地学 | 下半年截稿 | SCI期刊专刊信息4条

全文截稿: 2021-06-30

影响因子: 4.846

中科院JCR分区:

• 大类 : 地学 - 1区

• 小类 : 遥感 - 2区

网址：
https://www.journals.elsevier.com/international-journal-of-applied-earth-observation-and-geoinformation

This special issue welcomes contributions that showcase the advances in remote sensing of urban vegetation to support the urban sustainable development goals, including monitoring different geographical, physical and biological parameters of urban vegetation using various sensor platforms, as well as developing advanced techniques and methods for data processing and analysis. This includes, but not limited to:

Urban vegetation segmentation and classification

Urban green space mapping and assessment

Heterogeneous vegetation species characterization

Vegetation health/plant diseases monitoring

Urban vegetation change detection

Urban vegetation carbon stocks assessment

Urban green infrastructure monitoring

Rendering and visualization of urban vegetation

Multisensor data registration and fusion

Deep learning for urban vegetation detection

High performance computing for urban vegetation mapping

全文截稿: 2021-08-31

影响因子: 4.846

中科院JCR分区:

• 大类 : 地学 - 1区

• 小类 : 遥感 - 2区

网址：
https://www.journals.elsevier.com/international-journal-of-applied-earth-observation-and-geoinformation

LiDAR, as an active and accurate remote sensing technique, is being positively used in many applications ranging from land use/land cover classification, 3D urban modelling, road inspection, to forest inventory. Likewise, it is used as advanced ranging measurements on machinery to scanning devices applied as terrestrial, mobile or airborne laser scanning (TLS/MLS/ALS), where ALS approaches include newer applications from unmanned aerial vehicles (UAVs). However, point clouds obtained from these systems have the unique features of true three dimensionalities, large volume, varied point densities, heterogeneous distributions, scene complexity, and data incompleteness. It is still challengeable to fulfill efficient and effective point cloud understanding that includes point cloud registration and fusion, feature extraction, semantic labelling, segmentation, and classification, as well as large-scale point clouds for 3D scene modelling, geospatial mapping, and environmental monitoring applications. We are pleased to announce a Call for Papers on understanding LiDAR point clouds obtained from different platforms.

This Special Issue welcomes contributions that showcase the recent advancements in LiDAR point cloud understanding to support environmental monitoring, intelligent transportation systems, geospatial big data analysis, 3D modelling, and high-performance computing.

Areas of interest include, but not necessarily restricted to:

Multi-station/multi-sensor point cloud registration

Fusion of point clouds with optical/multispectral/hyperspectral imagery

Point cloud sampling, geometric primitive representation, and feature engineering

Semantic labelling, segmentation, classification, rendering of and visualization of large-scale point clouds

3D object detection, extraction, recognition and reconstruction in point clouds

Quality assessment and uncertainty quantification of point clouds

Machine/deep learning for large-scale point cloud understanding

Multispectral/hyperspectral point clouds for semantic interpretation of wetlands, cultivated and vegetated areas

全文截稿: 2021-09-01

影响因子: 3.909

中科院JCR分区:

• 大类 : 地学 - 2区

• 小类 : 工程：地质 - 2区

• 小类 : 地球科学综合 - 2区

网址：
https://www.journals.elsevier.com/engineering-geology

Natural heterogeneity, limited data, and data interpretation have been recognized as the primary sources of uncertainty for practical problems in the fields of engineering geology. With decades of developments in measurement technologies and advanced models, much effort has been devoted to reducing the uncertainty, mainly focusing on bridging the gaps between available data and accurate geological models. Engineering practice has shown that geology knowledge plays an essential role in characterizing and quantifying uncertainty in various geological models across scales. Ignorance of geological model uncertainty often leads to failures of engineered structures, geohazards (such as landslides and land subsidence), and groundwater and environmental problems, all of which can lead to substantial societal risk. Therefore, it is crucial to characterize and quantify the geological model uncertainty and systematically examine its effect on engineering design, geohazard mitigation, groundwater resources, and environmental issues.