基于深度学习的高分辨率遥感影像光伏用地提取

近年来我国光伏产业发展迅猛,随之也产生了诸多用地问题,通过遥感技术提取光伏用地,监测光伏用地分布与用地状况,对于光伏产业健康发展具有重要意义。本文提出一套基于深度学习方法的高分辨率遥感影像光伏用地自动提取方法,该方法利用GF-1等卫星影像和Google Earth影像构建光伏用地样本,基于ResNeSt-50作为骨干网络的DeepLab V3+模型实现深度学习语义分割算法,并结合计算机图形学方法对深度学习结果进行后处理,实现了面向高分辨率遥感影像较通用的且高精度的光伏用地自动提取。该方法的深度学习模型验证精度mIoU值达0.899 2,提取结果具有良好的边缘精度且具有广泛的适用性,支持GF-1、ZY-3、GF-6、GF-2和Google Earth等影像。     

中国地质学的产生与发展——写在地质调查所创建100周年之际

中国的地质学非我国自有,而是在传播和引入西方近代科学文化的过程中逐渐形成的,是"西学东渐"的结果。在欧美等西方国家,至19世纪中叶,便建立并完善了近代地质学的理论和方法体系,完成了学科体制化建设,比中国至少早一个世纪。从16世纪后半叶的"西学东渐",西方近代地学传入中国进行第一次启蒙,到引进、消化、吸收和本土化,最后建立中国地质学理论和方法体系,完成学科建制,中国地质事业历经了长达三个半世纪艰辛曲折并充满坎坷的历史进程。创建于1913年的地质调查所是中国近代第一个科研机构,在章鸿钊、丁文江、翁文灏等第一代地质学家的领导下,筚路蓝缕、艰苦奋斗,在短短的二、三十年里,全面开创和奠定了中国近代地质事业,培养了一大批早期地质学家,取得了举世瞩目的成果,赢得国内外学术界的广泛赞誉。地质调查所的成功及其所取得的成就远远超出了地质学,对我国古生物学、地理学、地震学、地球物理学、土壤学、考古、古人类学、燃料和地图等诸多学科领域都产生了深远影响。     

Recent advances in earthquake monitoring II: Emergence of next-generation intelligent systems

Seismic data processing techniques, together with seismic instrumentation, determine our earthquake monitoring capability and the quality of resulting earthquake catalogs. This paper is intended to review the improvement of earthquake monitoring capability from the perspective of data processing. Over the past two decades, seismologists have made considerable advancements in seismic data processing, partly thanks to the significant development of computational power, signal processing, and machine learning techniques. In particular, wide application of template matching and increasing use of deep learning significantly enhance our capability to extract signals of small earthquakes from noisy data. Relative location techniques provide a critical tool to elucidate fault geometries and seismicity migration patterns at unprecedented resolution. These techniques are becoming standard, leading to emerging intelligent software systems for next-generation earthquake monitoring. Prospective improvements in future research must consider the urgent needs in highly generalizable detection algorithms (for both permanent and temporary deployments) and in emergency real-time monitoring of ongoing sequences (e.g., aftershock and induced seismicity sequences). We believe that the maturing of intelligent and high-resolution processing systems could transform traditional earthquake monitoring workflows and eventually liberate seismologists from laborious catalog construction tasks.     

Spectral sparse Bayesian learning reflectivity inversion

A spectral sparse Bayesian learning reflectivity inversion method, combining spectral reflectivity inversion with sparse Bayesian learning, is presented in this paper. The method retrieves a sparse reflectivity series by sequentially adding, deleting or re‐estimating hyper‐parameters, without pre‐setting the number of non‐zero reflectivity spikes. The spikes with the largest amplitude are usually the first to be resolved. The method is tested on a series of data sets, including synthetic data, physical modelling data and field data sets. The results show that the method can identify thin beds below tuning thickness and highlight stratigraphic boundaries. Moreover, the reflectivity series, which is inverted trace‐by‐trace, preserves the lateral continuity of layers.     

Detecting events from the social media through exemplar-enhanced supervised learning

ABSTRACT

Understanding and detecting the intended meaning in social media is challenging because social media messages contain varieties of noise and chaos that are irrelevant to the themes of interests. For example, conventional supervised classification approaches would produce inconsistent solutions to detecting and clarifying whether any given Twitter message is really about a wildfire event. Consequently, a renovated workflow was designed and implemented. The workflow consists of four sequential procedures: (1) Apply the latent semantic analysis and cosine similarity calculation to examine the similarity between Twitter messages; (2) Apply Affinity Propagation to identify exemplars of Twitter messages; (3) Apply the cosine similarity calculation again to automatically match the exemplars to known training results, and (4) Apply accumulative exemplars to classify Twitter messages using a support vector machine approach. The overall correction ratio was over 90% when a series of ongoing and historical wildfire events were examined.     

Cartographic Industry Profiles

Abstract

Analysis of eye movements has been used for decades as a method for assessing the performance of visual stimuli. Until recently, this has mainly been applied to static and non-cartographic stimuli, but due to technological developments and reduced cost of equipment, interactive and cartographic applications are now feasible. suggest a new analysis method which applies Hägerstrand’s Space-Time-Cube (STC; ) to eye movement data. However, in an interactive three-dimensional STC, identifying and exploring key behaviours can be difficult. In order to ameliorate these difficulties, we propose a variation of the STC method, which uses two-dimensional projections of the STC onto the XT and YT planes. These two-dimensional projections are found to facilitate rapid identification of significant patterns in the data set. A prototype implementing this and other dynamical methods has been developed, and is presented with examples illustrating the benefits of working with two-dimensional projections of the STC.     

基于多核学习的高分辨率遥感图像目标检测方法

为更有效地实现复杂场景中的多类目标同时检测,本文提出了一种基于多核学习算法进行目标检测的框架。该方法由特征提取和模型训练2个阶段组成。特征提取阶段,引入了多尺度下的点特征、表观特征同时对多类目标进行综合描述;模型训练阶段,分别采用加权相加和相乘2种方法将提取的各个基础特征组合起来,在支持向量机的框架下对各特征所代表的基础核权重进行学习。将训练所得的分类器结合滑动窗搜索技术对遥感图像进行目标检测实验,结果表明,与传统单核支持向量机相比,准确率更高。     

Dislocation Pedagogy

This article describes an experiment in learning with a course on “research and philosophies of place and space” in which students were repeatedly dislocated from an environment familiar to them and replaced in key locations about the city with local place experts as their guide. The article begins with a review of the meaning of place and the opportunities offered by the City of Kingston for reflective engagement. It briefly hints at some of the social trends devaluing place learning and the consequently political nature of this pedagogy. Finally, it summarizes the lessons learned in five of the ten classes.     

Learning Wisdom Through Geographical Dislocations

Wisdom is at once one of the most elusive and most valued kinds of knowledge. Empirical research shows that, indeed, across cultures, people hope that life experience will eventually make them wiser. The problem is that, to date, the academic study of wisdom and of the processes by which it can be learned has been dominated by psychologists. The first part of the article reviews the state-of-the-art psychological scholarship on wisdom to show how that conceptualization lacks geographical sensitivity and therefore misses some of the crucial geographical mechanisms by which people become wiser. The second part of the article singles out and focuses on one such mechanism, namely, the learning of wisdom through geographical dislocations. By drawing on insights from the study of international migration, exile, and transculturation in postcolonial contexts, the final part of the article suggests specific learning processes that might strengthen the hypothesis that geographical dislocations and the attendant cross-cultural experiences they generate are often conducive to wisdom.