Improving the accuracy of random forest‐based land‐use classification using fused images and digital surface models produced via different interpolation methods

2021 ◽  
Author(s):  
Alper Akar
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Land Use Classification ◽  
Interpolation Methods ◽  
Surface Models

scholarly journals A New Approach to Refining Land Use Types: Predicting Point-of-Interest Categories Using Weibo Check-in Data

2020 ◽  
Vol 9 (2) ◽  
pp. 124 ◽  
Author(s):  
Xucai Zhang ◽  
Yeran Sun ◽  
Anyao Zheng ◽  
Yu Wang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Urban Planning ◽  
Human Mobility ◽  
Activity Patterns ◽  
Land Use Classification ◽  
Social Sensing ◽  
Point Of Interest ◽  
Land Use Types ◽  
Activity Behavior

The information of land use plays an important role in urban planning and optimizing the allocation of resources. However, traditional land use classification is imprecise. For instance, the type of commercial land is highly filled with the categories of shopping, eating, etc. The number of mixed-use lands is increasingly growing nowadays, and these lands sometimes are too mixed to be well investigated by conventional approaches such as remote sensing technology. To address this issue, we used a new social sensing approach to classify land use according to human mobility and activity patterns. Previous studies used other social sensing approaches to predict land use types at the parcel or the area level, whilst fine-grained point-of-interest (POI)-level land use data are likely to more useful in urban planning. To abridge this research gap, we proposed a new social sensing approach dedicated to classifying land use at a finer scale (i.e., POI-level or building level) according to human mobility and activity patterns reflected by location-based social network (LBSN) data. Specifically, we firstly investigated spatial and temporal patterns of human mobility and activity behavior using check-in data from a popular Chinese LBSN named Sina Weibo and subsequently applied those patterns to predicting the category of POI to refine urban land use classification in Guangzhou, China. In this study, we applied three classification methods (i.e., naive Bayes, support vector machines, and random forest) to recognize category of a certain POI by spatial and temporal features of human mobility and activity behavior as well as POIs’ locational characteristics. Random forest outperformed the other two methods and obtained an overall accuracy of 72.21%. Apart from that, we compared the results of the different rules in filtering check-in samples. The comparison results show that a reasonable rule to select samples is essential for predicting the category of POI. Moreover, the approach proposed in this study can be potentially applied to identifying functions of buildings according to visitors’ mobility and activity behavior and buildings’ locational characteristics.

scholarly journals Development of Land Use Regression Model for Seasonal Variation of Nitrogen Dioxide (NO2) in Lahore, Pakistan

2021 ◽  
Vol 13 (9) ◽  
pp. 4933
Author(s):  
Saimar Pervez ◽  
Ryuta Maruyama ◽  
Ayesha Riaz ◽  
Satoshi Nakai
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Urban Areas ◽  
Industrial Development ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Land Use Classification ◽  
Land Use Regression ◽  
Air Exposure

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

scholarly journals Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

2021 ◽  
Vol 10 (8) ◽  
pp. 503
Author(s):  
Hang Liu ◽  
Riken Homma ◽  
Qiang Liu ◽  
Congying Fang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Land ◽  
Urban Land Use ◽  
Random Forest Model ◽  
Growth Trend ◽  
Related Factors ◽  
Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

scholarly journals A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

2021 ◽  
Vol 13 (6) ◽  
pp. 3070
Author(s):  
Patrycja Szarek-Iwaniuk
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

scholarly journals A Cycle Gan Approach for Heterogeneous Domain Adaptation in Land Use Classification

2020 ◽  
Author(s):  
Claire Voreiter ◽  
Jean-Christophe Burnel ◽  
Pierre Lassalle ◽  
Marc Spigai ◽  
Romain Hugues ◽  
...  
Keyword(s):  
Land Use ◽  
Domain Adaptation ◽  
Land Use Classification

Neighbourhood places, collective efficacy and crime: A longitudinal perspective

Urban Studies ◽  
2021 ◽  
pp. 004209802110088
Author(s):  
Renee Zahnow ◽  
Jonathan Corcoran ◽  
Anthony Kimpton ◽  
Rebecca Wickes
Keyword(s):  
Land Use ◽  
Social Control ◽  
Social Interactions ◽  
Collective Efficacy ◽  
Social Cohesion ◽  
Mixed Effects ◽  
Land Use Classification ◽  
Social Survey ◽  
Panel Models ◽  
Over Time

Neighbourhood places like shops, cafes and parks support a variety of social interactions ranging from the ephemeral to the intimate. Repeated interactions at neighbourhood places over time lay the foundation for the development of social cohesion and collective efficacy. In this study, we examine the proposition that changes in the presence or arrangement of neighbourhood places can destabilise social cohesion and collective efficacy, which has implications for crime. Using spatially integrated crime, social survey and parcel-level land-use classification data, we estimate mixed effects panel models predicting changes in theft and nuisance crimes across 147 Australian neighbourhoods. The findings are consistent with neighbourhood social control and crime opportunity theories. Neighbourhood development – indicated by fewer vacant properties and fewer industrial and agricultural sites – is associated with higher collective efficacy and less crime over time. Conversely, introducing more restaurants, transit stations and cinemas is associated with higher theft and nuisance over time regardless of neighbourhood collective efficacy. We argue that the addition of socially conducive places can leave neighbourhoods vulnerable to crime until new patterns of sociability emerge and collective efficacy develops.

Sign in / Sign up

Export Citation Format

Share Document