Tobler's First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection under Weak Supervision.
Recent interest in geospatial artificial intelligence (GeoAI) has fostered a wide range of applications using artificial intelligence (AI), especially deep learning for geospatial problem solving. Major challenges, however, such as a lack of training data and ignorance of spatial principles and spat...
| Publicado en: | Annals of the American Association of Geographers Vol. 111; no. 7; pp. 1887 - 1906 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=152573622&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 152573622 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: 2021 vid: 111 iid: 7 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 152573622 10.1080/24694452.2021.1877527 ppf: 1887 ppct: 19 formats: tig: atl: Tobler's First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection under Weak Supervision. aug: au: Li, Wenwen Hsu, Chia-Yu Hu, Maosheng affil: School of Geographical Sciences and Urban Planning, Arizona State University School of Geography and Information Engineering, China University of Geosciences su: Artificial intelligence Geospatial data Terrain mapping Deep learning Remote sensing sug: subj: Artificial intelligence Geospatial data Terrain mapping Deep learning Remote sensing keyword: 地形特征 弱监督。 深度学习 目标检测 遥感 aprendizaje a fondo deep learning detección de objeto GeoAI GeoIA object detection percepción remota rasgo del terreno remote sensing supervisado débilmente terrain feature weakly supervised aprendizaje a fondo detección de objeto GeoIA percepción remota rasgo del terreno supervisado débilmente 地形特征 弱监督。 深度学习 目标检测 遥感 地形特征 弱监督。 深度学习 目标检测 遥感 aprendizaje a fondo deep learning detección de objeto GeoAI GeoIA object detection percepción remota rasgo del terreno remote sensing supervisado débilmente terrain feature weakly supervised aprendizaje a fondo detección de objeto GeoIA percepción remota rasgo del terreno supervisado débilmente 地形特征 弱监督。 深度学习 目标检测 遥感 ab: Recent interest in geospatial artificial intelligence (GeoAI) has fostered a wide range of applications using artificial intelligence (AI), especially deep learning for geospatial problem solving. Major challenges, however, such as a lack of training data and ignorance of spatial principles and spatial effects in AI model design remain, significantly hindering the in-depth integration of AI with geospatial research. This article reports our work in developing a cutting-edge deep learning model that enables object detection, especially of natural features, in a weakly supervised manner. Our work has made three innovative contributions: First, we present a novel method of object detection using only weak labels. This is achieved by developing a spatially explicit model according to Tobler's first law of geography to enable weakly supervised object detection. Second, we integrate the idea of an attention map into the deep learning–based object detection pipeline and develop a multistage training strategy to further boost detection performance. Third, we have successfully applied this model for the automated detection of Mars impact craters, the inspection of which often involved tremendous manual work prior to our solution. Our model is generalizable for detecting both natural and man-made features on the surface of the Earth and other planets. This research has made a major contribution to the enrichment of the theoretical and methodological body of knowledge of GeoAI. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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