No description available.

No description available.

No description available.

Urban land cover classification has always been crucial due to its ability to link many elements of human and physical environments. Timely, accurate, and detailed knowledge of the urban land cover information derived from remote sensing data is increasingly required among a wide variety of communities. This surge of interest has been predominately driven by the recent innovations in data, technologies, and theories in urban remote sensing. The development of light detection and ranging (LiDAR) systems, especially incorporated with high-resolution camera component, has shown great potential for urban classification. However, the performance of traditional and widely used classification methods is limited in this context, due to image interpretation complexity. On the other hand, random forests (RF), a newly developed machine learning algorithm, is receiving considerable attention in the field of image classification and pattern recognition. Several studies have shown the advantages of RF in land cover classification. However, few have focused on urban areas by fusion of LiDAR data and aerial images. The performance of the RF based feature selection and classification methods for urban areas was explored and compared to other popular feature selection approach and classifiers. Evaluation was based on several criteria: classification accuracy, impact of different training sample size, and computational speed. LiDAR data and aerial imagery with 0.5-m resolution were used to classify four land categories in the study area located in the City of Niagara Falls (ON, Canada). The results clearly demonstrate that the use of RF improved the classification performance in terms of accuracy and speed. Support vector machines (SVM) based and RF based classifiers showed similar accuracies. However, RF based classifiers were much quicker than SVM based methods. Based on the results from this work, it can be concluded that the RF based method holds great potential for recent and future urban land cover classification problem with LiDAR data and aerial images.

This research aims to examine the effects of the urban land acquisition and allocation programme by the government in Iran on the rate of increase of the prices of urban land and affordablity of housing prices for different income groups in the urban areas of the country during the period 1979 to 1988 which was on the basis of the enactment of three Urban Land Laws after the 1979 revolution. The implementation of these laws limited private ownership of vacant urban land in the country to about 1,000 to 1,500 square metres, depending on the size of the city, with the excess land being acquired by the government. In this respect the Urban Land Organisation under the Ministry of Housing and Urban Development was able to acquire 36,000 hectares through confiscation from the excess of private ownership, 8,258 hectares of private land in return for payment of compensation, and a further 41,272 hectares of land which already belonged to the various public organisations was transferred to its authority. Thereby a total of 85,557 hectares was assembled under the authority of the urban land organisation (ULO). As part of the land policy, the ULO then proceeded to allocate some 10,790 hectares or 12.6 percent of the total assembled land to eligible applicants including private households, housing cooperatives and public and private housing developers for housing construction. This was in addition to the 3,313 hectares or 3.9 percent of the total assembled land which was allocated for the purposes urban services and commercial buildings. In any case, while only 12.6 percent of the assembled land was allocated by the ULO it comprised about 32 percent of the number and 34 percent of the area of land plots for new starts of housing construction during the period 1979 to 1988. The hypothesis of the thesis with regard to the effect of government acquisition and allocation of urban land on the situation of urban land prices and housing in Iran is that between 1979 to 1988 this programme and the resultant activities has on the one hand led to a reduction of the rate of increase of the price of urban land in the market and, on the other, has contributed to the provision of affordable housing units for low and middle income households. The thesis has tested the first part of its hypothesis by collecting and analyzing the trend of development of urban land prices for a 15 year period between 1974 to 1988. The second part of the hypothesis has been tested by calculating the price of housing on ULO allocated and privately owned land and then comparing them with the effective demand of different income groups in the urban areas of the country. The evidence of the analysis for the first part of the hypothesis shows that during the period 1979 to 1988 the average price of one square metre of privately owned land was about 13 times higher than ULO allocated land. More importantly, however, is the trend of development of the price of privately owned land between 1974 to 1988 which shows that after the 1979 revolution average prices of privately owned land always kept below the 1976 figure prior to the revolution and from 1985 on wards the actual increase at constant prices was actually negative. Moreover, with the base year of 1974, the index of the average price of privately owned land for the period 1979 to 1988 was between 58.7 and 207.5 which was much lower than the consumer price index which was between 196.3 and 974.1 for the same period. The index of the average price of urban land for 1975 and 1976 prior to the 1979 revolution, however, is 125.3 and 170.5 which is higher than the consumer price index for the same period which was 109.9 and 128.1. consequently, it can be stated that the evidence from the analysis for the first part of the hypothesis supports its proposition in that the enactment and implementation of the aforementioned urban land policy after the 1979 revolution has led to a reduction of the rate of increase of urban land prices in the market between the period 1979 to 1988. The result of the analysis for the second part of the hypothesis on the other hand shows that on the basis of the recommended floor area of 75 square metre for housing units built on ULO allocated land, which was recommended by the Ministry of Housing and Urban Development, such units were in the main affordable to all income categories including all those in the low income groups. The units built on privately owned land were in the main only affordable to income groups 8-10 which comprised the high income groups and in 1987 and 1988 even income groups 8-9 of the high income groups were excluded from these units. More importantly, however, the analysis shows that even with bigger floor areas including the actual average floor area of urban housing units for the period 1979 to 1988, which ranged between 132 to 162 square metres during the stated period, the units built on ULO allocated land were still much more affordable to the low and middle income groups than the units built on privately owned land which would still be in the main unaf fordable to the low income groups. This difference in affordablity also applies to small units 50-75 square metres built on the two different categories of land. The result of the analysis for the second part of the hypothesis, therefore, also supports its proposition in that the enactment and implementation of the aforementioned urban land policy after the 1979 revolution has contributed to the provision of affordable housing units for low and middle income households in the period 1979 to 1988.

Urban land cover classification has always been crucial due to its ability to link many elements of human and physical environments. Timely, accurate, and detailed knowledge of the urban land cover information derived from remote sensing data is increasingly required among a wide variety of communities. This surge of interest has been predominately driven by the recent innovations in data, technologies, and theories in urban remote sensing. The development of light detection and ranging (LiDAR) systems, especially incorporated with high-resolution camera component, has shown great potential for urban classification. However, the performance of traditional and widely used classification methods is limited in this context, due to image interpretation complexity. On the other hand, random forests (RF), a newly developed machine learning algorithm, is receiving considerable attention in the field of image classification and pattern recognition. Several studies have shown the advantages of RF in land cover classification. However, few have focused on urban areas by fusion of LiDAR data and aerial images. The performance of the RF based feature selection and classification methods for urban areas was explored and compared to other popular feature selection approach and classifiers. Evaluation was based on several criteria: classification accuracy, impact of different training sample size, and computational speed. LiDAR data and aerial imagery with 0.5-m resolution were used to classify four land categories in the study area located in the City of Niagara Falls (ON, Canada). The results clearly demonstrate that the use of RF improved the classification performance in terms of accuracy and speed. Support vector machines (SVM) based and RF based classifiers showed similar accuracies. However, RF based classifiers were much quicker than SVM based methods. Based on the results from this work, it can be concluded that the RF based method holds great potential for recent and future urban land cover classification problem with LiDAR data and aerial images.

Thesis (M.U.R.P.) -- University of Adelaide, Dept. of Architecture, 1980.

Thesis (M.Env.St. 1979) from the Centre for Environmental Studies, University of Adelaide.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1973. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliography.

This dissertation seeks to explore the legacy of coloniality inherent in the built environment of South African cities today, especially the City of Tshwane (Pretoria), and propose strategies to rewrite a more inclusive and transformative architectural legacy. As the historical (and current) seat of the South African government, Pretoria has seen much of the making of South Africa’s colonial (as well as pre and post-colonial) history. The remains of the architectural heritage speak of European classical ideals, battles for imperial power and colonial ways of life, and many of these heritage buildings could be seen to be struggling to represent a diverse and transformed nation. As the call has gone out to question the future of statues and monuments of problematic past leaders, it brings to light the question of our built history, heritage and the legacy it leaves. Colonial architecture heritage faces different avenues of conservation, such as forms of reuse or adaptive reuse; however, many are facing abandonment due to its inability to transform or adapt to the changing needs of society. Such is Berea Park Sports Club's case, abandoned and then vandalised, its ruins speaking of possibly becoming forgotten altogether. By investigating the reuse of the building and sports grounds through the themes of urban land reform, architectural hybridity and relevant heritage approaches, this project seeks to reimagine the legacy of the site and address spatial and social justice concerns left in the wake of the colonial city. / Mini Dissertation (MArch (Prof))--University of Pretoria, 2020. / Architecture / MArch (Prof) / Unrestricted