An object-oriented approach to automated landform mapping: A case study of drumlins

2011 ◽  
Vol 37 (9) ◽  
pp. 1324-1336 ◽  
Author(s):  
Kakoli Saha ◽  
Neil A. Wells ◽  
Mandy Munro-Stasiuk
Author(s):  
Fredrik Andersson ◽  
Patrik Nilsson ◽  
Hans Johannesson

Abstract This paper proposes a requirement and concept model based on a functional decomposition of mechanical systems. It is an object-oriented approach to integrate the representation of the design artefact and the design activity, through the decisions made during the design evolution. The requirements co-evolve simultaneously with the formation of the conceptual layout, through the opportunity to alter between function and physical/abstract solutions. This approach structures the design requirements and concepts in such a way that it supports the ability to document their sources, to allow for validation and verifications of both requirements and design solutions. First, the proposed model is presented from a theoretical viewpoint. Secondly, a methodology for modelling requirements and concepts in an object-oriented fashion is discussed. Finally, the model is implemented in METIS software and tested in a case study of an electric window winder on a truck door.


DYNA ◽  
2020 ◽  
Vol 87 (215) ◽  
pp. 136-145
Author(s):  
Juan Ricardo Mancera Florez ◽  
Ivan Alberto Lizarazo Salcedo

In this paper, the potential of Sentinel-1A and Sentinel-2A satellite images for land cover mapping is evaluated at three levels of spatial detail; exploratory, reconnaissance, and semi-detailed. To do so, two different image classification approaches are compared: (i) a traditional pixel-wise approach; and (ii) an object–oriented approach. In both cases, the classification task was conducted using the “RandomForest” algorithm. The case study was also intended to identify a set of radar channels, optical bands, and indices that are relevant for classification. The thematic accuracy of the classifications displays the best results for the object-oriented approach to exploratory and recognition levels. The results show that the integration of multispectral and radar data as explanatory variables for classification provides better results than the use of a single data source.


IEE Review ◽  
1990 ◽  
Vol 36 (9) ◽  
pp. 338
Author(s):  
Stephen Wilson

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