On the Formulation of Conceptual Spaces for Land Cover Classification Systems

Baglatzi, Alkyoni; Kuhn, Walther

doi:10.1007/978-3-319-00615-4_10

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…But ecosystems are far from being representations of the abovementioned exemplary cases, and there will invariably be cases where these definitions are inapplicable. There needs to be a conceptual space [181] where the disparate characteristics that make up savannas and their relative advantages and weaknesses at different scales are discussed. In light of this, the delineation of savannas as distinct biomes that are not degraded forests [19,182] but an alternative stable state to forests [183] is crucial to keep them ecologically intact.…”

Section: A Consistent Definition Of Savannas In Remote Sensingmentioning

confidence: 99%

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

et al. 2022

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Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010–present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments.

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Section: A Consistent Definition Of Savannas In Remote Sensingmentioning

confidence: 99%

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

et al. 2022

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“…Moreover, he also proposed the concept of semantic reference systems [49,50]. He then continued his work to apply semantic reference systems to semantic transformations among instance objects in different land management systems using the theory and methods of Conceptual Space [51]. In 2005, he suggested that semantic interactions between geographic information services could be divided into two categories, specifically (1) defining the semantic connotations of metadata corresponding to every service interface for querying and searching semantic information, and (2) defining a mathematical model of semantic interoperation for the expression of semantic knowledge and reasoning using that knowledge [52].…”

Section: Characteristics Of Author Co-citationmentioning

confidence: 99%

A Co-Citation and Cluster Analysis of Scientometrics of Geographic Information Ontology

Lin

Shen

et al. 2018

IJGI

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Geographic information ontology represents an effective means of expressing geographic concepts and relationships between them. As an emerging field of study, it has drawn the attention of increasing numbers of scholars worldwide. In this study, both co-citation and cluster analysis methods of scientometrics are used to perform a comprehensive analysis of the papers on the topic of geographic information ontology indexed by the Web of Science (WoS) and published between 2001 and 2016. The results show that the history of the study of geographic information ontology can be divided roughly into three periods. Computer science and mathematics play important roles in this field of study. The International Journal of Geographical Information Science is an important periodical that provides knowledge resources for the study of geographic information ontology. The papers of Gruber TR and Guarino N are referenced most frequently, as well as that of Smith B., who formally introduced information ontology to the field of geographic information science. Providing personalized and intelligent geographic information services for users is an important focus of geographic information ontology.

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“…For the image feature extraction, CNN could be used to keep the balance between the main schema and fine details simultaneously for processing images. Besides, CNN has also shown advantages on crossing the barrier between standard extraction templates and complex real images, which is achieved by CNN tracking features on both high level and low level of image characteristics (Baglatzi and Kuhn 2013, Herrault, Sheeren et al 2013, Richter, Richter et al 2013.…”

Section: Deep Learning Developmentmentioning

confidence: 99%

A Deep Learning Study of Extracting Navigation Area From Cad Blueprints

Niu

Song

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Deep learning technology is a cutting edge topic of AI region, and draws more attention from photogrammetry and remote sensing society. In this study, we strive to combine deep learning with CAD designs to extract navigation area (room). To this, we mark more than 200 2D building blueprint in CAD forms to construct the learning set to train object detection model based on TensorFlow. This model is the faster R-CNN inception v2 model from COCO dataset. The test and result section is composed of three parts: First part demonstrates the model performance on learning dataset; second part applies the generated model to extract rooms from untrained raw CAD blueprints; Third part covers the comparison between deep learning extracted result and geometric based algorithm extracted result. Test result shows that the deep learning approach could achieve higher accuracy than geometric approach under regular shape situations. In conclusion, we have proposed a well-trained deep learning model that could be utilized to construct a schema of the navigation area for 2D CAD blueprints.</p>

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On the Formulation of Conceptual Spaces for Land Cover Classification Systems

Cited by 7 publications

References 24 publications

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

A Co-Citation and Cluster Analysis of Scientometrics of Geographic Information Ontology

A Deep Learning Study of Extracting Navigation Area From Cad Blueprints

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