Evaluation on Tourism Ecological Security in Nature Heritage Sites——Case of Kanas Nature Reserve of Xinjiang,China

The nature heritages are the precious legacy of nature with outstanding scientific and aesthetic value. They are quite different from other common ecotourism areas, because of its original and unique system, sensitive and vulnerable landscape, and peripheral cultural features. Therefore, the tourism development in the nature heritage sites should be on the premise of ecological security. The evaluation index system of tourism ecological security in nature heritage sites was constructed in this article by AHP and Delphi methods, including nature ecological security, landscape visual security and local culture ecological security, and the security thresholds of indices were also established. In the indices' weights of the evaluation model, the nature ecological security ranked the highest, followed by tourist landscape visual security and culture ecological security, which reflected the influence degree of the limited factor to tourism ecological security. Then, this paper carried out an empirical study of Kanas of Xinjiang Uygur Autonomous Region, China, which has the potential to be the World Nature Heritage. On the basis of the data attained from survey and observation on the spot, as well as questionnaire answered by tourists and local communities, the ecological security status in Kanas was evaluated. The result showed that the status of Kanas tourism ecological security was better, but there had some limiting factors. Lastly, effective measures were put forward to ensure its ecological security.

A More Efficient Approach for Remote Sensing Image Classification

Over the past decade,the significant growth of the convolutional neural network(CNN)based on deep learning(DL)approaches has greatly improved the machine learning(ML)algorithm’s performance on the semantic scene classification(SSC)of remote sensing images(RSI).However,the unbalanced attention to classification accuracy and efficiency has made the superiority of DL-based algorithms,e.g.,automation and simplicity,partially lost.Traditional ML strategies(e.g.,the handcrafted features or indicators)and accuracy-aimed strategies with a high trade-off(e.g.,the multi-stage CNNs and ensemble of multi-CNNs)are widely used without any training efficiency optimization involved,which may result in suboptimal performance.To address this problem,we propose a fast and simple training CNN framework(named FST-EfficientNet)for RSI-SSC based on an EfficientNetversion2 small(EfficientNetV2-S)CNN model.The whole algorithm flow is completely one-stage and end-to-end without any handcrafted features or discriminators introduced.In the implementation of training efficiency optimization,only several routine data augmentation tricks coupled with a fixed ratio of resolution or a gradually increasing resolution strategy are employed,so that the algorithm’s trade-off is very cheap.The performance evaluation shows that our FST-EfficientNet achieves new state-of-the-art(SOTA)records in the overall accuracy(OA)with about 0.8%to 2.7%ahead of all earlier methods on the Aerial Image Dataset(AID)and Northwestern Poly-technical University Remote Sensing Image Scene Classification 45 Dataset(NWPU-RESISC45D).Meanwhile,the results also demonstrate the importance and indispensability of training efficiency optimization strategies for RSI-SSC by DL.In fact,it is not necessary to gain better classification accuracy by completely relying on an excessive trade-off without efficiency.Ultimately,these findings are expected to contribute to the development of more efficient CNN-based approaches in RSI-SSC.

A Consistent Mistake in Remote Sensing Images’Classification Literature

Recently,the convolutional neural network(CNN)has been dom-inant in studies on interpreting remote sensing images(RSI).However,it appears that training optimization strategies have received less attention in relevant research.To evaluate this problem,the author proposes a novel algo-rithm named the Fast Training CNN(FST-CNN).To verify the algorithm’s effectiveness,twenty methods,including six classic models and thirty archi-tectures from previous studies,are included in a performance comparison.The overall accuracy(OA)trained by the FST-CNN algorithm on the same model architecture and dataset is treated as an evaluation baseline.Results show that there is a maximal OA gap of 8.35%between the FST-CNN and those methods in the literature,which means a 10%margin in performance.Meanwhile,all those complex roadmaps,e.g.,deep feature fusion,model combination,model ensembles,and human feature engineering,are not as effective as expected.It reveals that there was systemic suboptimal perfor-mance in the previous studies.Most of the CNN-based methods proposed in the previous studies show a consistent mistake,which has made the model’s accuracy lower than its potential value.The most important reasons seem to be the inappropriate training strategy and the shift in data distribution introduced by data augmentation(DA).As a result,most of the performance evaluation was conducted based on an inaccurate,suboptimal,and unfair result.It has made most of the previous research findings questionable to some extent.However,all these confusing results also exactly demonstrate the effectiveness of FST-CNN.This novel algorithm is model-agnostic and can be employed on any image classification model to potentially boost performance.In addition,the results also show that a standardized training strategy is indeed very meaningful for the research tasks of the RSI-SC.

FST-EfficientNetV2:Exceptional Image Classification for Remote Sensing

Recently,the semantic classification(SC)algorithm for remote sensing images(RSI)has been greatly improved by deep learning(DL)techniques,e.g.,deep convolutional neural networks(CNNs).However,too many methods employ complex procedures(e.g.,multi-stages),excessive hardware budgets(e.g.,multi-models),and an extreme reliance on domain knowledge(e.g.,handcrafted features)for the pure purpose of improving accuracy.It obviously goes against the superiority of DL,i.e.,simplicity and automation.Meanwhile,these algorithms come with unnecessarily expensive overhead on parameters and hardware costs.As a solution,the author proposed a fast and simple training algorithm based on the smallest architecture of EfficientNet version 2,which is called FST-EfficientNet.The approach employs a routine transfer learning strategy and has fast training characteristics.It outperforms all the former methods by a 0.8%–2.7%increase in accuracy.It does,however,use a higher testing resolution of 5122 and 6002,which results in high consumption of graphics processing units(GPUs).As an upgrade option,the author proposes a novel and more efficient method named FSTEfficientNetV2 as the successor.The new algorithm still employs a routine transfer learning strategy and maintains fast training characteristics.But a set of crucial algorithmic tweaks and hyperparameter re-optimizations have been updated.As a result,it achieves a noticeable increase in accuracy of 0.3%–1.1%over its predecessor.More importantly,the algorithm’sGPU costs are reduced by 75%–81%,with a significant reduction in training time costs of 60%–80%.The results demonstrate that an efficient training optimization strategy can significantly boost the CNN algorithm’s performance for RSISC.More crucially,the results prove that the distribution shift introduced by data augmentation(DA)techniques is vital to the method’s performance for RSI-SC,which has been ignored to date.These findings may help us gain a correct understanding of the CNN algorithm for RSI-SC.