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Ausgehend von einer Auseinandersetzung mit dem Begriff des Tätigseins - als Voraussetzung für Lernprozesse - wird in diesem Band die Bedeutung frei gewählter gegenüber instruierter Tätigkeiten diskutiert und in die Kontexte Unterricht sowie Lernwerkstatt- Arbeit gestellt. Die Gegenüberstellung von instruiertem und freiem Tätigsein fordert heraus, das Verhältnis von Instruktion und gelingenden Konstruktionsleistungen in der Interaktion zwischen Lernbegleitung und Lernendem genauer zu bestimmen. Dies gilt für die Arbeit in Lern- und Forschungswerkstätten, in Lernlaboren und beim Lernen im ganz "normalen" Seminarraum oder Klassenzimmer. Insgesamt stellt sich die Frage: Kann man sich die Tätigkeit des entdeckenden, des forschenden, des praxisorientierten und des Disziplinen überschreitenden Lernens so zu eigen machen, dass es in habituelles Lernhandeln übergeht? Und: Soll es das überhaupt? Die Reihe "Lernen und Studieren in Lernwerkstätten - Impulse für Theorie und Praxis einer innovativen Lehrerbildung" wird herausgegeben von Hartmut Wedekind, Markus Peschel, Eva-Kristina Franz, Johannes Gunzenreiner und Barbara Müller-Naendrup.

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The book “Assessment of Renewable Energy Resources with Remote Sensing" focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques. The eleven papers inside the book provide an overview of remote sensing applications on hydro, solar, wind and geothermal energy resources and their major goal is to provide state of art knowledge to contribute with the renewable energy resource deployment, especially in regions where energy demand is rapidly expanding. Renewable energy resources have an intrinsic relationship with local environmental features and the regional climate. Even small and fast environment and/or climate changes can cause significant variability in power generation at different time and space scales. Methodologies based on remote sensing are the primary source of information for the development of numerical models that aim to support the planning and operation of an electric system with a substantial contribution of intermittent energy sources. In addition, reliable data and knowledge on renewable energy resource assessment are fundamental to ensure sustainable expansion considering environmental, financial and energetic security.

metaheuristic --- parameter extraction --- solar photovoltaic --- whale optimization algorithm --- cloud detection --- digitized image processing --- artificial neural networks --- solar irradiance estimation --- solar irradiance forecasting --- solar energy --- sky camera --- remote sensing --- CSP plants --- coastal wind measurements --- scanning LiDAR --- plan position indicator --- velocity volume processing --- Hazaki Oceanographical Research Station --- cloud coverage --- image processing --- total sky imagery --- geothermal energy --- geophysical prospecting --- time domain electromagnetic method --- electrical resistivity tomography --- potential well field location --- GES-CAL software --- smart island --- solar radiation forecasting --- light gradient boosting machine --- multistep-ahead prediction --- feature importance --- voxel-design approach --- shading envelopes --- point cloud data --- computational design method --- passive design strategy --- lake breeze influence --- hydropower reservoir --- solar irradiance enhancement --- solar energy resource --- wind speed --- extreme value analysis --- scatterometer --- feature engineering --- forecasting --- graphical user interface software --- machine learning --- photovoltaic power plant --- surface solar radiation --- global radiation --- satellite --- Baltic area --- coastline --- cloud --- convection --- climate --- renewable energy resource assessment and forecasting --- remote sensing data acquisition --- data processing --- statistical analysis --- machine learning techniques

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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

metadata --- image classification --- sensitivity analysis --- ROI detection --- residual learning --- image alignment --- adaptive convolutional kernels --- Hough transform --- class imbalance --- land surface temperature --- inundation mapping --- multiscale representation --- object-based --- convolutional neural networks --- scene classification --- morphological profiles --- hyperedge weight estimation --- hyperparameter sparse representation --- semantic segmentation --- vehicle classification --- flood --- Landsat imagery --- target detection --- multi-sensor --- building damage detection --- optimized kernel minimum noise fraction (OKMNF) --- sea-land segmentation --- nonlinear classification --- land use --- SAR imagery --- anti-noise transfer network --- sub-pixel change detection --- Radon transform --- segmentation --- remote sensing image retrieval --- TensorFlow --- convolutional neural network --- particle swarm optimization --- optical sensors --- machine learning --- mixed pixel --- optical remotely sensed images --- object-based image analysis --- very high resolution images --- single stream optimization --- ship detection --- ice concentration --- online learning --- manifold ranking --- dictionary learning --- urban surface water extraction --- saliency detection --- spatial attraction model (SAM) --- quality assessment --- Fuzzy-GA decision making system --- land cover change --- multi-view canonical correlation analysis ensemble --- land cover --- semantic labeling --- sparse representation --- dimensionality expansion --- speckle filters --- hyperspectral imagery --- fully convolutional network --- infrared image --- Siamese neural network --- Random Forests (RF) --- feature matching --- color matching --- geostationary satellite remote sensing image --- change feature analysis --- road detection --- deep learning --- aerial images --- image segmentation --- aerial image --- multi-sensor image matching --- HJ-1A/B CCD --- endmember extraction --- high resolution --- multi-scale clustering --- heterogeneous domain adaptation --- hard classification --- regional land cover --- hypergraph learning --- automatic cluster number determination --- dilated convolution --- MSER --- semi-supervised learning --- gate --- Synthetic Aperture Radar (SAR) --- downscaling --- conditional random fields --- urban heat island --- hyperspectral image --- remote sensing image correction --- skip connection --- ISPRS --- spatial distribution --- geo-referencing --- Support Vector Machine (SVM) --- very high resolution (VHR) satellite image --- classification --- ensemble learning --- synthetic aperture radar --- conservation --- convolutional neural network (CNN) --- THEOS --- visible light and infrared integrated camera --- vehicle localization --- structured sparsity --- texture analysis --- DSFATN --- CNN --- image registration --- UAV --- unsupervised classification --- SVMs --- SAR image --- fuzzy neural network --- dimensionality reduction --- GeoEye-1 --- feature extraction --- sub-pixel --- energy distribution optimizing --- saliency analysis --- deep convolutional neural networks --- sparse and low-rank graph --- hyperspectral remote sensing --- tensor low-rank approximation --- optimal transport --- SELF --- spatiotemporal context learning --- Modest AdaBoost --- topic modelling --- multi-seasonal --- Segment-Tree Filtering --- locality information --- GF-4 PMS --- image fusion --- wavelet transform --- hashing --- machine learning techniques --- satellite images --- climate change --- road segmentation --- remote sensing --- tensor sparse decomposition --- Convolutional Neural Network (CNN) --- multi-task learning --- deep salient feature --- speckle --- canonical correlation weighted voting --- fully convolutional network (FCN) --- despeckling --- multispectral imagery --- ratio images --- linear spectral unmixing --- hyperspectral image classification --- multispectral images --- high resolution image --- multi-objective --- convolution neural network --- transfer learning --- 1-dimensional (1-D) --- threshold stability --- Landsat --- kernel method --- phase congruency --- subpixel mapping (SPM) --- tensor --- MODIS --- GSHHG database --- compressive sensing

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