Geospatial Knowledge Research Articles

Representing Geospatial Knowledge in Narratives

This paper explores the representation of geospatial knowledge within narratives through a Semantic Web approach. We introduce the NOnt+Space (NOnt+S) model, an extension of the CIDOC CRM-based Narrative Ontology, which allows the representation of narratives and their geospatial aspects. By leveraging standards such as CRMgeo and GeoSPARQL, NOnt+S ensures systematic and interoperable geospatial representation in narratives, enabling geospatial queries on knowledge graphs. We present an assessment of NOnt+S utilising data from the H2020 MOVING European project (2021-2024), which collected knowledge about European mountain value chains intended as Cultural Heritage. We have represented this knowledge as geospatial narratives using NOnt+S. GeoSPARQL queries and semantic reasoning applied to the created KG reveal the ontology ability to infer new geospatial knowledge. Our work contributes to the ongoing efforts in the Semantic Web community to integrate and represent geospatial information within narratives, promoting collaboration and interoperability across various scientific domains.

Методологическое обоснование содержания, структуры и программной реализации базы геопространственных знаний территориальных образований

The article deals with substantiating the contents, structure and software implementation of the geospatial knowledge base. The importance and relevance of forming its infrastructure and the corresponding informational base on the territory in modern geoinformatics and cartography are noted. The requirements for the contents, formal and logical representation of the mentioned base, its structure and connections of the elements at the logical level are given. The authors consider the software implementation of the processes of searching and issuing geospatial knowledge in a visualized form at the request of untrained users in the field of geoinformatics and cartography. The requisitions and information on the special original software, which allows observing the contents of the geospatial knowledge base, are shown. The effectiveness of visualizing the geospatial knowledge base through interactive configuration of software elements is substantiated, based on the metadata principle proposed by the authors, two formalisms and an algorithm for creating corresponding services for geospatial knowledge and other related data. These formalisms of the client’s parameters and server software applications are described, a variant of the geospatial knowledge base structure is given, and the requirements for the necessary software for the effective solution of the task are formulated. The authors` solution is considered in the form of a complex of multicomponent software, used at working with geodata and creating their own interactive maps by poorly trained non-professional users

Development of geotechnical properties geo-database for soil in Kano metropolis to enhance building construction

Building collapse is a prevalent and concerning issue that resulted in significant loss of lives and properties in many places in Nigeria including Kano state and determination of Geotechnical properties of soil could play a crucial role in preventing building collapse. This study uses Geographic Information System (GIS) methodologies to create a geospatial database of soil properties in Kano Metropolis. The database, created using Inverse Distance Weighting (IDW) and spatial interpolation techniques, provides 212 sets of soil properties being meticulously mapped across the study area. These meticulously generated maps illustrate various soil characteristics prevalent in the metropolis, offering invaluable insights for preliminary designs and construction planning for buildings within Kano Metropolis. The soils in this area are predominantly classified as CL (Clay of low plasticity), SC (Sandy Clay), and SM (Silty Sand) using Unified Soil Classification System (USCS). The moisture content spans from 2.01 to 46%, specific gravity within the range of 2.32 to 2.75, liquid limits varying between 17.2 and 45%, plastic limits within the range of 10.1 to 40.5%, and linear shrinkage values spanning from 1 to 13%. Furthermore, the shear strength parameters of the soil across the study area varies. The unit weight varies between 15.61 and 22.16 kN/ m3 , the cohesion (c) values ranging from 1.28 to 29.1 kN/m2 , and the angle of internal friction (ϕ) spanning from 5.5° to 32.2°. Despite variations in ultimate and allowable bearing capacity with depth, the ultimate bearing capacity (qult) generally ranges between 108 and 1150 kN/m2 , with the corresponding allowable bearing capacity (qall) falling between 40 and 343 kN/m2 . This research provides valuable geospatial knowledge to engineers, architects, and construction professionals in Kano Metropolis, enhancing decision-making and project planning precision, ultimately contributing to sustainable development and growth of Kano metropolis.

Rapid Production Method of Massive Thematic Maps Based on Geospatial Knowledge Extraction

<p>Geospatial knowledge in massive academic papers can provide knowledge services such as location-based research hotspot analysis, spatio-temporal data aggregation, research results recommendation, etc. However, geospatial knowledge often exists implicitly in literature resources in unstructured form, which is difficult to be directly accessed and mined and utilized for rapid production of massive thematic maps. In this paper, we take the geospatial knowledge of the area studied as an example and introduce its extraction method in detail. An integrated feature template matching and random forest classification algorithm is proposed for accurately identifying research areas from the abstract texts of academic papers and producing thematic maps. Firstly, the precise recognition of geographical names is achieved step by step based on BiLSTM-CRF algorithm and improved heuristic disambiguation method; then, the area studied is extracted by the designed integrated feature recognition template of area studied using random forest classification algorithm, and a fast thematic map is designed for the knowledge of area studied, topic and literature. The experimental results show that the area studied recognition accuracy can reach 97%, the F-value is 96%, and the recall rate reaches 96%, achieving high accuracy and high efficiency of area studied extraction in text. Based on the geospatial knowledge, the thematic map can achieve the effect of fast map formation and accurate expression.</p> <p> </p>

Construct and Query A Fine-Grained Geospatial Knowledge Graph

In this paper, we propose the fine-grained geospatial knowledge graph (FineGeoKG), which can capture the neighboring relations between geospatial objects. We call such neighboring relations strong geospatial relations (SGRs) and define six types of SGRs. In FineGeoKG, the vertices (or entities) are geospatial objects. The edges (or relations) can have “sgr” labels together with properties, which are used to quantify SGRs in both topological and directional aspects. FineGeoKG is different from WorldKG, Yago2Geo, and other existing geospatial knowledge graphs, since its edges can capture the spatial coherence among geospatial objects. To construct FineGeoKG efficiently, the crucial problem is to find out SGRs. We improve the existing geospatial interlinking algorithm in order to find out SGRs faster. To answer SGR queries efficiently, we design an index to organize the SGR edges and improve the binary join method for subgraph matching. We conduct experiments on the real datasets and the experimental results show that the proposed algorithm is more efficient than the baseline algorithms. We also demonstrate the usefulness of FineGeoKG by presenting the results of complicated spatial queries which focus on structural and semantic information. Such queries can help researchers (for example, ecologists) find groups of objects following specific spatial patterns.

Развитие геопространственной деятельности в России: стратегические направления и первоочередные задачи

Scientific literature and official documents on the development of geospatial activities are analyzed in the paper. Arguments confirming that such activities is one of the key drivers of national socio-economic development are given. Based on the analysis of domestic profile agencies` scope the assessment of their readiness to create and mass use of geospatial knowledge infrastructure in many areas of economy is provided. The authors identified strategic directions for the development of geospatial activities in Russia, formulated priority tasks for the transformation of such industry and creation the national geospatial ecosystem that contributes to widespread operating spatial data in the economy and state security. The topics of breakthrough R and Ds on the creation and development of intelligent solutions and technologies for processing information that forms the corresponding competencies are proposed. The goals aimed at upgrading the system of training highly qualified personnel and measures to improve geospatial activities in the Russian Federation are listed

A hypergraph-based hybrid graph convolutional network for intracity human activity intensity prediction and geographic relationship interpretation

Human activity intensity prediction, i.e., estimating the dynamic population distribution, is crucial to many location-based applications, particularly intelligent transportation systems and urban planning. Geographical artificial intelligence (GeoAI) is an emerging field that has contributed many new modeling approaches for spatiotemporal phenomena, especially spatiotemporal graph neural networks (ST-GNNs). However, there are two challenges to the prediction of human activity intensity. On the one hand, accurate predictions require modeling not only pairwise spatial relationships, e.g., distance and spatial flow but also complex high-order relationships, such as clusters and groups. On the other hand, it is not appropriate to incorporate prior knowledge on the spatial structure of data with subjective assumptions using graphs with predefined topologies or weights. To mitigate such challenges, a novel deep learning-based model was proposed, namely, the Hypergraph-based Hybrid Graph Convolutional Network (HyGCN). In our study, high-order relationships are modeled by hypergraph convolutions, and pairwise relationships are captured in graph convolutions. A spatial fusion layer is added to fuse the pairwise and high-order relationships. Then, adaptive graph and hypergraph generation methods are proposed to interpret edge weights and understand the learned geographic relationships in accordance with principles. After systematic experiments on large-scale mobility data, our results demonstrate that HyGCN outperforms state-of-the-art models. The learned geospatial knowledge can be disentangled and explained from the perspectives of geographic principles. This study offers new insights for not only revisiting geographic laws in GeoAI but also utilizing spatiotemporal knowledge in deep learning models.

Adaptive scalable spatio-temporal graph convolutional network for PM2.5 prediction

PM2.5 Prediction is a complex task of large-scale spatio-temporal analysis, which not only needs comprehension of static geospatial knowledge and relative features but also needs to analyze the real-time situation. This paper discusses the characteristics of the static graph and the dynamic graph in spatio-temporal series tasks. An Adaptive Scalable Spatio-temporal Graph Convolutional Network(ASGCN) model is proposed to predict PM2.5. To capture and analyze the characteristics of the time series period of PM2.5, a time convolution network based on the strategies of inception and gating is proposed and used as a temporal module. A dynamic graph idea is adopted to distinguish the spatio-temporal similarity of different periods. And an adaptive weighted multilayer graph convolution network is used to process static and dynamic graphs, aiming to analyze the spatial relationship of PM2.5 stations. The convolution network with the inception and gating improves the time-series feature capture ability, and adaptive static and dynamic graphs enhance the spatial relationship analysis ability. The temporal and spatial modules of the model are relatively independent, which benefits obtaining the potential information of datasets to improve the prediction accuracy. At the same time, these modules cooperate to make the model adaptable to various data. We choose a great number of comparative models and design a thorough experimental scheme including single-step prediction, multi-step prediction, hyperparameter experiments, and ablation experiments on two real PM2.5 datasets collected in China. Finally, the model achieves performance close to or better than the current state-of-the-art models selected for comparison in prediction tasks.

Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning

Detecting impact craters on the Martian surface is a critical component of studying Martian geomorphology and planetary evolution. Accurately determining impact crater boundaries, which are distinguishable geomorphic units, is important work in geological and geomorphological mapping. The Martian topography is more complex than that of the Moon, making the accurate detection of impact crater boundaries challenging. Currently, most techniques concentrate on replacing impact craters with circles or points. Accurate boundaries are more challenging to identify than simple circles. Therefore, a boundary delineator for Martian crater instances (BDMCI) using fusion data is proposed. First, the optical image, digital elevation model (DEM), and slope of elevation difference after filling the DEM (called slope of EL_Diff to highlight the boundaries of craters) were used in combination. Second, a benchmark dataset with annotations for accurate impact crater boundaries was created, and sample regions were chosen using prior geospatial knowledge and an optimization strategy for the proposed BDMCI framework. Third, the multiple models were fused to train at various scales using deep learning. To repair patch junction fractures, several postprocessing methods were devised. The proposed BDMCI framework was also used to expand the catalog of Martian impact craters between 65°S and 65°N. This study provides a reference for identifying terrain features and demonstrates the potential of deep learning algorithms in planetary science research.

From Maps to Geospatial Knowledge Graph: Geospatial Knowledge Representation and Reasoning

From Maps to Geospatial Knowledge Graph: Geospatial Knowledge Representation and Reasoning

ENABLING KNOWLEDGE SHARING BY MANAGING DEPENDENCIES AND INTEROPERABILITY BETWEEN INTERLINKED SPATIAL KNOWLEDGE GRAPHS

Abstract. Knowledge sharing is increasingly being recognized as necessary to address societal, economic, environmental, and public health challenges. This often requires collaboration between federal, local, and tribal governments along with the private sector, nonprofit organizations, and institutions of higher education. To achieve this, there needs to be a move away from data-centric to knowledge sharing architectures, such as a Geospatial Knowledge Infrastructure (GKI). Data from multiple organizations need to be properly contextualized in both space and time to support geographically based planning, decision making, cooperation and coordination. A spatial knowledge graph (SKG) is a useful paradigm for facilitating knowledge sharing and collaboration. However, interoperability between independently developed SKGs from different organizations that reference the same geographies is often not automated in a machine-readable way due to a lack of standardization. This paper outlines an architecture that automates interoperability and dependency management between SKGs as they are formally published by version and period of validity. We are calling this approach a spatial knowledge mesh (SKM), as it is a specialization of the data mesh architecture along with the concept of a common geo-registry to facilitate knowledge sharing more easily. The initial implementation, called GeoPrism Registry, is being developed as an open-source spatial knowledge infrastructure as a platform to help countries meet their NSDI and GKI objectives. It was fist funded and deployed to support ministries of health and is more recently being utilized in GeoPlatform.gov.

Mining Geospatial Relationships from Text

A geospatial Knowledge Graph (KG) is a heterogeneous information network, capable of representing relationships between spatial entities in a machine-interpretable format, and has tremendous applications in logistics and social networks. Existing efforts to build a geospatial KG, have mainly used sparse spatial relationships, e.g., a district located inside a city, which provide only marginal benefits compared to a traditional database. In spite of the substantial advances in the tasks of link prediction and knowledge graph completion, identifying geospatial relationships remains challenging, particularly due to the fact that spatial entities are represented with single-point geometries, and textual attributes are frequently missing. In this study, we present GTMiner, a novel framework capable of jointly modeling Geospatial and Textual information to construct a knowledge graph, by mining three useful spatial relationships from a geospatial database, in an end-to-end fashion. The system is divided into three components: (1) a Candidate Selection module, to efficiently select a small number of candidate pairs; (2) a Relation Prediction component to predict spatial relationships between the entities; (3) a KG Refinement procedure, to improve both coverage and correctness of a geospatial knowledge graph. We carry out experiments on four cities' geospatial databases, from publicly-available sources and compare with existing algorithms for link prediction and geospatial data integration. Finally, we conduct an ablation study to motivate our design choices and an efficiency analysis to show that the time required by GTMiner for training and inference is comparable, or even shorter, than existing solutions.

EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

AbstractOver the past decade, the electric vehicle (EV) industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV‐centric knowledge graph (EVKG) as a comprehensive, cross‐domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV‐related knowledge, including EV adoption, EV supply equipment, and electricity transmission network, to support decision‐making related to EV technology development, infrastructure planning, and policy‐making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV‐relevant ontology modules from existing well‐known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision‐making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV‐related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV‐related knowledge into a single, easily accessible resource, the EVKG supports decision‐makers in making informed choices about EV technology development, infrastructure planning, and policy‐making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape.

Geocognitive mapping technology

The author of the article considers the need to develop a new direction in cartography due to the fact that human civilization has reached the threshold of a fundamentally new era of the existence of the economy and society — the era of knowledge. Adoption of effective management and project decisions in such areas as spatial (territorial) development of the Russian Federation, ensuring the defense and security of the country, cadastral registration of real estate objects, preservation of objects of historical and cultural heritage, environmental protection, use of natural resources, development of the domestic tourism industry, the design and construction of buildings and structures, science, education, etc., is impossible without the creation of modern cartographic software. The article discusses the process of creating and using cartographic products containing geospatial knowledge. The approach of thematic mapping considered by the author on the basis of geospatial knowledge of certain subject areas is presented by a set-theoretic description. The process of geocognitive mapping is represented by a technological scheme. A feature of the geocognitive mapping process is the use in the combination of professional knowledge in the fields of thematic cartography and each of the subject areas to be represented in thematic geospatial models and maps. On the basis of the above studies, the following methodological principle of mapping based on geoscience can be formulated: geospatial knowledge displayed on the map about a particular subject area is a set of information about the objects of this subject area, their properties and the relationships connecting them, the processes taking place in this subject area, as well as methods of analysis emerging situations and ways of solving certain problems this subject area by developing spatial solutions.

Application of geospatial and remote sensing data to support locust management

Negative impacts on agricultural activities by different locust species are well documented and have always been one of the major threats to food security and livelihoods, especially for local communities. Locust management and control have led to less frequent and intense plagues and outbreaks worldwide. However, political insecurity and armed conflicts affect locust management, and can as well as changing climate, and land use management contribute to new outbreaks. In the context of the increasing world population and higher demand for agricultural production, locust pests will remain of high concern. Geospatial and remote sensing data have become an important source of information for different applications within locust research and management. However, there is still a gap between available information and actual practical usage. In this study, we demonstrate the importance of geospatial and remote sensing data and how this information can be prepared for a straightforward application for stakeholders. For this purpose, we use the h3-hexagonal hierarchical geospatial indexing system to simplify and structure spatial information into standardized hexagon units. The presented concept provides decision makers and ground teams with a simplified information database that contains area-wide information over time and space and can be used without detailed geospatial knowledge and background. The concept is designed for the use case of Italian locust management in the Pavlodar region (Kazakhstan) and based on actual practices. It can be extrapolated to any other study area or species of interest. Our results underline the importance of actual land management on locust presence. Up-to-date land management information can be derived from time-series analyses of remote sensing data. Furthermore, essential meteorological data are used to generate locust-specific climatic characteristics within the h3-system. Within this system, areal prioritizing for locust management can be achieved based on the included spatial information and experience from ongoing practices.

Semantic 3D city interfaces—Intelligent interactions on dynamic geospatial knowledge graphs

Abstract This article presents a system architecture and a set of interfaces that can build scalable information systems capable of large city modeling based on dynamic geospatial knowledge graphs to avoid pitfalls of Web 2.0 applications while blending artificial and human intelligence during the knowledge enhancement processes. We designed and developed a GeoSpatial Processor, an SQL2SPARQL Transformer, and a geospatial tiles ordering tasks and integrated them into a City Export Agent to visualize and interact with city models on an augmented 3D web client. We designed a Thematic Surface Discovery Agent to automatically upgrade the model’s level of detail to interact with thematic parts of city objects by other agents. We developed a City Information Agent to help retrieve contextual information, provide data concerning city regulations, and work with a City Energy Analyst Agent that automatically estimates the energy demands for city model members. We designed a Distance Agent to track the interactions with the model members on the web, calculate distances between objects of interest, and add new knowledge to the Cities Knowledge Graph. The logical foundations and CityGML-based conceptual schema used to describe cities in terms of the OntoCityGML ontology, together with the system of intelligent autonomous agents based on the J-Park Simulator Agent Framework, make such systems capable of assessing and maintaining ground truths with certainty. This new era of GeoWeb 2.5 systems lowers the risk of deliberate misinformation within geography web systems used for modeling critical infrastructures.

Symbolic and subsymbolic GeoAI: Geospatial knowledge graphs and spatially explicit machine learning

Transactions in GISVolume 26, Issue 8 p. 3118-3124 GUEST EDITORIAL Symbolic and subsymbolic GeoAI: Geospatial knowledge graphs and spatially explicit machine learning Gengchen Mai, Corresponding Author Gengchen Mai gengc[email protected] orcid.org/0000-0002-7818-7309 Spatially Explicit Artificial Intelligence Lab, Department of Geography, University of Georgia, Athens, Georgia, USA Correspondence Gengchen Mai, Spatially Explicit Artificial Intelligence Lab, Department of Geography, University of Georgia, Athens, GA 30605, USA. Email: [email protected]Search for more papers by this authorYingjie Hu, Yingjie Hu orcid.org/0000-0002-5515-4125 GeoAI Lab, Department of Geography, University at Buffalo, Buffalo, New York, USASearch for more papers by this authorSong Gao, Song Gao orcid.org/0000-0003-4359-6302 GeoDS Lab, Department of Geography, University of Wisconsin-Madison, Madison, Wisconsin, USASearch for more papers by this authorLing Cai, Ling Cai orcid.org/0000-0001-7106-4907 Center for Spatial Studies, University of California, Santa Barbara, Santa Barbara, California, USASearch for more papers by this authorBruno Martins, Bruno Martins orcid.org/0000-0002-3856-2936 Instituto Superior Técnico and INESC-ID, University of Lisbon, Lisbon, PortugalSearch for more papers by this authorJohannes Scholz, Johannes Scholz orcid.org/0000-0002-3212-8864 Institute of Geodesy, Graz University of Technology, Graz, AustriaSearch for more papers by this authorJing Gao, Jing Gao orcid.org/0000-0003-1778-8909 Department of Geography and Spatial Sciences, University of Delaware, Newark, Delaware, USASearch for more papers by this authorKrzysztof Janowicz, Krzysztof Janowicz Center for Spatial Studies, University of California, Santa Barbara, Santa Barbara, California, USA Department of Geography and Regional Research, University of Vienna, Vienna, AustriaSearch for more papers by this author Gengchen Mai, Corresponding Author Gengchen Mai [email protected] orcid.org/0000-0002-7818-7309 Spatially Explicit Artificial Intelligence Lab, Department of Geography, University of Georgia, Athens, Georgia, USA Correspondence Gengchen Mai, Spatially Explicit Artificial Intelligence Lab, Department of Geography, University of Georgia, Athens, GA 30605, USA. Email: [email protected]Search for more papers by this authorYingjie Hu, Yingjie Hu orcid.org/0000-0002-5515-4125 GeoAI Lab, Department of Geography, University at Buffalo, Buffalo, New York, USASearch for more papers by this authorSong Gao, Song Gao orcid.org/0000-0003-4359-6302 GeoDS Lab, Department of Geography, University of Wisconsin-Madison, Madison, Wisconsin, USASearch for more papers by this authorLing Cai, Ling Cai orcid.org/0000-0001-7106-4907 Center for Spatial Studies, University of California, Santa Barbara, Santa Barbara, California, USASearch for more papers by this authorBruno Martins, Bruno Martins orcid.org/0000-0002-3856-2936 Instituto Superior Técnico and INESC-ID, University of Lisbon, Lisbon, PortugalSearch for more papers by this authorJohannes Scholz, Johannes Scholz orcid.org/0000-0002-3212-8864 Institute of Geodesy, Graz University of Technology, Graz, AustriaSearch for more papers by this authorJing Gao, Jing Gao orcid.org/0000-0003-1778-8909 Department of Geography and Spatial Sciences, University of Delaware, Newark, Delaware, USASearch for more papers by this authorKrzysztof Janowicz, Krzysztof Janowicz Center for Spatial Studies, University of California, Santa Barbara, Santa Barbara, California, USA Department of Geography and Regional Research, University of Vienna, Vienna, AustriaSearch for more papers by this author First published: 18 December 2022 https://doi.org/10.1111/tgis.13012Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume26, Issue8December 2022Pages 3118-3124 RelatedInformation

GeoKG'2022 Workshop Report: The 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs

The topic of knowledge graphs (KGs) has recently attracted extensive attention in both industry and academia. Knowledge graphs are a new paradigm for representing, retrieving, integrating, and reasoning data from highly heterogeneous and multimodal sources. For example, international conferences, such as the Knowledge Graph Conference, have emerged in the past years, not to mention the increasing number of specialized workshops on KGs co-located with major computer science conferences, including Knowledge Graph Workshop at KDD 2021, Workshop for Deep Learning in Knowledge Graphs at ISWC 2021, Workshop on Knowledge Graph Construction at ESWC 2021, to name but a few. Also, the number of KGs-related papers accepted at these major conferences, including SIGSPATIAL, is rapidly increasing. Meanwhile, we also witness the increasing popularity of knowledge graph technologies in geography, geoinformatics, and GIScience domains. There are growing numbers of knowledge graph-related manuscripts accepted to the top geospatial-related venues, such as the International Journal of Geographical Information Science, Transactions in GIS, the International Journal of Applied Earth Observation and Geoinformation, and so on. Transactions in GIS also held two special issues about knowledge graphs: 1) Symbolic and Subsymbolic GeoAI: Geospatial Knowledge Graphs and Spatially Explicit Machine Learning [4], and 2) Knowledge-based GIS (K-GIS): Theories, Techniques and Applications. In addition, government agencies, industries, and non-governmental organizations (NGOs) are also lifting resources on topics of exploring KGs to build up interdisciplinary science and applications. One example is NSF's Accelerate Convergence Program, which promotes the idea of building an Open Knowledge Network to harness the data revolution. Funded by this program, the KnowWhereGraph is presently among the largest geo-enabled knowledge graphs, which integrates 28 different data layers at the intersection between humans and their environment [2].

KSTAGE: A knowledge-guided spatial-temporal attention graph learning network for crop yield prediction

Accurate and timely crop yield prediction is difficult to achieve due to the nonlinear and dynamic spatial–temporal correlations included during the crop growth process. The latest approaches utilize deep learning techniques to implicitly capture sequential dependencies but fail to consider prior knowledge. In this paper, a novel Knowledge-guided Spatial–Temporal Attention Graph Network (KSTAGE), which incorporates prior knowledge to model dynamic temporal relationships and geospatial topology structures, is proposed. The proposed KSTAGE first utilizes a 3D convolutional neural network (CNN) to embed the initial spectral feature. Following its development, a new Knowledge-guided Temporal Multi-head Attention Algorithm (KTMA), the core of KSTAGE, is applied to automatically generate temporal attention weights from a self-attention mechanism under the guidance of prior knowledge. Furthermore, a new strategy for aligning self-attention scores with the prior distribution is developed. Finally, a location-aware Spatial Attention Graph Network utilizing geospatial knowledge is proposed to aggregate the spatial neighborhood features for the final yield prediction. The experimental results obtained on county-level yield prediction tasks in China and the contiguous United States (CONUS) demonstrate that the proposed KSTAGE achieves obvious improvements over the baselines.

Virtual Fence Geospatial Intelligence to Protect Enterprise Cloud Server Data

Abstract: Data sharing and access area unit capabilities businesses and organizations need the foremost currently. Remote operating and mobile access to resources and collaboration platforms created it easier to access knowledge and resources from anyplace, anytime. Employees need to access documents and email from completely different devices, and from varied locations at a time. Access from untrusted networks is usually a threat to businesses. This may lead to knowledge loss and overexposure of essential knowledge. To mitigate the deficiencies of logical security mechanisms, and coinciding with the trend of cyber-physical systems, security mechanisms are planned that integrate with the physical surroundings. to make sure that business’s knowledge and resources area unit safe. In this project we tend to propose AN innovative Virtual Fence that uses a location knowledge and geospatial intelligence. Geospatial knowledge analysis enhances understanding, insight, decision-making, and prediction. Location intelligence (LI) is achieved via image and analysis of geospatial knowledge. Then we tend to improve the safety of information access in knowledge Server for an organization or the other specific locations exploitation the location-based cryptosystem. Virtual Fence provides a way to secure sensitive data among a company. It are often set to Off, On, Restricted read or browse solely. Once a geo-fenced boundary is outlined, the opportunities what businesses will do is proscribed by solely their creativeness. The most advantage of fitting such a geo fence is in avoiding knowledge discharge. Once outlined the trusty network locations, nobody will access knowledge from a unique network location/device. The experiment shows that our theme is possible in sensible applications.