Slope ecological restoration based on image classification and construction of sports fitness index

2021 ◽  
Vol 14 (16) ◽  
Author(s):  
Huang He
2019 ◽  
Vol 3 (1) ◽  
pp. 1-9
Author(s):  
Robert M. Anderson ◽  
Amy M. Lambert

The island marble butterfly (Euchloe ausonides insulanus), thought to be extinct throughout the 20th century until re-discovered on a single remote island in Puget Sound in 1998, has become the focus of a concerted protection effort to prevent its extinction. However, efforts to “restore” island marble habitat conflict with efforts to “restore” the prairie ecosystem where it lives, because of the butterfly’s use of a non-native “weedy” host plant. Through a case study of the island marble project, we examine the practice of ecological restoration as the enactment of particular norms that define which species are understood to belong in the place being restored. We contextualize this case study within ongoing debates over the value of “native” species, indicative of deep-seated uncertainties and anxieties about the role of human intervention to alter or manage landscapes and ecosystems, in the time commonly described as the “Anthropocene.” We interpret the question of “what plants and animals belong in a particular place?” as not a question of scientific truth, but a value-laden construct of environmental management in practice, and we argue for deeper reflexivity on the part of environmental scientists and managers about the social values that inform ecological restoration.


2020 ◽  
Vol 79 (9) ◽  
pp. 781-791
Author(s):  
V. О. Gorokhovatskyi ◽  
I. S. Tvoroshenko ◽  
N. V. Vlasenko

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.


Author(s):  
Sumit Kaur

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 


PIERS Online ◽  
2007 ◽  
Vol 3 (5) ◽  
pp. 625-628
Author(s):  
Jian Yang ◽  
Xiaoli She ◽  
Tao Xiong

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