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Achieving the 17 UN Sustainable Development Goals (SDGs) requires accurate, consistent and cost-efficient data. The papers in this book set out some of the frontiers regarding the use of Earth Observation (EO) data derived from satellites to help populate sustainable development indicators. It sets out the current challenges and opportunities in the field along with some case studies from around the world illustrating applications, such as exploring the use of EO for tracking SDG indicators using population grids to assessing resilience to extreme events such as flooding. Likewise, it covers diverse approaches that offer insights and guidance on environmental land management, desertification and support for evaluating education from space.

earth observation --- end-users --- environmental management --- land use --- Brazil --- Sustainable Development Goals --- hazards --- Earth observations --- remote sensing --- demography --- urbanization --- gridded population --- very high resolution satellite data --- habitat suitability --- habitat assessment --- Environmental Land Management --- monitoring --- verification --- reporting --- soil organic carbon --- soil organic matter --- Earth Observation --- SDGs --- indicator type --- data challenges --- UN sustainable development goals --- education --- socio-economic --- overcrowded schools --- vulnerability --- flooding --- Earth Observation (EO) --- Google Street View (GSV) --- climate change --- uncertainty evaluation --- desertification --- global change --- Earth observation --- planetary measurement --- Land Degradation Neutrality

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This book collects 14 articles from the Special Issue entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries” of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.

faster region-based CNN --- visual tracking --- intelligent tire manufacturing --- eye-tracking device --- neural networks --- A* --- information measure --- oral evaluation --- GSA-BP --- tire quality assessment --- humidity sensor --- rigid body kinematics --- intelligent surveillance --- residual networks --- imaging confocal microscope --- update mechanism --- multiple linear regression --- geometric errors correction --- data partition --- Imaging Confocal Microscope --- image inpainting --- lateral stage errors --- dot grid target --- K-means clustering --- unsupervised learning --- recommender system --- underground mines --- digital shearography --- optimization techniques --- saliency information --- gated recurrent unit --- multivariate time series forecasting --- multivariate temporal convolutional network --- foreign object --- data fusion --- update occasion --- generative adversarial network --- CNN --- compressed sensing --- background model --- image compression --- supervised learning --- geometric errors --- UAV --- nonlinear optimization --- reinforcement learning --- convolutional network --- neuro-fuzzy systems --- deep learning --- image restoration --- neural audio caption --- hyperspectral image classification --- neighborhood noise reduction --- GA --- MCM uncertainty evaluation --- binary classification --- content reconstruction --- kinematic modelling --- long short-term memory --- transfer learning --- network layer contribution --- instance segmentation --- smart grid --- unmanned aerial vehicle --- forecasting --- trajectory planning --- discrete wavelet transform --- machine learning --- computational intelligence --- tire bubble defects --- offshore wind --- multiple constraints --- human computer interaction --- Least Squares method

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The use of renewable energy sources (RESs) is a need of global society. This editorial, and its associated Special Issue “Grid-Connected Renewable Energy Sources”, offers a compilation of some of the recent advances in the analysis of current power systems that are composed after the high penetration of distributed generation (DG) with different RESs. The focus is on both new control configurations and on novel methodologies for the optimal placement and sizing of DG. The eleven accepted papers certainly provide a good contribution to control deployments and methodologies for the allocation and sizing of DG.

Research & information: general --- Technology: general issues --- solar energy --- wind energy --- energy storage --- renewable energy integration --- Europe --- advanced metering infrastructure --- data acquisition --- IEC standards --- low-cost --- open source --- power measurement --- smart meter --- uncertainty evaluation --- frequency stabilization --- coordinated control --- wind turbine generator --- high-fidelity battery model --- releasable and absorbable energy --- photovoltaic emulator --- photovoltaic panel --- single diode model --- MPPT --- FSWT-SCIG --- battery storage system --- power system stability --- synchronous generator --- hybrid system --- voltage source converter --- passivity-based control --- proportional-integral control --- voltage regulation --- bi-directional converter --- LC impedance source converter --- DC–DC power converter --- bi-directional power flow --- alternating current networks --- direct current networks --- optimal power flow --- non-linear optimization --- control of power electronic converters --- distributed generation --- mixed-integer nonlinear programming --- second-cone programming --- discrete-sine cosine algorithm --- metaheuristic optimization --- DG placement --- evolutionary algorithms --- energy management --- fuzzy controller --- power systems analysis --- interconnected power systems --- latencies --- time-delay effects --- wide area monitoring systems --- renewable energy conversion --- power conditioning devices --- renewable energy policies --- power quality --- computations methods --- control strategies --- electric vehicle charging --- energy management systems --- ancillary services --- monitoring --- prognostic and diagnostic

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The use of renewable energy sources (RESs) is a need of global society. This editorial, and its associated Special Issue “Grid-Connected Renewable Energy Sources”, offers a compilation of some of the recent advances in the analysis of current power systems that are composed after the high penetration of distributed generation (DG) with different RESs. The focus is on both new control configurations and on novel methodologies for the optimal placement and sizing of DG. The eleven accepted papers certainly provide a good contribution to control deployments and methodologies for the allocation and sizing of DG.

solar energy --- wind energy --- energy storage --- renewable energy integration --- Europe --- advanced metering infrastructure --- data acquisition --- IEC standards --- low-cost --- open source --- power measurement --- smart meter --- uncertainty evaluation --- frequency stabilization --- coordinated control --- wind turbine generator --- high-fidelity battery model --- releasable and absorbable energy --- photovoltaic emulator --- photovoltaic panel --- single diode model --- MPPT --- FSWT-SCIG --- battery storage system --- power system stability --- synchronous generator --- hybrid system --- voltage source converter --- passivity-based control --- proportional-integral control --- voltage regulation --- bi-directional converter --- LC impedance source converter --- DC–DC power converter --- bi-directional power flow --- alternating current networks --- direct current networks --- optimal power flow --- non-linear optimization --- control of power electronic converters --- distributed generation --- mixed-integer nonlinear programming --- second-cone programming --- discrete-sine cosine algorithm --- metaheuristic optimization --- DG placement --- evolutionary algorithms --- energy management --- fuzzy controller --- power systems analysis --- interconnected power systems --- latencies --- time-delay effects --- wide area monitoring systems --- renewable energy conversion --- power conditioning devices --- renewable energy policies --- power quality --- computations methods --- control strategies --- electric vehicle charging --- energy management systems --- ancillary services --- monitoring --- prognostic and diagnostic

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The topics of the book cover forest parameter estimation, methods to assess land cover and change, forest disturbances and degradation, and forest soil drought estimations. Airborne laser scanner data, aerial images, as well as data from passive and active sensors of different spatial, spectral and temporal resolutions have been utilized. Parametric and non-parametric methods including machine and deep learning methods have been employed. Uncertainty estimation is a key topic in each study. In total, 15 articles are included, of which one is a review article dealing with methods employed in remote sensing aided greenhouse gas inventories, and one is the Editorial summary presenting a short review of each article.

Research & information: general --- Environmental economics --- forest structure change --- EBLUP --- small area estimation --- multitemporal LiDAR and stand-level estimates --- forest cover --- Sentinel-1 --- Sentinel-2 --- data fusion --- machine-learning --- Germany --- South Africa --- temperate forest --- savanna --- classification --- Sentinel 2 --- land use land cover --- improved k-NN --- logistic regression --- random forest --- support vector machine --- statistical estimator --- IPCC good practice guidelines --- activity data --- emissions factor --- removals factor --- Picea crassifolia Kom --- compatible equation --- nonlinear seemingly unrelated regression --- error-in-variable modeling --- leave-one-out cross-validation --- digital surface model --- digital terrain model --- canopy height model --- constrained neighbor interpolation --- ordinary neighbor interpolation --- point cloud density --- stereo imagery --- remotely sensed LAI --- field measured LAI --- validation --- magnitude --- uncertainty --- temporal dynamics --- state space models --- forest disturbance mapping --- near real-time monitoring --- CUSUM --- NRT monitoring --- deforestation --- degradation --- tropical forest --- tropical peat --- forest type --- deep learning --- FCN8s --- CRFasRNN --- GF2 --- dual-FCN8s --- random forests --- error propagation --- bootstrapping --- Landsat --- LiDAR --- La Rioja --- forest area change --- data assessment --- uncertainty evaluation --- inconsistency --- forest monitoring --- drought --- time series satellite data --- Bowen ratio --- carbon flux --- boreal forest --- windstorm damage --- synthetic aperture radar --- C-band --- genetic algorithm --- multinomial logistic regression