Scene-Change Detection Based on Multi-Feature-Fusion Latent Dirichlet Allocation Model for High-Spatial-Resolution Remote Sensing Imagery

Remote sensing images are primary data sources for land use classification. High spatial resolution images enable more accurate analysis and identification of land cover types. However, a higher spatial resolution also brings new challenges to the existing classification methods. In the low-level feature spaces of remote sensing images, it is difficult to improve classification performance by modifying classifiers. Probabilistic topic models can connect low-level features and high-level semantics of remote sensing images. Latent Dirichlet allocation (LDA) models are representatives of probabilistic topic models. However, at present, probabilistic topic models are mainly adopted for scene classification and image retrieval in remote sensing image analysis only. In this study, multiscale segmentation was employed to construct bag-of-words (BoW) representations of high-resolution images. The segmented patches were then utilized as “image documents.” A structural topic model was used with an LDA model to import spatial information from the image documents at two levels: topical prevalence and topical content in the form of covariates. In this way, latent topic features in image documents can be more accurately deduced. The proposed method showed more satisfactory classification performance than standard LDA models and demonstrated a certain degree of robustness against the changes in the segmentation scale. Acknowledgement for the data support from “Yangtze River Delta Science Data Center, National Earth System Science Data Center, National Science & Technology Infrastructure of China ( http://nnu.geodata.cn:8008 )”.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

The study aims to investigate the integration of artificial intelligence technology into Weibo sentiment analysis, aiming to enhance the effectiveness of Weibo in human-computer interaction education. Initially, the Weibo sentiment dictionary is created, and a conventional model for sentiment analysis of user-forwarded Weibo is introduced, specifically the Latent Dirichlet Allocation (LDA) model. Then, the deep learning models in the field of artificial intelligence, namely, the convolutional neural network (CNN) model and the long short-term memory network (LSTM) model, are proposed. Tencent Weibo data set is obtained through Application Programming Interface (API) crawler. The experimental environment of the deep learning model is analyzed and the data set is preprocessed. The results show that when the number of topics is 120, the relative maximum value of F1 is 69.92% and 69.96% with and without the introduction of emotional features in the LDA model, respectively. The accuracy of CNN model and LSTM model is 0.793 and 0.849, respectively. In the three cases of user characteristics, user characteristics + Weibo features, and use characteristics + Weibo features + relationship characteristics, the polarity of the forwarded comments of the LDA model doesn’t change much. In conclusion, the LDA model demonstrates universality and accuracy in sentiment analysis of user-forwarded Weibo, while LSTM proves to be more suitable for sentiment classification in this context. Leveraging the LDA deep learning model, LSTM effectively analyzes the sentiment of users forwarding Weibo. These findings serve as an experimental foundation for the efficient integration of Weibo in human-computer interaction education.

Scene change detection is the process of identifying the differences between the multi-temporal image scenes at the semantic level, which has significant potential in the application of urban development and land management. In this paper, we propose a novel deep convolution canonical correlation analysis neural network (DCCANet) architecture, which could consider the spectral-spatial-temporal correlation feature for scene change detection in remote sensing images. For this purpose, we put together the convolutional neural networks (CNNs) and the deep canonical correlation analysis (DCCA) into the end-to-end network. The CNN could get the spectral-spatial feature information for scene representation, while the later could enhance the temporal correlation by nonlinear high- dimensional transformations between the multi-temporal image scenes for scene change detection. Experiments with high-resolution remote sensing image scene datasets demonstrated that our proposed approach can get a better performance in scene classification and change detection.

This study aimed to assess the different needs of patients with breast cancer and their families in online health communities at different treatment phases using a Latent Dirichlet Allocation (LDA) model. Using Python, breast cancer-related posts were collected from two online health communities: patient-to-patient and patient-to-doctor. After data cleaning, eligible posts were categorized based on the treatment phase. Subsequently, an LDA model identifying the distinct need-related topics for each phase of treatment, including data preprocessing and LDA topic modeling, was established. Additionally, the demographic and interactive features of the posts were manually analyzed. We collected 84,043 posts, of which 9504 posts were included after data cleaning. Early diagnosis and rehabilitation treatment phases had the highest and lowest number of posts, respectively. LDA identified 11 topics: three in the initial diagnosis phase and two in each of the remaining treatment phases. The topics included disease outcomes, diagnosis analysis, treatment information, and emotional support in the initial diagnosis phase; surgical options and outcomes, postoperative care, and treatment planning in the perioperative treatment phase; treatment options and costs, side effects management, and disease prognosis assessment in the non-operative treatment phase; diagnosis and treatment options, disease prognosis, and emotional support in the relapse and metastasis treatment phase; and follow-up and recurrence concerns, physical symptoms, and lifestyle adjustments in the rehabilitation treatment phase. The needs of patients with breast cancer and their families differ across various phases of cancer therapy. Therefore, specific information or emotional assistance should be tailored to each phase of treatment based on the unique needs of patients and their families.

To improve hydro-chemical modeling and forecasting, there is a need to better understand flood-induced variability in water chemistry and the processes controlling it in watersheds. In the literature, assumptions are often made, for instance, that stream chemistry reacts differently to rainfall events depending on the season; however, methods to verify such assumptions are not well developed. Often, few floods are studied at a time and chemicals are used as tracers. Grouping similar events from large multivariate datasets using principal component analysis and clustering methods helps to explain hydrological processes; however, these methods currently have some limits (definition of flood descriptors, linear assumption, for instance). Most clustering methods have been used in the context of regionalization, focusing more on mapping results than on understanding processes. In this study, we extracted flood patterns using the probabilistic Latent Dirichlet Allocation (LDA) model, its first use in hydrology, to our knowledge. The LDA method allows multivariate temporal datasets to be considered without having to define explanatory factors beforehand or select representative floods. We analyzed a multivariate dataset from a long-term observatory (Kervidy-Naizin, western France) containing data for four solutes monitored daily for 12 years: nitrate, chloride, dissolved organic carbon, and sulfate. The LDA method extracted four different patterns that were distributed by season. Each pattern can be explained by seasonal hydrological processes. Hydro-meteorological parameters help explain the processes leading to these patterns, which increases understanding of flood-induced variability in water quality. Thus, the LDA method appears useful for analyzing long-term datasets.

Understanding and predicting the effect of global change phenomena on biodiversity is challenging given that biodiversity data are highly multivariate, containing information from tens to hundreds of species in any given location and time. The Latent Dirichlet Allocation (LDA) model has been recently proposed to decompose biodiversity data into latent communities. While LDA is a very useful exploratory tool and overcomes several limitations of earlier methods, it has limited inferential and predictive skill given that covariates cannot be included in the model. We introduce a modified LDA model (called LDAcov) which allows the incorporation of covariates, enabling inference on the drivers of change of latent communities, spatial interpolation of results, and prediction based on future environmental change scenarios. We show with simulated data that our approach to fitting LDAcov is able to estimate well the number of groups and all model parameters. We illustrate LDAcov using data from two experimental studies on the long‐term effects of fire on southeastern Amazonian forests in Brazil. Our results reveal that repeated fires can have a strong impact on plant assemblages, particularly if fuel is allowed to build up between consecutive fires. The effect of fire is exacerbated as distance to the edge of the forest decreases, with small‐sized species and species with thin bark being impacted the most. These results highlight the compounding impacts of multiple fire events and fragmentation, a scenario commonly found across the southern edge of Amazon. We believe that LDAcov will be of wide interest to scientists studying the effect of global change phenomena on biodiversity using high‐dimensional datasets. Thus, we developed the R package LDAcov to enable the straightforward use of this model.

Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions.

How to recognize human action from videos captured by modern cameras efficiently and effectively is a challenge in real applications. Traditional methods which need professional analysts are facing a bottleneck because of their shortcomings. To cope with the disadvantage, methods based on computer vision techniques, without or with only a few human interventions, have been proposed to analyse human actions in videos automatically. This paper provides a method combining the three dimensional Scale Invariant Feature Transform (SIFT) detector and the Latent Dirichlet Allocation (LDA) model for human motion analysis. To represent videos effectively and robustly, we extract the 3D SIFT descriptor around each interest point, which is sampled densely from 3D Space-time video volumes. After obtaining the representation of each video frame, the LDA model is adopted to discover the underlying structure-the categorization of human actions in the collection of videos. Public available standard datasets are used to test our method. The concluding part discusses the research challenges and future directions.

The extent of anthropogenic land use in watersheds determines the amount of pollutants discharged to streams. This indirectly and directly affects stream water quality and biological health. Most studies have therefore focused on ways to reduce non-point pollution sources to streams from the surrounding land use in watersheds. However, the mechanistic pathways between land use and the deterioration of stream water quality and biological assemblages remain unclear. This study estimated a structural equation model (SEM) representing the impact of agricultural and urban land use on water quality and the benthic macroinvertebrate index (BMI) using IBM AMOS in the Nam-Han river systems, South Korea. The estimated SEM showed that the percent of urban and agricultural land in the watersheds significantly affected both the water quality and the BMI of the streams. Specifically, a higher percent of urban land use had directly increased the biochemical oxygen demand (BOD) and total phosphorus (TP), and deteriorated the BMI of streams. Similarly, higher proportions of agricultural land use had also directly increased the BOD, total nitrogen (TN), and total phosphorus (TP) concentrations, and lowered the BMI of streams. In addition, it was observed that the percent of urban and agricultural land use had indirectly deteriorated the BMI through increased BOD. However, we were not able to observe any significant indirect effect of the percent of urban and agricultural land use through increased nutrients including TN and TP. These results indicate that increased urban and agricultural land use in the watersheds had directly and indirectly affected the physicochemical characteristics and benthic macroinvertebrate communities in streams. Our findings emphasize the need to develop more elaborate environmental management and restoration strategies to improve the water quality and biological status of streams.

Large-scale development of urban land use has led to change of a variety of natural processes and ecological processes, resulting in complex eco-environmental consequences. The objective of this study was to analyze the urban land use and its impact on air environment effect in Chengdu, western China from 1992 to 2008 following the RS (Remote Sensing) and GIS technique. The environmental effects data of urban land use was extracted and analyzed by overlaying layers of urban land use and the density of nitrogen dioxide and total suspended particulate matter in sampling points data concerning to the air quality of the environment in Chengdu based on GIS spatial analysis method. The results show that the main feature of urban land use change was substantial reduction of cultivated land and construction land and forest land increased significantly within the study area from1992 to 2008. The temporal-spatial change was notable in study period time. Land use has a significant impact on urban air environment, the chroma change of nitrogen dioxide derived from forest land was obvious, the area occupied by different nitrogen dioxide chroma was the largest. The urban land use impact on the highest class chroma of total suspended particulate matter was notable and its area was the greatest. The results show also the spatial distribution of nitrogen dioxide chroma and total suspended particulate matter chroma in study area is reduced following from Qingbaijiang District-Xindu District-downtown to both sides. The spatial distribution of industry, mining and traffic land is basically the same chroma spatial distribution. Therefore, the results of this study provide a scientific basis for improvement air environment quality, the urban sustainable development and a scientific response for decisions from the municipal governments.

We explored land use, fish assemblage structure, and stream habitat associations in 20 catchments in Opequon Creek watershed, West Virginia. The purpose was to determine the relative importance of urban and agriculture land use on stream biotic integrity, and to evaluate the spatial scale (i.e., whole-catchment vs riparian buffer) at which land use effects were most pronounced. We found that index of biological integrity (IBI) scores were strongly associated with extent of urban land use in individual catchments. Sites that received ratings of poor or very poor based on IBI scores had > 7% of urban land use in their respective catchments. Habitat correlations suggested that urban land use disrupted flow regime, reduced water quality, and altered stream channels. In contrast, we found no meaningful relationship between agricultural land use and IBI at either whole-catchment or riparian scales despite strong correlations between percent agriculture and several important stream habitat measures, including nitrate concentrations, proportion of fine sediments in riffles, and the abundance of fish cover. We also found that variation in gradient (channel slope) influenced responses of fish assemblages to land use. Urban land use was more disruptive to biological integrity in catchments with steeper channel slopes. Based on comparisons of our results in the topographically diverse Opequon Creek watershed with results from watersheds in flatter terrains, we hypothesize that the potential for riparian forests to mitigate effects of deleterious land uses in upland portions of the watershed is inversely related to gradient.

Revealing the spatial–temporal pattern and convergence characteristics of urban land use efficiency has important guiding significance for adjusting and optimizing the regional urban land use structure. Taking the provincial units in China as the research object, the urban land use efficiency evaluation system considering the unexpected output was constructed, and the slack-based measure (SBA) model was used to quantitatively measure the provincial urban land use efficiency from 2000 to 2020. The exploratory spatial data analysis (ESDA) model and spatial convergence index were combined to reveal the spatial–temporal pattern and convergence characteristics of provincial urban land use efficiency. The results showed that the provincial urban land use efficiency has been continuously improving, with regional differences as shown in eastern region > northeast region > central region > western region. Moran’s I of provincial urban land use efficiency was greater than 0, there was a positive spatial correlation, and the clustering feature became increasingly significant. The spatial form of LISA was characterized by “small agglomeration and large dispersion”; the H(High)-H(High) type was clustered in the Yangtze River Delta and Beijing–Tianjin–Hebei, while the L(Low)-L(Low) type was clustered in Xizang, Xinjiang and Qinghai. There was a σ convergence in provincial urban land use efficiency, and there was significant absolute β convergence and conditional β convergence of provincial urban land use efficiency. The results showed that the differences in provincial urban land use efficiency were shrinking, showing a “catch-up effect”, and converging to their respective stable states over time. Based on the analysis of the spatial–temporal pattern and convergence characteristics of provincial urban land use efficiency in China, we could provide a direction for the optimization of the urban land use structure and efficiency improvement in China, in order to narrow the differences in urban land use efficiency in China’s four major regions.

The accessibility provided by the transportation system plays an essential role in driving urban growth and urban functional land use changes. Conventional studies on land use simulation usually simplified the accessibility as proximities and adopted the grid-based simulation strategy, leading to the insufficiencies of characterizing spatial geometry of land parcels and simulating subtle land use changes among urban functional types. To overcome these limitations, an Accessibility-interacted Vector-based Cellular Automata (A-VCA) model was proposed for the better simulation of realistic land use change among different urban functional types. The accessibility at both local and zonal scales derived from actual travel time data was considered as a key driver of fine-scale urban land use changes and was integrated into the vector-based CA simulation process. The proposed A-VCA model was tested through the simulation of urban land use changes in the City of Toronto, Canada, during 2012–2016. A vector-based CA without considering the driving factor of accessibility (VCA) and a popular grid-based CA model (Future Land Use Simulation, FLUS) were also implemented for comparisons. The simulation results reveal that the proposed A-VCA model is capable of simulating fine-scale urban land use changes with satisfactory accuracy and good morphological feature (kappa = 0.907, figure of merit = 0.283, and cumulative producer’s accuracy = 72.83% ± 1.535%). The comparison also shows significant outperformance of the A-VCA model against the VCA and FLUS models, suggesting the effectiveness of the accessibility-interactive mechanism and vector-based simulation strategy. The proposed model provides new tools for a better simulation of fine-scale land use changes and can be used in assisting the formulation of urban and transportation planning.

This paper presents an investigation of topic modeling in embedding spaces performances in the context of depression assessment. Using the textual content of social media users from the eRisk 2018 dataset, a classification task is performed employing features generated from the Embedded Topic Model. To set contrast with traditional topic modeling, a full comparison with the Latent Dirichlet Allocation model is accomplished. An extensive range of topics and different preprocessing strategies are studied to demonstrate the efficiency of the models. Our results show a noteworthy improvement in the explored task from the application of the novel topic modeling approach.