Spatiotemporal estimation of hourly 2-km ground-level ozone over China based on Himawari-8 using a self-adaptive geospatially local model

Ground-level ozone(O_(3))is a primary air pollutant,which can greatly harm human health and ecosystems.At present,data fusion frameworks only provided ground-level O_(3) concentrations at coarse spatial(e.g.,10 km)or temporal(e.g.,daily)resolutions.As photochemical pollution continues increasing over China in the last few years,a high-spatial–temporal-resolution product is required to enhance the comprehension of ground-level O_(3) formation mechanisms.To address this issue,our study creatively explores a brand-new framework for estimating hourly 2-km ground-level O_(3) concentrations across China(except Xinjiang and Tibet)using the brightness temperature at multiple thermal infrared bands.Considering the spatial heterogeneity of ground-level O_(3),a novel Self-adaptive Geospatially Local scheme based on Categorical boosting(SGLboost)is developed to train the estimation models.Validation results show that SGLboost performs well in the study area,with the R2 s/RMSEs of 0.85/19.041 lg/m^(3) and 0.72/25.112 lg/m^(3) for the space-based cross-validation(CV)(2017–2019)and historical space-based CV(2019),respectively.Meanwhile,SGLboost achieves distinctly better metrics than those of some widely used machine learning methods,such as e Xtreme Gradient boosting and Random Forest.Compared to recent related works over China,the performance of SGLboost is also more desired.Regarding the spatial distribution,the estimated results present continuous spatial patterns without a significantly partitioned boundary effect.In addition,accurate hourly and seasonal variations of ground-level O_(3) concentrations can be observed in the estimated results over the study area.It is believed that the hourly 2-km results estimated by SGLboost will help further understand the formation mechanisms of ground-level O_(3) in China.

Splitting and Merging Based Multi-model Fitting for Point Cloud Segmentation

This paper deals with the massive point cloud segmentation processing technology on the basis of machine vision, which is the second essential factor for the intelligent data processing of three dimensional conformation in digital photogrammetry. In this paper, multi-model fitting method is used to segment the point cloud according to the spatial distribution and spatial geometric structure of point clouds by fitting the point cloud into different geometric primitives models. Because point cloud usually possesses large amount of 3D points, which are uneven distributed over various complex structures, this paper proposes a point cloud segmentation method based on multi-model fitting. Firstly, the pre-segmentation of point cloud is conducted by using the clustering method based on density distribution. And then the follow fitting and segmentation are carried out by using the multi-model fitting method based on split and merging. For the plane and the arc surface, this paper uses different fitting methods, and finally realizing the indoor dense point cloud segmentation. The experimental results show that this method can achieve the automatic segmentation of the point cloud without setting the number of models in advance. Compared with the existing point cloud segmentation methods, this method has obvious advantages in segmentation effect and time cost, and can achieve higher segmentation accuracy. After processed by method proposed in this paper, the point cloud even with large-scale and complex structures can often be segmented into 3D geometric elements with finer and accurate model parameters, which can give rise to an accurate 3D conformation.

Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems

Building the physics-driven mechanism model has always been the core scientific paradigm for parameter estimation in Earth surface systems,and developing the data-driven machine learning model is a crucial way for paradigm transformation in geoscience research.The coupling of mechanism and learning models can realize the combination of“rationalism”and“empiricism”,which is one of the most concerned research hotspots.In this paper,for remote sensing inversion and dynamic simulation,we deeply analyze the internal bottleneck and complementarity of mechanism and learning models and build a coupling paradigm framework with mechanism-learning cascading model,learning-embedded mechanism model,and mechanism-infused learning model.We systematically summarize ten specific coupling methods,including preprocessing and initialization,intermediate variable transfer,post-refinement processing,model substitution,model adjustment,model solution,input variable constraints,objective function constraints,model structure constraints,hybrid,etc.,and analyze the main existing problems and future challenges.The research aims to provide a new perspective for in-depth understanding and application of the mechanism-learning coupling model and provide theoretical and technical support for improving the inversion and simulation capabilities of parameters in Earth surface systems and serving the development of Earth system science.

Multi-temporal urban semantic understanding based on GF-2 remote sensing imagery:from tri-temporal datasets to multi-task mapping

High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection(BCD)and semantic change detection(SCD)simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location,ignoring the changed classes.Public SCD datasets are rare but much needed.To solve the above problems,a tri-temporal SCD dataset made up of Gaofen-2(GF-2)remote sensing imagery(with 11 LULC classes and 60 change directions)was built in this study,namely,the Wuhan Urban Semantic Understanding(WUSU)dataset.Popular deep learning based methods for LULC classification,BCD and SCD are tested to verify the reliability of WUSU.A Siamese-based multi-task joint framework with a multi-task joint loss(MJ loss)named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification,BCD and SCD,compared to the state-of-the-art(SOTA)methods.Finally,a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUsU dataset and the ChangeMJ framework have good application values.

A survey on vision-based UAV navigation

Research on unmanned aerial vehicles(UAV)has been increasingly popular in the past decades,and UAVs have been widely used in industrial inspection,remote sensing for mapping&surveying,rescuing,and so on.Nevertheless,the limited autonomous navigation capability severely hampers the application of UAVs in complex environments,such as GPS-denied areas.Previously,researchers mainly focused on the use of laser or radar sensors for UAV navigation.With the rapid development of computer vision,vision-based methods,which utilize cheaper and more flexible visual sensors,have shown great advantages in the field of UAV navigation.The purpose of this article is to present a comprehensive literature review of the vision-based methods for UAV navigation.Specifically on visual localization and mapping,obstacle avoidance and path planning,which compose the essential parts of visual navigation.Furthermore,throughout this article,we will have an insight into the prospect of the UAV navigation and the challenges to be faced.

Advances in spaceborne hyperspectral remote sensing in China

With the maturation of satellite technology,Hyperspectral Remote Sensing(HRS)platforms have developed from the initial ground-based and airborne platforms into spaceborne platforms,which greatly promotes the civil application of HRS imagery in the fields of agriculture,forestry,and environmental monitoring.China is playing an important role in this evolution,especially in recent years,with the successful launch and operation of a series of civil hyper-spectral spacecraft and satellites,including the Shenzhou-3 spacecraft,the Gaofen-5 satellite,the SPARK satellite,the Zhuhai-1 satellite network for environmental and resources monitoring,the FengYun series of satellites for meteorological observation,and the Chang’E series of spacecraft for planetary exploration.The Chinese spaceborne HRS platforms have various new characteristics,such as the wide swath width,high spatial resolution,wide spectral range,hyperspectral satellite networks,and microsatellites.This paper focuses on the recent progress in Chinese spaceborne HRS,from the aspects of the typical satellite systems,the data processing,and the applications.In addition,the future development trends of HRS in China are also discussed and analyzed.

Review on graph learning for dimensionality reduction of hyperspectral image

Graph learning is an effective manner to analyze the intrinsic properties of data.It has been widely used in the fields of dimensionality reduction and classification for data.In this paper,we focus on the graph learning-based dimensionality reduction for a hyperspectral image.Firstly,we review the development of graph learning and its application in a hyperspectral image.Then,we mainly discuss several representative graph methods including two manifold learning methods,two sparse graph learning methods,and two hypergraph learning methods.For manifold learning,we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph.For sparse graph,we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph.For hypergraph learning,we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data.