A Multi-scale Polygonal Object Matching Method Based on MBR Combinatorial Optimization Algorithm

Aiming to solving the problem of positional discrepancy of corresponding objects in multi-scale polygonal object matching and that the potential matching pairs can't be directly identified by the method of areal overlapping, it is proposed that a multi-scale polygonal object matching method based on minimum bounding rectangle combinatorial optimization algorithm. The basic idea of our method is that:①identifying the potential matching pairs of 1:1, 1:N and M:N with combinatorial algorithm and simple shape characteristic;②establishing multi-characteristic artificial neural network model to evaluate these potential matching pairs. The proposed method is demonstrated in the experiment of matching between 1:2000 and 1:10000 polygonal objects of residential buildings and industrial facilities in Zhoushan, Zhejiang Province. The experimental results showed that the proposed matching method show superior performance against a method of area overlapping and artificial neural network. Its precision and recall are 96.5% and 89.0% under the positional discrepancy scenario, and it successfully match 1:0, 1:1,1:N and M:N matching pair

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Perovskites have attracted increasing attention because of their excellent physical and chemical properties in various fields, exhibiting a universal formula of ABO3 with matching compatible sizes of A-site and B-site cations. In this work, four different prediction models of machine learning algorithms, including support vector regression based on radial basis kernel function (SVM-RBF), ridge regression (RR), random forest (RF), and back propagation neural network (BPNN), are established to predict the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy of perovskite materials. Combined with the fitting diagrams of the predicted values and DFT calculated values, the results show that SVM-RBF has a smaller bias in predicting the crystal volume. RR has a smaller bias in predicting the thermodynamic stability. RF has a smaller bias in predicting the formation energy, crystal volume, and thermodynamic stability. BPNN has a smaller bias in predicting the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy. Obviously, different machine learning algorithms exhibit different sensitivity to data sample distribution, indicating that we should select different algorithms to predict different performance parameters of perovskite materials

Patterns of Nighttime Crowd Flows in Tourism Cities Based on Taxi Data—Take Haikou Prefecture as an Example

The study of patterns of crowd flows represents an emerging and expanding research field. The most straightforward and efficient approach to investigate the patterns of crowd flows is to concentrate on traffic flow. However, assessments of simple point-to-point movement frequently lack universal validity, and little research has been conducted on the regularity of nighttime movement. Due to the suspension of public transportation at night, taxi orders are critical in capturing the features of nighttime crowd flows in a tourism city. Using Haikou as an example, this paper proposes a mixed Geogrid Spatio-temporal model (MG-STM) for the tourism city in order to address the challenges. Firstly, by collecting the pick-up/drop-off/in-out flow of crowds, this research uses DCNMF dimensionality reduction to extract semi-supervised spatio-temporal variation features and the K-Means clustering method to determine the cluster types of nighttime crowd flows’ changes in each geogrid. Secondly, by constructing a mixed-evaluation model based on LJ1-01 nighttime light data, crowd flows’ clusters, and land use data in geogrid-based regions, the pattern of nighttime crowd flows in urban land use areas is successfully determined. The results suggest that MG-STM can estimate changes in the number of collective flows in various regions of Haikou effectively and appropriately. Moreover, population density of land use areas shows a high positive correlation with the lag of crowd flows. Each 5% increase in population density results in a 30-min delay in the peak of crowd flows. The MG-STM will be extremely beneficial in developing and implementing systems for criminal tracking and pandemic prevention

An on-demand scheme driven by the knowledge of geospatial distribution for large-scale high-resolution impervious surface mapping

Impervious surface has become one of the key factors of regional environmental problems and disasters. There rises an urgent need for mapping large-scale high-resolution impervious surfaces to help delicate modeling and overall planning. In the existing large-scale impervious surface mapping studies, there are many studies and products at medium resolution (10 ~ 100 m), some of which are with time series; while only few are at high resolution (<10 m), but not appeared with temporal updates. In the conventional scheme for large-scale high-resolution mapping, plenty of high-resolution imagery (HRI) are required to cover the entire large area and achieve wider coverage as much as possible. The high cost of obtaining abundant HRI limits large-scale high-resolution impervious surface mapping, leading to rare high-resolution impervious surface study at large scales. To alleviate the difficulties in the conventional scheme, an on-demand HRI scheme was proposed based on geos`patial distribution knowledge (low overall proportion and high geospatial aggregation) of impervious surface at large scales, with the advantage of reducing the demand for HRI while ensuring coverage. Adopting the information and knowledge obtained from medium-resolution impervious surface data at large scales, the proposed on-demand HRI scheme only requires HRI where it is really needed, rather than for the entire large area as in the conventional scheme. Reducing the study area by a morphology-based method and selecting necessary HRI by the bidirectional image filtering (BIF) strategy, the on-demand HRI scheme has a smaller requirement of the HRI resources. The proposed on-demand HRI scheme and conventional scheme were implemented and discussed in five study areas. The results show that compared with the conventional method, the proposed on-demand HRI scheme reduced the requirement of HRI while ensuring coverage; and in the case of insufficient HRI coverage, it can reduce the HRI requirements while narrowing data gaps in the large-scale high-resolution impervious surface result. It was also found that the proposed scheme performs well in large-scale areas with low overall proportion and high geospatial aggregation of impervious surface found in the medium-resolution remote sensing product. Additionally, the on-demand HRI scheme will be also useful for large-scale high-resolution mapping of other land cover types

Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN

Spatial object matching is one of the fundamental technologies used for updating and merging spatial data. This study focused mainly on the matching optimization of multiscale spatial polygonal objects. We proposed a granularity factor evaluation index that was developed to promote the recognition ability of complex matches in multiscale spatial polygonal object matching. Moreover, we designed the granularity factor matching model based on a backpropagation neural network (BPNN) and designed a multistage matching workflow. Our approach was validated experimentally using two topographical datasets at two different scales: 1:2000 and 1:10,000. Our results indicate that the granularity factor is effective both in improving the matching score of complex matching and reducing the occurrence of missing matching, and our matching model is suitable for multiscale spatial polygonal object matching, with a high precision and recall reach of 97.2% and 90.6%

Integrating Remote Sensing and Spatiotemporal Analysis to Characterize Artificial Vegetation Restoration Suitability in Desert Areas: A Case Study of Mu Us Sandy Land

One of the major barriers to hindering the sustainable development of the terrestrial environment is the desertification process, and revegetation is one of the most significant duties in anti-desertification. Desertification deteriorates land ecosystems through species decline, and remote sensing is becoming the most effective way to monitor desertification. Mu Us Sandy Land is the fifth largest desert and the representative area under manmade vegetation restorations in China. Therefore, it is essential to understand the spatiotemporal characteristics of artificial desert transformation for seeking the optimal revegetation location for future restoration planning. However, there are no previous studies focusing on exploring regular patterns between the spatial distribution of vegetation restoration and human-related geographical features. In this study, we use Landsat satellite data from 1986 to 2020 to achieve annual monitoring of vegetation change by a threshold segmentation method, and then use spatiotemporal analysis with Open Street Map (OSM) data to explore the spatiotemporal distribution pattern between vegetation occurrence and human-related features. We construct an artificial vegetation restoration suitability index (AVRSI) by considering human-related features and topographical factors, and we assess artificial suitability for vegetation restoration by mapping methods based on that index and the vegetation distribution pattern. The AVRSI can be commonly used for evaluating restoration suitability in Sandy areas and it is tested acceptable in Mu Us Sandy Land. Our results show during this period, the segmentation threshold and vegetation area of Mu Us Sandy Land increased at rates of 0.005/year and 264.11 km2/year, respectively. Typically, we found the artificial restoration vegetation suitability in Mu Us area spatially declines from southeast to northwest, but eventually increases in the most northwest region. This study reveals the revegetation process in Mu Us Sandy Land by figuring out its spatiotemporal vegetation change with human-related features and maps the artificial revegetation suitability

Integrating Remote Sensing and Spatiotemporal Analysis to Characterize Artificial Vegetation Restoration Suitability in Desert Areas: A Case Study of Mu Us Sandy Land

One of the major barriers to hindering the sustainable development of the terrestrial environment is the desertification process, and revegetation is one of the most significant duties in anti-desertification. Desertification deteriorates land ecosystems through species decline, and remote sensing is becoming the most effective way to monitor desertification. Mu Us Sandy Land is the fifth largest desert and the representative area under manmade vegetation restorations in China. Therefore, it is essential to understand the spatiotemporal characteristics of artificial desert transformation for seeking the optimal revegetation location for future restoration planning. However, there are no previous studies focusing on exploring regular patterns between the spatial distribution of vegetation restoration and human-related geographical features. In this study, we use Landsat satellite data from 1986 to 2020 to achieve annual monitoring of vegetation change by a threshold segmentation method, and then use spatiotemporal analysis with Open Street Map (OSM) data to explore the spatiotemporal distribution pattern between vegetation occurrence and human-related features. We construct an artificial vegetation restoration suitability index (AVRSI) by considering human-related features and topographical factors, and we assess artificial suitability for vegetation restoration by mapping methods based on that index and the vegetation distribution pattern. The AVRSI can be commonly used for evaluating restoration suitability in Sandy areas and it is tested acceptable in Mu Us Sandy Land. Our results show during this period, the segmentation threshold and vegetation area of Mu Us Sandy Land increased at rates of 0.005/year and 264.11 km2/year, respectively. Typically, we found the artificial restoration vegetation suitability in Mu Us area spatially declines from southeast to northwest, but eventually increases in the most northwest region. This study reveals the revegetation process in Mu Us Sandy Land by figuring out its spatiotemporal vegetation change with human-related features and maps the artificial revegetation suitability

Novel Grid Collection and Management Model of Remote Sensing Change Detection Samples

Peer reviewed: TrueAcknowledgements: We appreciate the constructive suggestions and comments from the editor and anonymous reviewers.Publication status: PublishedRemote sensing data have become an important data source for urban and regional change detection, owing to their advantages of authenticity, objectivity, immediacy, and low cost. The method of collection and management for remote sensing change detection samples (RS_CDS) assumes a crucial role in the effectiveness of remote sensing intelligent change detection (RSICD). To achieve rapid collection and real-time sharing of RS_CDS, this study proposes a grid collection and management model of RS_CDS based on GeoSOT (GCAM-GeoSOT), including the grid collection method of RS_CDS (GCM-SD) and grid management method of RS_CDS (GMM-SD). To verify the feasibility and retrieval efficiency of GMM-SD, Oracle and PostgreSQL databases were combined and the retrieval efficiency and database capacity were compared with the corresponding spatial databases, Oracle Spatial and PostgreSQL + PostGIS, respectively. The experimental results showed that GMM-SD not only ensures the reasonable capacity consumption of the database but also has a higher retrieval efficiency for the RS_CDS. This results in a noteworthy comprehensive performance enhancement, with a 47.63% improvement compared to Oracle Spatial and a 40.24% improvement compared to PostgreSQL + PostGIS.</jats:p

Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting

Due to the periodic and dynamic changes of traffic flow and the spatial&ndash;temporal coupling interaction of complex road networks, traffic flow forecasting is highly challenging and rarely yields satisfactory prediction results. In this paper, we propose a novel methodology named the Augmented Multi-component Recurrent Graph Convolutional Network (AM-RGCN) for traffic flow forecasting by addressing the problems above. We first introduce the augmented multi-component module to the traffic forecasting model to tackle the problem of periodic temporal shift emerging in traffic series. Then, we propose an encoder&ndash;decoder architecture for spatial&ndash;temporal prediction. Specifically, we propose the Temporal Correlation Learner (TCL) which incorporates one-dimensional convolution into LSTM to utilize the intrinsic temporal characteristics of traffic flow. Moreover, we combine TCL with the graph convolutional network to handle the spatial&ndash;temporal coupling interaction of the road network. Similarly, the decoder consists of TCL and convolutional neural networks to obtain high-dimensional representations from multi-step predictions based on spatial&ndash;temporal sequences. Extensive experiments on two real-world road traffic datasets, PEMSD4 and PEMSD8, demonstrate that our AM-RGCN achieves the best results