An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study

Li, Wenwen; Zhou, Xingxing; Wu, Sheng

doi:10.3390/ijgi5100179

Cited by 12 publications

(11 citation statements)

References 36 publications

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“…where d is distance between pixel centers and z i is the height at the location i (Figure 3a). In other words, if we calculate a slope value from the elevation values presented in Figure 3a, the rate of change in the south-north direction for cell z 5 can be calculated using Equation (1) and the rate of change in the east-west direction for cell z 5 can be calculated using Equation (2). a b Then these values are converted to a reflectance value using an appropriate reflectance map (R) [46]:…”

Section: Shaded Relief Computationmentioning

confidence: 99%

“…Detection of terrain features plays a key role in learning about land surface, geologic structures, and geomorphological processes. Terrain features are also utilized in several spatial applications such as land control [1], land use [2], hazard management [3], hydrology [4], and environmental setting analysis [5]. One of the early terrain feature detection methods has been field investigation.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

Syzdykbayev

Karimi

2020

Remote Sensing

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Detection of terrain features (ridges, spurs, cliffs, and peaks) is a basic research topic in digital elevation model (DEM) analysis and is essential for learning about factors that influence terrain surfaces, such as geologic structures and geomorphologic processes. Detection of terrain features based on general geomorphometry is challenging and has a high degree of uncertainty, mostly due to a variety of controlling factors on surface evolution in different regions. Currently, there are different computational techniques for obtaining detailed information about terrain features using DEM analysis. One of the most common techniques is numerically identifying or classifying terrain elements where regional topologies of the land surface are constructed by using DEMs or by combining derivatives of DEM. The main drawbacks of these techniques are that they cannot differentiate between ridges, spurs, and cliffs, or result in a high degree of false positives when detecting spur lines. In this paper, we propose a new method for automatically detecting terrain features such as ridges, spurs, cliffs, and peaks, using shaded relief by controlling altitude and azimuth of illumination sources on both smooth and rough surfaces. In our proposed method, we use edge detection filters based on azimuth angle on shaded relief to identify specific terrain features. Results show that the proposed method performs similar to or in some cases better (when detecting spurs than current terrain features detection methods, such as geomorphon, curvature, and probabilistic methods.

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Section: Shaded Relief Computationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

Syzdykbayev

Karimi

2020

Remote Sensing

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“…In this work, the GeoSPARQL ontology including spatial information, the fluent ontology for temporal information and the domain ontology that stored knowledge and contextual information related to the geospatial environment, has been combined allowing to represent and to reason about spatio-temporal dynamics. More recently, Li et al [51] have implemented an integrated computational framework to support semantic modeling and reasoning about spatio-temporal change of geographical objects in land use/cover (LULCC) data, regarding space, time and topology. In this framework, a spatial ontology has been created to encode essential knowledge about spatio-temporal variation changes such as deforestation and urbanization.…”

Section: Ontology-based Change Detectionmentioning

confidence: 99%

Semantic Remote Sensing Scenes Interpretation and Change Interpretation

Ghazouani¹,

Farah²,

Solaiman³

2018

Ontology in Information Science

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A fundamental objective of remote sensing imagery is to spread out the knowledge about our environment and to facilitate the interpretation of different phenomena affecting the Earth's surface. The main goal of this chapter is to understand and interpret possible changes in order to define subsequently strategies and adequate decision-making for a better soil management and protection. Consequently, the semantic interpretation of remote sensing data, which consists of extracting useful information from image date for attaching semantics to the observed phenomenon, allows easy understanding and interpretation of such occurring changes. However, performing change interpretation task is not only based on the perceptual information derived from data but also based on additional knowledge sources such as a prior and contextual. This knowledge needs to be encoded in an appropriate way for being used as a guide in the interpretation process. On the other hand, interpretation may take place at several levels of complexity from the simple recognition of objects on the analyzed scene to the inference of site conditions and to change interpretation. For each level, information elements such as data, information and knowledge need to be represented and characterized. This chapter highlights the importance of ontologies exploiting for encoding the domain knowledge and for using it as a guide in the semantic scene interpretation task.

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“…Semantic search aims to match digital geospatial resources to user queries based on the meaning (semantics) of the queries, and therefore can identify relevant data and services even though they are not labeled with the exactly same words. Early attempts of semantic search include the use of ontology-based query expansion (Lutz and Klien 2006), rule-based semantic reasoning (Li, Zhou and Wu 2016), and faceted search, e.g., Apache Solr (Hostetter 2006). Despite the existing research efforts, additional work is still necessary to integrate semantic search into current SDIs.…”

Section: Search Functionmentioning

confidence: 99%

“…Early attempts of semantic search include the use of ontology-based query expansion (Lutz and Klien 2006), rule-based semantic reasoning (Li, Zhou and Wu 2016), and faceted search, e.g., Apache Solr (Hostetter 2006). Despite the existing research efforts, additional work is still necessary to integrate semantic search into current SDIs.…”

Section: Search Functionmentioning

confidence: 99%

Spatial Data Infrastructures

Hu¹,

Li²

2017

GIS&T BoK

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Spatial data infrastructure (SDI) is the infrastructure that facilitates the discovery, access, management, distribution, reuse, and preservation of digital geospatial resources. These resources may include maps, data, geospatial services, and tools. As cyberinfrastructures, SDIs are similar to other infrastructures, such as water supplies and transportation networks, since they play fundamental roles in many aspects of the society. These roles have become even more significant in today's big data age, when a large volume of geospatial data and Web services are available. From a technological perspective, SDIs mainly consist of data, hardware, and software. However, a truly functional SDI also needs the efforts of people, supports from organizations, government policies, data and software standards, and many others. In this chapter, we will present the concepts and values of SDIs, as well as a brief history of SDI development in the U.S. We will also discuss the components of a typical SDI, and will specifically focus on three key components: geoportals, metadata, and search functions. Examples of the existing SDI implementations will also be discussed. Definitions 1. Spatial data infrastructure:The technology, policies, standards, and human resources necessary to acquire, process, store, distribute, and improve utilization of geospatial data, services, and other digital resources. 2. Geoportal: A gateway website through which people can search, discover, access, and visualize the geospatial resources within a SDI. 3. Metadata: Documentation about who, when, how, what, why, and many other facets of the data and the data production process. Metadata can be used for describing not only data, but also tools, services, and other geospatial resources. Data standard:A commonly agreed specification on how data should be recorded and described. Geospatial interoperability:The ability of different geographic information systems to share, exchange, and operate (heterogenous) geospatial data and functions. 6. Web service: A Web application that provides standardized application programming interfaces to allow remote access to data and functions over the Internet.

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An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study

Cited by 12 publications

References 36 publications

A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

Semantic Remote Sensing Scenes Interpretation and Change Interpretation

Spatial Data Infrastructures

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