Pub Date : 2022-03-01DOI: 10.1080/14498596.2022.2037473
Prakash Ps, B. Aithal
ABSTRACT Building footprint datasets are valuable for a variety of uses in urban settings. For a number of urban applications, polygonal building outlines with regularised bounds are required and are extremely challenging to prepare. We propose a deep learning strategy based on convolutional neural networks for retrieving building footprints. The model was trained using images from a variety of places across the metropolis, highlighting differences in land use patterns and the built environment. The evaluation measures indicate how the accuracy characteristics of distinct built-up settings differ. The results of the model are equivalent to cutting-edge building extraction methods.
{"title":"Building footprint extraction from very high-resolution satellite images using deep learning","authors":"Prakash Ps, B. Aithal","doi":"10.1080/14498596.2022.2037473","DOIUrl":"https://doi.org/10.1080/14498596.2022.2037473","url":null,"abstract":"ABSTRACT Building footprint datasets are valuable for a variety of uses in urban settings. For a number of urban applications, polygonal building outlines with regularised bounds are required and are extremely challenging to prepare. We propose a deep learning strategy based on convolutional neural networks for retrieving building footprints. The model was trained using images from a variety of places across the metropolis, highlighting differences in land use patterns and the built environment. The evaluation measures indicate how the accuracy characteristics of distinct built-up settings differ. The results of the model are equivalent to cutting-edge building extraction methods.","PeriodicalId":50045,"journal":{"name":"Journal of Spatial Science","volume":"68 1","pages":"487 - 503"},"PeriodicalIF":1.9,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48382684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-08DOI: 10.1080/14498596.2022.2034130
Huu Loc Ho, Hai Son Vu, D. Tran, Edward Park, An Giang
ABSTRACT This study estimates the surface soil moisture content in a case study situated in the Vietnamese Red River Delta, using the Landsat 8 satellite images. The trapezoidal relationship between land surface temperature and vegetation index was used to obtain soil wetness indexes. A split-window algorithm was developed to overcome the missing of atmospheric data. The method was validated with ground truth across different land covers. The RMSE between the calculated and measured SMC ranges between 0.556 and 0.971 and varies across different types of land covers. The method is important to monitor SMC across large areas with limited surveyed data.
{"title":"Mapping volumetric soil moisture in the Vietnamese Red River Delta using Landsat 8 images","authors":"Huu Loc Ho, Hai Son Vu, D. Tran, Edward Park, An Giang","doi":"10.1080/14498596.2022.2034130","DOIUrl":"https://doi.org/10.1080/14498596.2022.2034130","url":null,"abstract":"ABSTRACT This study estimates the surface soil moisture content in a case study situated in the Vietnamese Red River Delta, using the Landsat 8 satellite images. The trapezoidal relationship between land surface temperature and vegetation index was used to obtain soil wetness indexes. A split-window algorithm was developed to overcome the missing of atmospheric data. The method was validated with ground truth across different land covers. The RMSE between the calculated and measured SMC ranges between 0.556 and 0.971 and varies across different types of land covers. The method is important to monitor SMC across large areas with limited surveyed data.","PeriodicalId":50045,"journal":{"name":"Journal of Spatial Science","volume":"68 1","pages":"469 - 485"},"PeriodicalIF":1.9,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42492496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-26DOI: 10.1080/14498596.2022.2027291
D. Shojaei, Farshad Badiee, H. Olfat, A. Rajabifard, B. Atazadeh
ABSTRACT Land administration systems are being modernised to streamline the cadastral data lodgement. However, in many jurisdictions, cadastral data are still stored as a flat file. This method of data storage has significant limitations in terms of effective access, management, query, and analysis of cadastral data. Therefore, this study elicited the requirements and proposed an approach to automate the cadastral data storage. The proposed approach was successfully implemented within the land registry organisation in Victoria, Australia and the database management system was rigorously tested. The outcomes can potentially contribute to the implementation of a similar data storage infrastructure in other jurisdictions.
{"title":"Requirements of a data storage infrastructure for effective land administration systems: case study of Victoria, Australia","authors":"D. Shojaei, Farshad Badiee, H. Olfat, A. Rajabifard, B. Atazadeh","doi":"10.1080/14498596.2022.2027291","DOIUrl":"https://doi.org/10.1080/14498596.2022.2027291","url":null,"abstract":"ABSTRACT Land administration systems are being modernised to streamline the cadastral data lodgement. However, in many jurisdictions, cadastral data are still stored as a flat file. This method of data storage has significant limitations in terms of effective access, management, query, and analysis of cadastral data. Therefore, this study elicited the requirements and proposed an approach to automate the cadastral data storage. The proposed approach was successfully implemented within the land registry organisation in Victoria, Australia and the database management system was rigorously tested. The outcomes can potentially contribute to the implementation of a similar data storage infrastructure in other jurisdictions.","PeriodicalId":50045,"journal":{"name":"Journal of Spatial Science","volume":"68 1","pages":"431 - 449"},"PeriodicalIF":1.9,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48039227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-26DOI: 10.1080/14498596.2022.2028270
Jiakuan Han, Xiaochen Kang, Yi Yang, Yinyin Zhang
ABSTRACT In spatiotemporal applications, geographically weighted principal component analysis (GWPCA) is commonly adopted to describe spatial heterogeneity. However, time effects are ignored in GWPCA. In this study, the temporal effect was incorporated into GWPCA . Thus, an extended model, geographically and temporally weighted principal component analysis (GTWPCA), was developed to simultaneously explore spatial and temporal non-stationarity. The GTWPCA was implemented using a case study of air pollution in China. The results mainly show that GTWPC1 (the local component one in GTWPCA) corresponds to a ‘winning group’ with constantly varying ‘winning’ variables adapted to the spatiotemporal non-stationary characteristics of air pollution in China.
{"title":"Geographically and temporally weighted principal component analysis: a new approach for exploring air pollution non-stationarity in China, 2015–2019","authors":"Jiakuan Han, Xiaochen Kang, Yi Yang, Yinyin Zhang","doi":"10.1080/14498596.2022.2028270","DOIUrl":"https://doi.org/10.1080/14498596.2022.2028270","url":null,"abstract":"ABSTRACT In spatiotemporal applications, geographically weighted principal component analysis (GWPCA) is commonly adopted to describe spatial heterogeneity. However, time effects are ignored in GWPCA. In this study, the temporal effect was incorporated into GWPCA . Thus, an extended model, geographically and temporally weighted principal component analysis (GTWPCA), was developed to simultaneously explore spatial and temporal non-stationarity. The GTWPCA was implemented using a case study of air pollution in China. The results mainly show that GTWPC1 (the local component one in GTWPCA) corresponds to a ‘winning group’ with constantly varying ‘winning’ variables adapted to the spatiotemporal non-stationary characteristics of air pollution in China.","PeriodicalId":50045,"journal":{"name":"Journal of Spatial Science","volume":"68 1","pages":"451 - 468"},"PeriodicalIF":1.9,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47206279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-12DOI: 10.1080/14498596.2021.2008539
E. Falayi, J. Adepitan, A. Adewole, T. Roy-Layinde
ABSTRACT The chaotic behaviour of monthly rainfall data of Benin, Cote d’Ivoire, Cameroon, Ghana, Niger, Nigeria, Senegal and Togo between January 1901 and December 2015 were investigated using wavelet transformation analysis and time series techniques. Wavelet power spectrum was used to split the time series into different scales. Power concentrations between 8 and 16 months were observed for the selected locations. The embedding dimension, delay and largest Lyapunov exponent (LE) were calculated. We observed positive LE ranging from 0.13 to 0.36, indicating the rainfall was chaotic. Ghana had the highest values of LE, while the lowest LE was observed at Niger..
{"title":"Analysis of rainfall data of some West African countries using wavelet transform and nonlinear time series techniques","authors":"E. Falayi, J. Adepitan, A. Adewole, T. Roy-Layinde","doi":"10.1080/14498596.2021.2008539","DOIUrl":"https://doi.org/10.1080/14498596.2021.2008539","url":null,"abstract":"ABSTRACT The chaotic behaviour of monthly rainfall data of Benin, Cote d’Ivoire, Cameroon, Ghana, Niger, Nigeria, Senegal and Togo between January 1901 and December 2015 were investigated using wavelet transformation analysis and time series techniques. Wavelet power spectrum was used to split the time series into different scales. Power concentrations between 8 and 16 months were observed for the selected locations. The embedding dimension, delay and largest Lyapunov exponent (LE) were calculated. We observed positive LE ranging from 0.13 to 0.36, indicating the rainfall was chaotic. Ghana had the highest values of LE, while the lowest LE was observed at Niger..","PeriodicalId":50045,"journal":{"name":"Journal of Spatial Science","volume":"68 1","pages":"385 - 396"},"PeriodicalIF":1.9,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41987417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-02DOI: 10.1080/14498596.2021.2019971
G. Wright
This issue of the Journal of Spatial Science includes papers investigating techniques that have direct application such as road median extraction, automatic rooftop extraction, transformation of historic maps into interactive web maps, assessing vacant land as a measure of urban decline, modelling forest characteristics and an interface to visualize large space-time datasets with applications in smart cities. Additionally, research is presented on more fundamental issues such as point generalisation, and receiver code biases and positioning integrity in GNSS. Kumar, Lewis, Cahalane and Peters present the GLIMPSE system to provide a framework for storage, management, accessibility and integration of 3D LiDAR data acquired from multiple platforms. The authors detail a point cloud retrieval approach that provides spatially optimised access to point cloud data for a particular geographic area based on user specifications. With the integrated use of a geospatial database, the GLIMPSE system and point cloud retrieval approach improved the efficiency of road median extraction. Automatic building rooftop extraction is of great importance to many applications including building reconstruction, solar energy supply, and disaster management. The study by B. Wu, S. Wu, Li, J. Wu, Huang, Chen and Yu proposes a building rooftop extraction method using DSM data generated from aerial stereo images and vegetation cover vector data. The proposed method was applied to the centre of Shanghai, China, a typical high density urban area, and experimental results show the method can successfully extract building rooftops. An improved method for generalisation of point features with consideration of reinforcing relationships by Zhang, Yu and Chen aims to preserve global patterns of point cluster during map scaling, an important technique for clear presentation of points in multi-scale maps. Existing methods tend to include single point features ignoring spatial interactions between different types of points, such as different types of facilities that are usually colocated together to reinforce their functions in business. In this respect, generalization of point features should consider not only their own importance but also the reinforcing effects from other nearby features. In the article by Horbiński and Lorek a method for creating an interactive web map of the preindustrial state on the basis of analogue nineteenth-century maps of southern Poland is presented. The main objective is to present a universal scheme that allows transformation of old topographic maps into interactive web maps. The Leaflet library was used as a working environment for programming. Receiver code biases (RCBs) have long been identified as time-constant. However, RCBs can exhibit remarkable intraday variability, that affects GNSS-based ionospheric retrieval and timing applications with different combinations. Ke, Sheng and Wang propose a modified geometry-free GNSS model to extract receiver code
{"title":"Editorial","authors":"G. Wright","doi":"10.1080/14498596.2021.2019971","DOIUrl":"https://doi.org/10.1080/14498596.2021.2019971","url":null,"abstract":"This issue of the Journal of Spatial Science includes papers investigating techniques that have direct application such as road median extraction, automatic rooftop extraction, transformation of historic maps into interactive web maps, assessing vacant land as a measure of urban decline, modelling forest characteristics and an interface to visualize large space-time datasets with applications in smart cities. Additionally, research is presented on more fundamental issues such as point generalisation, and receiver code biases and positioning integrity in GNSS. Kumar, Lewis, Cahalane and Peters present the GLIMPSE system to provide a framework for storage, management, accessibility and integration of 3D LiDAR data acquired from multiple platforms. The authors detail a point cloud retrieval approach that provides spatially optimised access to point cloud data for a particular geographic area based on user specifications. With the integrated use of a geospatial database, the GLIMPSE system and point cloud retrieval approach improved the efficiency of road median extraction. Automatic building rooftop extraction is of great importance to many applications including building reconstruction, solar energy supply, and disaster management. The study by B. Wu, S. Wu, Li, J. Wu, Huang, Chen and Yu proposes a building rooftop extraction method using DSM data generated from aerial stereo images and vegetation cover vector data. The proposed method was applied to the centre of Shanghai, China, a typical high density urban area, and experimental results show the method can successfully extract building rooftops. An improved method for generalisation of point features with consideration of reinforcing relationships by Zhang, Yu and Chen aims to preserve global patterns of point cluster during map scaling, an important technique for clear presentation of points in multi-scale maps. Existing methods tend to include single point features ignoring spatial interactions between different types of points, such as different types of facilities that are usually colocated together to reinforce their functions in business. In this respect, generalization of point features should consider not only their own importance but also the reinforcing effects from other nearby features. In the article by Horbiński and Lorek a method for creating an interactive web map of the preindustrial state on the basis of analogue nineteenth-century maps of southern Poland is presented. The main objective is to present a universal scheme that allows transformation of old topographic maps into interactive web maps. The Leaflet library was used as a working environment for programming. Receiver code biases (RCBs) have long been identified as time-constant. However, RCBs can exhibit remarkable intraday variability, that affects GNSS-based ionospheric retrieval and timing applications with different combinations. Ke, Sheng and Wang propose a modified geometry-free GNSS model to extract receiver code","PeriodicalId":50045,"journal":{"name":"Journal of Spatial Science","volume":"67 1","pages":"1 - 2"},"PeriodicalIF":1.9,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42603698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-27DOI: 10.1080/14498596.2021.2000898
S. Madonsela, M. Cho, L. Naidoo, R. Main, N. Majozi
ABSTRACT This study investigated the utility of Sentinel-2 spectral data for estimating leaf area index (LAI), leaf and canopy chlorophyll content of maize at different growth stages. Vegetation indices based on the visible-near infrared and red-edge regions of the spectrum were computed from Sentinel-2 imagery acquired within one or two days of field data collection. Results showed that green chlorophyll index (CIgreen) and red-edge chlorophyll index (CIred-edge), using the red-edge variant centred at 705 nm, consistently showed higher relationship to maize LAI with r 2 of 0.65 and 0.63 during the early stages of growth, respectively, and an r 2 of 0.79 and 0.81 during tassel stage, respectively. Regarding canopy chlorophyll content the results indicated the spectral advantage of the Sentinel-2 sensor with the presence of two red-edge bands for continuous monitoring of maize chlorophyll content. Overall, the results indicated that maize biophysical variables can be monitored at satellite level using Sentinel-2 data.
{"title":"Exploring the utility of Sentinel-2 for estimating maize chlorophyll content and leaf area index across different growth stages","authors":"S. Madonsela, M. Cho, L. Naidoo, R. Main, N. Majozi","doi":"10.1080/14498596.2021.2000898","DOIUrl":"https://doi.org/10.1080/14498596.2021.2000898","url":null,"abstract":"ABSTRACT This study investigated the utility of Sentinel-2 spectral data for estimating leaf area index (LAI), leaf and canopy chlorophyll content of maize at different growth stages. Vegetation indices based on the visible-near infrared and red-edge regions of the spectrum were computed from Sentinel-2 imagery acquired within one or two days of field data collection. Results showed that green chlorophyll index (CIgreen) and red-edge chlorophyll index (CIred-edge), using the red-edge variant centred at 705 nm, consistently showed higher relationship to maize LAI with r 2 of 0.65 and 0.63 during the early stages of growth, respectively, and an r 2 of 0.79 and 0.81 during tassel stage, respectively. Regarding canopy chlorophyll content the results indicated the spectral advantage of the Sentinel-2 sensor with the presence of two red-edge bands for continuous monitoring of maize chlorophyll content. Overall, the results indicated that maize biophysical variables can be monitored at satellite level using Sentinel-2 data.","PeriodicalId":50045,"journal":{"name":"Journal of Spatial Science","volume":"68 1","pages":"339 - 351"},"PeriodicalIF":1.9,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41359832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-27DOI: 10.1080/14498596.2021.2013966
A. Paul, Sayari Bhattacharyya, D. Chakraborty
ABSTRACT A specific amount of shade tree density is essential for quality tea production. Here, deep convolutional neural network (DCNN) based architectures are used for detecting and measuring the canopy area of shade trees in high-resolution remote sensing (RS) images covering tea gardens with precision, recall, F1 score and Intersection-over-Union value of 98.9%, 85.1%, 91.36 and 0.96 respectively. Subsequently, shade tree density is estimated with average error of 0.03. In the present paper a fully automated DCNN-based process is established which not only detects shade trees in RS imagery, but also estimates their canopy density for assisting tea garden management.
{"title":"Estimation of Shade Tree Density in Tea Garden using Remote Sensing Images and Deep Convolutional Neural Network","authors":"A. Paul, Sayari Bhattacharyya, D. Chakraborty","doi":"10.1080/14498596.2021.2013966","DOIUrl":"https://doi.org/10.1080/14498596.2021.2013966","url":null,"abstract":"ABSTRACT A specific amount of shade tree density is essential for quality tea production. Here, deep convolutional neural network (DCNN) based architectures are used for detecting and measuring the canopy area of shade trees in high-resolution remote sensing (RS) images covering tea gardens with precision, recall, F1 score and Intersection-over-Union value of 98.9%, 85.1%, 91.36 and 0.96 respectively. Subsequently, shade tree density is estimated with average error of 0.03. In the present paper a fully automated DCNN-based process is established which not only detects shade trees in RS imagery, but also estimates their canopy density for assisting tea garden management.","PeriodicalId":50045,"journal":{"name":"Journal of Spatial Science","volume":"68 1","pages":"415 - 429"},"PeriodicalIF":1.9,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46504849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-27DOI: 10.1080/14498596.2021.2013329
A. Sen, B. Suleymanoglu, M. Soycan
ABSTRACT In this study, we compared the LiDAR filtering performances of unsupervised machine learning methods, such as linkage, K-means, and self-organizing maps, for urban areas to provide a practical guide to researchers. The input parameters (x-y-z and intensity) were normalized and weighted using a chi-squared independence test to improve the classification accuracy. The best successful results were obtained using the weighted linkage method in terms of the total error of 13.53%, 3.96%, and 1.07% for the three samples, respectively. In comparison with other approaches, methods weighted by chi-squared have significant potential for classification and filtering and outperform many popular approaches.
{"title":"Performance of unsupervised machine learning methods using chi-squared weights for LiDAR point cloud filtering in urban areas","authors":"A. Sen, B. Suleymanoglu, M. Soycan","doi":"10.1080/14498596.2021.2013329","DOIUrl":"https://doi.org/10.1080/14498596.2021.2013329","url":null,"abstract":"ABSTRACT In this study, we compared the LiDAR filtering performances of unsupervised machine learning methods, such as linkage, K-means, and self-organizing maps, for urban areas to provide a practical guide to researchers. The input parameters (x-y-z and intensity) were normalized and weighted using a chi-squared independence test to improve the classification accuracy. The best successful results were obtained using the weighted linkage method in terms of the total error of 13.53%, 3.96%, and 1.07% for the three samples, respectively. In comparison with other approaches, methods weighted by chi-squared have significant potential for classification and filtering and outperform many popular approaches.","PeriodicalId":50045,"journal":{"name":"Journal of Spatial Science","volume":"68 1","pages":"397 - 414"},"PeriodicalIF":1.9,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45403472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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