very quickly became a practical and research framework in various other disciplines, from leisure and service-oriented to technical and geospatial domains. However, various technologies employed and interests of different stockholders create a constant need for rescaling of smart data to facilitate their usability in providing optimized smart tourism services. One of the pressing concerns is the functional alignment of geospatial data with tourism-related data. Thus, we aim to pinpoint the growing importance of smart geospatial services, by pointing to the main downturn of the current smart destination issue with geospatial data resolutions, and, by building upon the relations of the geospatial layer of data with the tourism-specific layer. To this end, we pinpoint two further research directions - reinvestigating spatial and temporal resolution as a core of data smartness and the need for contextual (tourism-oriented) scaling of smart technology. This could be of keen interest in post-pandemic tourism, where smart geospatial services will be of pressing concern, but also it still an issue to be resolved in further smart destination development.

FAIR AND STANDARD ACCESS TO SPATIAL DATA AS THE MEANS FOR ACHIEVING SUSTAINABLE DEVELOPMENT GOALS

Abstract. FAIR, which stands for Findable, Accessible, Interoperable and Reusable, are the main principles adopted for sharing scientific data across communities. Implementing FAIR principles in publishing increases the value of digital resources, and the reuse of these by humans as well as machines. Introducing FAIR practices to the geospatial domain is especially relevant for the foundation geospatial data, such as precise positioning data. Within the next five years, Global Navigation Satellite Systems (GNSS), with corrections from internet or satellite communications, will permit national coverage of positioning services with real-time accuracy of several centimetres or better. However, implementing FAIR principles is not yet common practice in the geospatial domain. There are dozens of standards available for defining and sharing geospatial data. These include the ISO 19100 series of standards, OGC specifications and several community profiles and best practice. However, in most cases these standards fall short in ensuring the FAIR distribution of geospatial resources. As our preliminary findings show, current geodetic metadata and data are not yet fully FAIR and data discovery and access is still very challenging. In this paper we discuss the concept of FAIR and its meaning for geodetic data, explore the needs of precise positioning users and their requirement for metadata and present preliminary results on the FAIRness of current geodetic standards.

ACADEMIC TRACK OF FOSS4G 2019 BUCHAREST – THE ASYMPTOTIC CONNECTION BETWEEN SOFTWARE AND DATA: PREFACE

Abstract. FOSS4G stands for Free and Open Source Software for Geospatial. It is the flagship event of OSGeo. Each FOSS4G has its special aura, kindly designed by each Local Organising Committe, sharing the local culture and spirit with the greater community. In 2019, geo-spatial.org, the OSGeo Local Chapter of Romania, won the honour of organising the geospatial event of the year. FOSS4G 2019 was held in Bucharest (Romania), in three of the most important buildings of this city: National Theatre of Bucharest, InterContinental Hotel and Faculty of Geography from the University of Bucharest.Following the established tradition of FOSS4G conferences, at the 2019 edition, an Academic Track ran in parallel with the General Track. The main purpose of this track was to bring together researchers, teachers, developers, users and practitioners carrying out research activities in geospatial domains, with an emphasis on the open source solutions. All types of topics such as results achieved, case studies, work in progress, academic endeavours to conceptualize, assess or teach open source geospatial software and data, were welcomed. The Academic Committee discouraged prevalent presentations of technologies or use cases without properly justifying originality to the scientific state of the art, emphasizing on particular novelty.At this edition, 53 papers were submitted to the Academic Track. These were blind reviewed by 3 reviewers. Finally 38 scientific papers were selected for publication in this volume of the ISPRS Archives. The editors would like to thank all the authors, the members from the Scientific Committee and the Organizing Committee for their valuable contributions. We hope you enjoy reading the proceedings.

A Modeling and Measurement Approach for the Uncertainty of Features Extracted from Remote Sensing Images

The reliability of remote sensing (RS) image classification is crucial for applying RS image classification results. However, it has received minimal attention, especially the uncertainty of features extracted from RS images. The uncertainty of image features constantly accumulates, propagates, and ultimately affects the reliability and accuracy of image classification results. Thus, research on the quantitative modeling and measurement of the feature uncertainty of RS images is very necessary. To make up for the lack of research on quantitative modeling and measurement of uncertainty of image features, this study first investigates and summarizes the appearance characteristics of the feature uncertainty of RS images in geospatial and feature space domains based on the source and formation mechanisms of feature uncertainty. Then, a modeling and measurement approach for the uncertainty of image features is proposed on the basis of these characteristics. In this approach, a new Local Adaptive Multi-Feature Weighting Method based on Information Entropy and the Local Distribution Density of Points is proposed to model and measure the feature uncertainty of an image in the geospatial and feature space domains. In addition, a feature uncertainty index is also constructed to comprehensively describe and quantify the feature uncertainty, which can also be used to refine the classification map to improve its accuracy. Finally, we propose two effectiveness verification schemes in two perspectives, namely, statistical analysis and image classification, to verify the validity of the proposed approach. Experimental results on two real RS images confirm the validity of the proposed approach. Our study on the feature uncertainty of images may contribute to the development of uncertainty control methods or reliable classification schemes for RS images.

SDQO AND SFO, ONTOLOGIES FOR SPATIAL DATA QUALITY ASSESSMENT

Abstract. Spatial quality assessment is based on the conformance of data to its specifications or fitness for users’ purpose. These specifications and the users’ purposes include the rules and constraints that a dataset should comply with. Assessing the compliance of data to the rules is still an active research subject and rule-based approach is the common method. For the efficient rule-based system implementation, it is desired to automate assessment process with a domain-independent and web-based approach. Reasoning capability and re-usability of semantic web components are expected to promote efficient implementation. In literature, many domains such as agriculture, music, Linked Data and geospatial domain etc. apply ontology-based methods for quality management. There is a need to model geospatial quality concepts and rules in a domain-independent way to automate the quality management process. In our model of rule formalism, we use Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). We devise two types of ontologies. These are; the specification ontologies (SfO) and the Spatial Data Quality Ontology (SDQO). SfO is to be created by domain experts/users to define rules according to specifications. SDQO is responsible with quality assessment; it is domain independent and makes assessment based on the rules defined by any SfO for the related domain. The quality elements are domain and toposemantic consistency that assessed by SWRL. In this paper, the design considerations of the ontologies for quality assessment are explained with an example.

Fostering Data Sharing in Multidisciplinary Research Communities: A Case Study in the Geospatial Domain

The sharing of research data allows for information reuse and knowledge advancement but its realization is often a challenge and seldom successful in practice. We propose a workflow for the design of a User Support System (USS) aimed at tutoring research groups in data sharing by considering their social and domain backgrounds. Our engagement approach focuses on multidisciplinary geospatial research, particularly when interoperable data sharing is required. Specifically, we first characterize the research community on the basis of the behavior and competences in data management by its groups and then target the needs of the latter with specific facilities. We address for the first time in literature the issue of modeling research groups as targets of the USS and provide a roadmap to standardize USS activities across different communities. We describe the implementation of the workflow in the context of an Italian research project and we assess the impact of the USS in terms of increase in the number of nodes and resources in the project’s data infrastructure, and of fulfilment of the expectations by the research groups.

Machine learning for cross-gazetteer matching of natural features

ABSTRACTDefining and identifying duplicate records in a dataset is a challenging task which grows more complex when the modeled entities themselves are hard to delineate. In the geospatial domain, it may not be clear where a mountain, stream, or valley ends and begins, a problem carried over when such entities are catalogued in gazetteers. In this paper, we take two gazetteers, GeoNames and SwissNames3D, and perform matching – identifying records in each that are about the same entity – across a sample of natural feature records. We first perform rule-based matching, establishing competitive results, then apply machine learning using Random Forests, a method well-suited to the matching task. We report on the performance of a wider array of matching features than has been previously studied, including domain-specific ones such as feature type, land cover class, and elevation. Our results show an increase in performance using machine learning over rules, with a notable performance gain from considering feature types, but negligible gains from other specialized matching features. We argue that future work in this area should strive to be more reproducible and report results on a realistic testing pipeline including candidate selection, feature extraction, and classification.

Interlinking geospatial and building geometry with existing and developing standards on the web

Abstract Geometric data plays a central role in the geospatial domain, architectural design and construction industry. For upcoming, new approaches to store building data, such as the Semantic Web, no universal common agreement exists on the combination of geometric and non-geometric data. It can therefore be unclear to users on how to represent their geometries, leading to a decelerated application and advancement of making building data available over the web. This gap can only be bridged if a common approach on the representation of geometries on the web is achieved. To first generate a common understanding of geometry representations, an overview of existing and developing geometry (web) standards needs to be given and discussed, i.e., the Industry Foundation Classes (IFC), CityGML, GeoSPARQL, and the OntoBREP and GEOM ontologies. This discussion needs to consider general contexts, e.g., 2D, 3D, detailed, or tessellated geometries, and specific use cases of the construction industry. Based on these discussions, this paper aims to propose a general recommendation for web-based geometry representations to enhance future applications of building data on the web. Due to the variety of use cases and their requirements, as well as technical constraints based on deviant interpretations of geometry descriptions from different geometry kernels, it became clear, that no approach or standard is generally superior to others. The biggest distinction identified in this paper is posed between the context of visualizing, where simplified, tessellated geometry holds the highest advantage, and (parametric) modeling, which requires semantically detailed geometry representations. Hence, we recommend to interlink non-geometric data with multiple geometry representations, to address all relevant contexts and use cases appropriately. The individual geometry representations should be chosen based on the relevant use cases for an optimal experience when using and exchanging geometry on the web. With this recommendation, the benefits of all discussed approaches can be exploited while avoiding their respective challenges.

ANALYTICAL HIERARCHY PROCESS FOR IDENTIFICATION OF SUITABLE WATER HARVESTING SITE IN GEOSPATIAL ENVIRONMENT

Abstract. Water is the prime requirement for agriculture, domestic uses and industrial production. In India the per capita available of water is decreasing at a higher rate due to impact of climate change and ever-increasing population. Rainwater harvesting is the technique which is being used in effective storing of surface runoff. There are various types of water harvesting structures namely check dam, farm pond, percolation tank, etc. However, identification of potential site for the construction of the particular water harvesting structure is bit difficult as it depends upon numerous parameters such as soil type, slope, water availability, land use and land cover of the site and the surrounding, etc. The guidelines such as Food and Agriculture Organization; Integrated Mission for Sustainable Development are available for selecting suitable site for water harvesting structures. As the site suitability analysis involves multiple parameters for decision making, in the present study, the selection of suitable site for check dam is made through analytical hierarchy process (AHP) in geospatial domain for Hatni watershed, Madhya Pradesh, India. As location for check dam construction is influenced by soil texture, slope of the terrain, land use land cover, stream order and water availability; these parameters were derived from remote sensing data and analysed. The slope and stream network layers were generated from the digital elevation model (DEM). Further, the influence of soil and water availability in identifying the suitable sites was studied through soil texture and curve number. Different parameters influence the site suitability analysis, therefore, in the present study, IMSD guidelines were used to assign weights to each parameter under consideration. The layers were assigned weights by AHP technique based on pairwise comparison. The layers were reclassified according to the weights, then overlay analysis has been done to get the final site suitability map. As remote sensing provides the synoptic coverage of the earth, it has been further utilised to study the impact of water harvesting structure on its surrounding. The land use land cover map of before and after the construction of water harvesting structure was analysed for change in vegetation condition. It was observed that the vegetative cover has increased after implementation of the water conservation measures. It may be concluded that the geospatial technology has immense potential in site suitability studies for water harvesting structures.

News coverage, digital activism, and geographical saliency: A case study of refugee camps and volunteered geographical information.

The last several decades have witnessed a shift in the way in which news is delivered and consumed by users. With the growth and advancements in mobile technologies, the Internet, and Web 2.0 technologies users are not only consumers of news, but also producers of online content. This has resulted in a novel and highly participatory cyber-physical news awareness ecosystem that fosters digital activism, in which volunteers contribute content to online communities. While studies have examined the various components of this news awareness ecosystem, little is still known about how news media coverage (and in particular digital media) impacts digital activism. In order to address this challenge and develop a greater understanding of it, this paper focuses on a specific form of digital activism, that of the production of digital geographical content through crowdsourcing efforts. Using refugee camps from around the world as a case study, we examine the relationship between news coverage (via Google news), search trends (via Google trends) and user edit contribution patterns in OpenStreetMap, a prominent geospatial data crowdsourcing platform. In addition, we compare and contrast these patterns with user edit patterns in Wikipedia, a well-known non-geospatial crowdsourcing platform. Using Google news and Google trends to derive a measure of thematic public awareness, our findings indicate that digital activism bursts tend to take place during periods of sustained build-up of public awareness deficit or surplus. These findings are in line with two prominent mass communication theories: agenda setting and corrective action, and suggest the emergence of a novel stimulus-awareness-activism framework in today’s participatory digital age. Moreover, these findings further complement existing research examining the motivational factors that drive users to contribute to online collaborative communities. This paper brings us one step closer to understanding the underlying mechanisms that drive digital activism in particular in the geospatial domain.

ENRICHMENT AND POPULATION OF A GEOSPATIAL ONTOLOGY FOR SEMANTIC INFORMATION EXTRACTION

Abstract. The massive amount of user-generated content available today presents a new challenge for the geospatial domain and a great opportunity to delve into linguistic, semantic, and cognitive aspects of geographic information. Ontology-based information extraction is a new, prominent field in which a domain ontology guides the extraction process and the identification of pre-defined concepts, properties, and instances from natural language texts. The paper describes an approach for enriching and populating a geospatial ontology using both a top-down and a bottom-up approach in order to enable semantic information extraction. The top-down approach is applied in order to incorporate knowledge from existing ontologies. The bottom-up approach is applied in order to enrich and populate the geospatial ontology with semantic information (concepts, relations, and instances) extracted from domain-specific web content.

Raising Semantics-Awareness in Geospatial Metadata Management

Geospatial metadata are often encoded in formats that either are not aimed at efficient retrieval of resources or are plainly outdated. Particularly, the quantum leap represented by the Semantic Web did not induce so far a consistent, interlinked baseline in the geospatial domain. Datasets, scientific literature related to them, and ultimately the researchers behind these products are only loosely connected; the corresponding metadata intelligible only to humans, duplicated in different systems, seldom consistently. We address these issues by relating metadata items to resources that represent keywords, institutes, researchers, toponyms, and virtually any RDF data structure made available over the Web via SPARQL endpoints. Essentially, our methodology fosters delegated metadata management as the entities referred to in metadata are independent, decentralized data structures with their own life cycle. Our example implementation of delegated metadata envisages: (i) editing via customizable web-based forms (including injection of semantic information); (ii) encoding of records in any XML metadata schema; and (iii) translation into RDF. Among the semantics-aware features that this practice enables, we present a worked-out example focusing on automatic update of metadata descriptions. Our approach, demonstrated in the context of INSPIRE metadata (the ISO 19115/19119 profile eliciting integration of European geospatial resources) is also applicable to a broad range of metadata standards, including non-geospatial ones.

Adaptive Pareto-based approach for geo-ontology matching

• Geo-ontology matching for achieving interoperability in geo-spatial domain. • Multiple similarity measures for matching various entities of geo-ontologies. • Pareto optimization used to optimally combine similarity measures. • Adaptive Pareto ranking approach for geo-ontology matching. • Performance comparison of Adaptive Pareto ranking with existing approaches.

ChronosDB

An array DBMS streamlines large N -d array management. A large portion of such arrays originates from the geospatial domain. The arrays often natively come as raster files while standalone command line tools are one of the most popular ways for processing these files. Decades of development and feedback resulted in numerous feature-rich, elaborate, free and quality-assured tools optimized mostly for a single machine. ChronosDB partially delegates in situ data processing to such tools and offers a formal N -d array data model to abstract from the files and the tools. ChronosDB readily provides a rich collection of array operations at scale and outperforms SciDB by up to 75× on average.

Assessing the usability of the NDCDB checklist with Systematic Usability Scale (SUS)

This paper explains the process of assessing the usability of the National Digital Cadastral Database (NDCDB) Checklist; which was developed in a more extensive research exercise with the aim to investigate the significance and usability of NDCDB for the use of multiple spatial land-based analysis. System Usability Scale or SUS was used to determine the perceived usability of the NDCDB Checklist developed to aid users to correctly optimise the National Digital Cadastral Database or NDCDB. Eighteen participants from relevant geospatial domains were invited to test the usability of the checklist through a remote asynchronous testing. The overall SUS score of the NDCDB Checklist was 75.1, which is defined as “Acceptable” under the SUS acceptability range. The SUS score was then extracted to identify additional information of Learnability and Usability components of which both resulted in 68.8 and 76.7 respectively. The SUS score indicates that the Usability component of the NDCDB Checklist to be acceptable while the Learnability component to be marginally acceptable. As a result, the NDCDB Checklist is considered fit and appropriate to be used by stakeholders, with high usability and marginal learnability in aiding land-based spatial analysis. The benefit of the NDCDB Checklist will encourage proper utilisation of the NDCDB for other spatial analysis aside land-based spatial analysis.

News Coverage, Digital Activism, and Geographical Saliency: A Case Study of Refugee Camps and Volunteered Geographical Information

The last several decades have witnessed a shift in the way in which news is delivered and consumed by users. With the growth and advancements in mobile technologies, the Internet, and Web 2.0 technologies users are not only consumers of news, but also producers of online content. This has resulted in a novel and highly participatory cyber-physical news awareness ecosystem that fosters digital activism, in which volunteers contribute content to online communities. While studies have examined the various components of this news awareness ecosystem, little is still known about how news media coverage (and in particular digital media) impacts digital activism. In order to address this challenge and develop a greater understanding of it, this paper focuses on a specific form of digital activism, that of the production of digital geographical content through crowdsourcing efforts. Using refugee camps from around the world as a case study, we examine the relationship between news coverage (via Google news), search trends (via Google trends) and user edit contribution patterns in OpenStreetMap, a prominent geospatial data crowdsourcing platform. In addition, we compare and contrast these patterns with user edit patterns in Wikipedia, a well-known non-geospatial crowdsourcing platform. Using Google news and Google trends to derive a measure of thematic public awareness, our findings indicate that digital activism bursts tend to take place during periods of sustained build-up of public awareness deficit or surplus. These findings are in line with two prominent mass communication theories: agenda setting and corrective action, and suggest the emergence of a novel stimulus-awareness-activism framework in today’s participatory digital age. Moreover, these findings further complement existing research examining the motivational factors that drive users to contribute to online collaborative communities. This paper brings us one step closer to understanding the underlying mechanisms that drive digital activism in particular in the geospatial domain.

The Evolution and Structure of Extreme Optical Lightning Flashes.

This study documents the composition, morphology, and motion of extreme optical lightning flashes observed by the Lightning Imaging Sensor (LIS). The furthest separation of LIS events (groups) in any flash is 135 km (89 km), the flash with the largest footprint had an illuminated area of 10,604 km2, and the most dendritic flash has 234 visible branches. The longest-duration convective LIS flash lasted 28 s and is overgrouped and not physical. The longest-duration convective-to-stratiform propagating flash lasted 7.4 s, while the longest-duration entirely stratiform flash lasted 4.3 s. The longest series of nearly consecutive groups in time lasted 242 ms. The most radiant recorded LIS group (i.e., "superbolt") is 735 times more radiant than the average group. Factors that impact these optical measures of flash morphology and evolution are discussed. While it is apparent that LIS can record the horizontal development of the lightning channel in some cases, radiative transfer within the cloud limits the flash extent and level of detail measured from orbit. These analyses nonetheless suggest that lightning imagers such as LIS and Geostationary Lightning Mapper can complement ground-based lightning locating systems for studying physical lightning phenomena across large geospatial domains.

Transdisciplinary Foundations of Geospatial Data Science

Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understanding of the scientific reasoning behind data mining and machine learning approaches. This requires taking a transdisciplinary view of data science and recognizing its foundations in mathematics, statistics, and computer science. Focusing on the geospatial domain, we apply this crucial transdisciplinary perspective to five common geospatial techniques (hotspot detection, colocation detection, prediction, outlier detection and teleconnection detection). We also describe challenges and opportunities for future advancement.