Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

陡坡地集流聚肥梯田技术的应用

本文根据径流农业和水土保持综合治理的基本原理，以及对辽宁西部陡坡地资源的调查研究，介绍了陡坡地集流聚肥梯田的内涵、集流机理。

日本农业文化遗产可持续性保护传承的经验及启鉴——以德岛县为例

在发掘中保护、在利用中传承、实现农业文化遗产的可持续性发展具有重要意义。通过访日实地调查、个案研究等方式,以德岛县全球重要农业文化遗产——西阿波陡坡农耕系统为例,基于可持续发展视角反思工业文明对该系统的冲击,深入探究该系统为实现可持续发展所采取的措施,系统总结日本经验对中国农业文化遗产保护传承的启鉴,主要包括树立并落实可持续发展理念、注重遗产地居民内源性保护意识的培养、给予强有力的政策性支持、建立多方参与的保护利用机制。