scholarly journals Land-Use Change Detection with Convolutional Neural Network Methods

Environments ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 25 ◽  
Author(s):  
Cong Cao ◽  
Suzana Dragićević ◽  
Songnian Li
Keyword(s):  
Land Use ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Land Use Classification ◽  
Limited Data ◽  
Residential Areas ◽  
Change Analysis ◽  
Svm Model ◽  
Network Methods ◽  
The City

Convolutional neural networks (CNN) have been used increasingly in several land-use classification tasks, but there is a need to further investigate its potential. This study aims to evaluate the performance of CNN methods for land classification and to identify land-use (LU) change. Eight transferred CNN-based models were fully evaluated on remote sensing data for LU scene classification using three pre-trained CNN models AlexNet, GoogLeNet, and VGGNet. The classification accuracy of all the models ranges from 95% to 98% with the best-performed method the transferred CNN model combined with support vector machine (SVM) as feature classifier (CNN-SVM). The transferred CNN-SVM model was then applied to orthophotos of the northeastern Cloverdale as part of the City of Surrey, Canada from 2004 to 2017 to perform LU classification and LU change analysis. Two sources of datasets were used to train the CNN–SVM model to solve a practical issue with the limited data. The obtained results indicated that residential areas were expanding by creating higher density, while green areas and low-density residential areas were decreasing over the years, which accurately indicates the trend of LU change in the community of Cloverdale study area.

scholarly journals LAND USE CLASSIFICATION FROM COMBINED USE OF REMOTE SENSING AND SOCIAL SENSING DATA

2018 ◽  
Vol XLII-4 ◽  
pp. 33-39 ◽  
Author(s):  
A. S. Anugraha ◽  
H.-J. Chu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Human Behavior ◽  
Remote Sensing Data ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Classification ◽  
Social Sensing ◽  
Sensing Data ◽  
The City

<p><strong>Abstract.</strong> Large amounts of data can be sensed and analyzed to discover patterns of human behavior in cities for the benefit of urban authorities and citizens, especially in the areas of traffic forecasting, urban planning, and social science. In New York, USA, social sensing, remote sensing, and urban land use information support the discovery of patterns of human behavior. This research uses two types of openly accessible data, namely, social sensing data and remote sensing data. Bike and taxi data are examples of social sensing data, whereas sentinel remote sensed imagery is an example of remote sensing data. This research aims to sense and analyze the patterns of human behavior and to classify land use from the combination of remote sensing data and social sensing data. A decision tree is used for land use classification. Bike and taxi density maps are generated to show the locations of people around the city during the two peak times. On the basis of a geographic information system, the maps also reflect the residential and office areas in the city. The overall accuracy of land use classification after the consideration of social sensing data is 85.3%. The accuracy assessment shows that the combination of remote sensing data and social sensing data facilitates accurate urban land use classification.</p>

scholarly journals EVALUATION OF THE PERFORMANCE OF SAR AND SAR-OPTICAL FUSED DATASET FOR CROP DISCRIMINATION

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 563-571
Author(s):  
S. Mustak ◽  
G. Uday ◽  
B. Ramesh ◽  
B. Praveen
Keyword(s):  
Land Use ◽  
Deciduous Forest ◽  
Accuracy Assessment ◽  
Parameter Tuning ◽  
Northern Region ◽  
Evergreen Forest ◽  
Support Vector ◽  
Cropping Pattern ◽  
Land Use Classification ◽  
Crop Discrimination

<p><strong>Abstract.</strong> Crop discrimination and acreage play a vital role in interpreting the cropping pattern, statistics of the produce and market value of each product. Sultan Battery is an area where a large amount of irrigated and rainfed paddy crops are grown along with Rubber, Arecanut and Coconut. In addition, the northern region of Sultan Battery is covered with evergreen and deciduous forest. In this study, the main objective is to evaluate the performance of optical and Synthetic Aperture Radar (SAR)-optical hybrid fusion imageries for crop discrimination in Sultan Bathery Taluk of Wayanad district in Kerala. Seven land use classes such as paddy, rubber, coconut, deciduous forest, evergreen forest, water bodies and others land use (e.g., built-up, barren etc.) were selected based on literature review and local land use classification policy. Both Sentinel-2A (optical) and sentinel-1A (SAR) satellite imageries of 2017 for Kharif season were used for classification using three machine learning classifiers such as Support Vector Machine (SVM), Random Forest (RF) and Classification and Regression Trees (CART). Further, the performance of these techniques was also compared in order to select the best classifier. In addition, spectral indices and textural matrices (NDVI, GLCM) were extracted from the image and best features were selected using the sequential feature selection approach. Thus, 10-fold cross-validation was employed for parameter tuning of such classifiers to select best hyperparameters to improve the classification accuracy. Finally, best features, best hyperparameters were used for final classification and accuracy assessment. The results show that SVM outperforms the RF and CART and similarly, Optical+SAR datasets outperforms the optical and SAR satellite imageries. This study is very supportive for the earth observation scientists to support promising guideline to the agricultural scientist, policy-makers and local government for sustainable agriculture practice.</p>

scholarly journals Impacts of Urban Sprawl on Livability in Kaduna Metropolis, Nigeria

2020 ◽  
pp. 334-343
Author(s):  
Nuhu H. Tini ◽  
Bartholomew Joshua Light
Keyword(s):  
Land Use ◽  
Urban Sprawl ◽  
Urban Growth ◽  
Land Use Planning ◽  
Urban Poor ◽  
Developed Countries ◽  
Economic Effects ◽  
Public Facility ◽  
Residential Areas ◽  
The City

Urban sprawl is a global phenomenon in the contemporary era. It is mostly taking place in the less developed countries due to natural increase and consistent movement of people into the mega cities and large urban centers. The phenomenon has globally gained attention from diverse researchers in the field of urban geography, environmental studies, city and region planning in view of its significant influence on the urban environment. However, the effect of sprawl on urban livability and economy in Nigerian cities is scarcely investigated especially in Northern Nigeria. This research explores the social and economic effects of urban sprawl in Kaduna metropolis. Remote Sensing and Geographic Information System (GIS) Technologies were applied for the analysis. The study found that Kaduna metropolis has experienced a progressive increase in the built-up area; in 2006 it had an aerial coverage of 13,980 hectares, a rise of 107.91% from 2001 aerial coverage of 6724 hectares. In 2012, the city had an aerial coverage of 15,808 hectares, an increase of 13.08% from 2006. Conversely, there has been a remarkable decrease in percentage of vegetation (1,458 hectares) and agricultural (11,739 hectares) land areas. In turn, such changes has adversely affected urban facilities or utilities such as pipe-borne water, electricity, health facilities, schools, security, transportation, wastewater infrastructures and fire safety services, which has become overstressed. Economic crisis has manifested in the rise of unemployment and escalating number of urban poor. Residential land use has encroached into open spaces while commercial activities overrun residential areas. Increase in distance and journey time make travel cost unbearable to the common man. These and social fragmentation retard livability in the city. Thus calls for a balance sustainable development in Kaduna metropolis and effective management of urban growth by the Kaduna Capital Development Board Authority. In due course, smart growth policy, growth management, urban containment, effective land use planning and public facility adequacy have been recommended to foster viable urban growth in Kaduna city and elsewhere.

Semi-Supervised Land-Use Classification Using Weakly Labeled Remote Sensing Data

2021 ◽  
Author(s):  
Rui Wang ◽  
Man-On Pun ◽  
Huiliang Yu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Land Use Classification ◽  
Sensing Data

scholarly journals Social Sensing for Urban Land Use Identification

2020 ◽  
Vol 9 (9) ◽  
pp. 550
Author(s):  
Adindha Anugraha ◽  
Hone-Jay Chu ◽  
Muhammad Ali
Keyword(s):  
Land Use ◽  
Accuracy Assessment ◽  
Urban Land ◽  
Demographic Characteristics ◽  
Urban Land Use ◽  
Land Uses ◽  
Land Use Classification ◽  
Residential Areas ◽  
Social Sensing ◽  
Land Use Maps

The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses.

scholarly journals Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images

2019 ◽  
Vol 21 (7) ◽  
pp. 1825-1838 ◽  
Author(s):  
Yi Zhu ◽  
Xueqing Deng ◽  
Shawn Newsam
Keyword(s):  
Land Use ◽  
Ground Level ◽  
Land Use Classification ◽  
Fine Grained ◽  
The City

scholarly journals Perkembangan Kawasan Permukiman Akibatnya Terhadap Ruang Terbuka di Kota Semarang

2015 ◽  
Vol 11 (4) ◽  
pp. 430
Author(s):  
Muhammad Ridha Azzaki ◽  
Sugiono Soetomo
Keyword(s):  
Land Use ◽  
Population Growth ◽  
Open Space ◽  
Mutual Influence ◽  
Rapid Development ◽  
Capital City ◽  
High Rate ◽  
Residential Areas ◽  
Projected Change ◽  
The City

Semarang is the capital city of Central Java Province, as a metropolitan city, Semarang has the capablity to support the rapid development of the city , one of the evident is the highly of activity on physical infrastucture, one of them is the construction of residential areas along the high rate of population growth. Settlement area development activities emerge the negatively impact to reduce the existence of open space area. This study uses a quantitative method through positivistic approach. Research data presented by the form of figures and the analysis using the statistics. This study was first carried out in 2006 and 2011 to analyze the spatial through digitized the image map of Semarang, and the results of the digitization of spatial land area of open space and a residential area, which is used to formulate some stage subsequent analysis: 1) Identification and analysis of the influence of the development of residential areas against the open space in the city, 2) Analysis of the acceleration of the projected change of land per year in Semarang in 2006-2020, 3) Analysis of the application of open space 30% (sample in District Tembalang). The result of this analysis showed the relationship between the relevant mutual influence. The rate of population growth and development of residential areas with a relationship of mutual influence supply and demand. Then, as the development of residential areas causes the reduction of open space. In additon, the background of this problem is how to formulate the recommendations to control the land use plan , in order to create an ideal city land use in the future.

scholarly journals Impacts of Anthropogenic Factors on Urban Air Quality in Lagos Metropolis

2019 ◽  
Vol 11 (2) ◽  
pp. 35
Author(s):  
Peter Nkashi AGAN
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Raw Materials ◽  
Anthropogenic Activities ◽  
Anthropogenic Factors ◽  
Land Uses ◽  
Residential Areas ◽  
Industrial Land ◽  
The City

Land use is the utilization and reordering of land cover for human comfort. This process disrupts the pristine state of the environment reducing the quality of environmental receptors like water, air, vegetation etc. Air pollution is introduced into the environment as a result of anthropogenic activities from commercial, industrial and residential areas. These activities are burning of fossil fuels for power generation, transport of goods and services, valorization of raw materials into finished products, bush burning, use of gas cookers, generators and electric stove etc. The introduction of pollutants into the planetary layer of the atmosphere has impacted negatively on the quality of the environment posing threat to humans and the survival of the ecosystem. In Lagos metropolis, commercial activities and high population densities have caused elevated levels of pollution in the city. This study aimed to investigate the spatial distribution of pollutant in Lagos metropolis with a view to revealing the marked spatial/temporal difference in pollutants levels over residential, commercial and industrial land uses. Commercial and industrial land uses revealed higher levels of pollutants than the residential areas. Pearson product moment correlation coefficients revealed strong positive relationship between land use and air quality in the city.

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