2020 |
Gonzalez, Carlos Javier Delgado Rooftop-Place Suitability Analysis for Urban Air Mobility Hubs: A GIS and Neural Network Approach Masters Thesis Universidade Nova De Lisboa, 2020. Abstract | Links | BibTeX | Tags: Geographic Information Systems (GIS), Mastergeotech, neural networks, urban mobility @mastersthesis{Gonzalez2020, title = {Rooftop-Place Suitability Analysis for Urban Air Mobility Hubs: A GIS and Neural Network Approach}, author = {Carlos Javier Delgado Gonzalez }, editor = {Joel Dinis Baptista Ferreira da Silva and Roberto Henriques and Carlos Granell-Canut }, url = {http://hdl.handle.net/10362/93642}, year = {2020}, date = {2020-02-27}, address = {Lisboa}, school = {Universidade Nova De Lisboa}, abstract = {Nowadays, constant overpopulation and urban expansion in cities worldwide have led to several transport-related challenges. Traffic congestion, long commuting, parking difficulties, automobile dependence, high infrastructure maintenance costs, poor public transportation, and loss of public space are some of the problems that afflict major metropolitan areas. Trying to provide a solution for the future inner-city transportation, several companies have worked in recent years to design aircraft prototypes that base their technology on current UAVs. Therefore, vehicles with electrical Vertical Take-Off and Landing (eVTOL) technology are rapidly emerging so that they can be included in the Urban Air Mobility (UAM) system. For this to become a reality, space agencies, governments and academics are generating concepts and recommendations to be considered a safe means of transportation for citizens. However, one of the most relevant points for this future implementation is the suitable location of the potential UAM hubs within the metropolitan areas. Since although UAM vehicles can take advantage of infrastructure such as roofs of buildings to clear and land, several criteria must be considered to find the ideal location. As a solution, this thesis seeks to carry out an integral rooftop-place suitability analysis by involving both the essential variables of the urban ecosystem and the adequate rooftop surfaces for UAM operability. The study area selected for this research is Manhattan (New York, U.S), which is the most densely populated metropolitan area of one of the megacities in the world. The applied methodology has an unsupervised-data-driving and GIS-based approach, which is covered in three sections. The first part is responsible for analyzing the suitability of place when evaluating spatial patterns given by the application of Self-Organizing Maps on the urban ecosystem variables attached to the city census blocks. The second part is based on the development of an algorithm in Python for both the evaluation of the flatness of the roof surfaces and the definition of the UAM platform type suitable for its settlement. The final stage performs a combined analysis of the suitability indexes generated for the development of UAM hubs. Results reflect that 16% of the roofs in the study area have high integral suitability for the development of UAM hubs, where UAVs platforms and Vertistops (small size platforms) are the types that can be the most settled in Manhattan. The reproducibility self-assessment of this research when considering Nüst et al. [45] criteria (https://osf.io/j97zp/) is: 2, 1, 2, 1, 1 (input data, preprocessing, methods, computational environment, results). GitHub repository code is available in https://github.com/carlosjdelgadonovaims/rooftop-place_suitability_analysis_for_Urban_Air_Mobility_hubs}, keywords = {Geographic Information Systems (GIS), Mastergeotech, neural networks, urban mobility}, pubstate = {published}, tppubtype = {mastersthesis} } Nowadays, constant overpopulation and urban expansion in cities worldwide have led to several transport-related challenges. Traffic congestion, long commuting, parking difficulties, automobile dependence, high infrastructure maintenance costs, poor public transportation, and loss of public space are some of the problems that afflict major metropolitan areas. Trying to provide a solution for the future inner-city transportation, several companies have worked in recent years to design aircraft prototypes that base their technology on current UAVs. Therefore, vehicles with electrical Vertical Take-Off and Landing (eVTOL) technology are rapidly emerging so that they can be included in the Urban Air Mobility (UAM) system. For this to become a reality, space agencies, governments and academics are generating concepts and recommendations to be considered a safe means of transportation for citizens. However, one of the most relevant points for this future implementation is the suitable location of the potential UAM hubs within the metropolitan areas. Since although UAM vehicles can take advantage of infrastructure such as roofs of buildings to clear and land, several criteria must be considered to find the ideal location. As a solution, this thesis seeks to carry out an integral rooftop-place suitability analysis by involving both the essential variables of the urban ecosystem and the adequate rooftop surfaces for UAM operability. The study area selected for this research is Manhattan (New York, U.S), which is the most densely populated metropolitan area of one of the megacities in the world. The applied methodology has an unsupervised-data-driving and GIS-based approach, which is covered in three sections. The first part is responsible for analyzing the suitability of place when evaluating spatial patterns given by the application of Self-Organizing Maps on the urban ecosystem variables attached to the city census blocks. The second part is based on the development of an algorithm in Python for both the evaluation of the flatness of the roof surfaces and the definition of the UAM platform type suitable for its settlement. The final stage performs a combined analysis of the suitability indexes generated for the development of UAM hubs. Results reflect that 16% of the roofs in the study area have high integral suitability for the development of UAM hubs, where UAVs platforms and Vertistops (small size platforms) are the types that can be the most settled in Manhattan. The reproducibility self-assessment of this research when considering Nüst et al. [45] criteria (https://osf.io/j97zp/) is: 2, 1, 2, 1, 1 (input data, preprocessing, methods, computational environment, results). GitHub repository code is available in https://github.com/carlosjdelgadonovaims/rooftop-place_suitability_analysis_for_Urban_Air_Mobility_hubs |
2014 |
Torres-Sospedra, Joaquín; Nebot, Patricio Two-stage procedure based on smoothed ensembles of neural networks applied to weed detection in orange groves Journal Article Biosystems Engineering, 123 , pp. 40–55, 2014, ISSN: 1537-5110, (IF: 1.619, Q1). Abstract | Links | BibTeX | Tags: Agricultural robotics, neural networks @article{TorresSospedra2014, title = {Two-stage procedure based on smoothed ensembles of neural networks applied to weed detection in orange groves}, author = { Joaquín Torres-Sospedra and Patricio Nebot}, url = {http://hdl.handle.net/10234/123543}, doi = {10.1016/j.biosystemseng.2014.05.005}, issn = {1537-5110}, year = {2014}, date = {2014-01-01}, journal = {Biosystems Engineering}, volume = {123}, pages = {40--55}, abstract = {The potential impacts of herbicide utilization compel producers to use new methods of weed control. The problem of how to reduce the amount of herbicide and yet maintain crop production has stimulated many researchers to study selective herbicide application. The key of selective herbicide application is how to discriminate the weed areas efficiently. We introduce a procedure for weed detection in orange groves which consists of two different stages. In the first stage, the main features in an image of the grove are determined (Trees, Trunks, Soil and Sky). In the second, the weeds are detected only in those areas which were determined as Soil in the first stage. Due to the characteristics of weed detection (changing weather and light conditions), we introduce a new training procedure with noisy patterns for ensembles of neural networks. In the experiments, a comparison of the new noisy learning was successfully performed with a set of well-known classification problems from the machine learning repository published by the University of California, Irvine. This first comparison was performed to determine the general behavior and performance of the noisy ensembles. Then, the new noisy ensembles were applied to images from orange groves to determine where weeds are located using the proposed two-stage procedure. Main results of this contribution show that the proposed system is suitable for weed detection in orange, and similar, groves. ?? 2014 IAgrE.}, note = {IF: 1.619, Q1}, keywords = {Agricultural robotics, neural networks}, pubstate = {published}, tppubtype = {article} } The potential impacts of herbicide utilization compel producers to use new methods of weed control. The problem of how to reduce the amount of herbicide and yet maintain crop production has stimulated many researchers to study selective herbicide application. The key of selective herbicide application is how to discriminate the weed areas efficiently. We introduce a procedure for weed detection in orange groves which consists of two different stages. In the first stage, the main features in an image of the grove are determined (Trees, Trunks, Soil and Sky). In the second, the weeds are detected only in those areas which were determined as Soil in the first stage. Due to the characteristics of weed detection (changing weather and light conditions), we introduce a new training procedure with noisy patterns for ensembles of neural networks. In the experiments, a comparison of the new noisy learning was successfully performed with a set of well-known classification problems from the machine learning repository published by the University of California, Irvine. This first comparison was performed to determine the general behavior and performance of the noisy ensembles. Then, the new noisy ensembles were applied to images from orange groves to determine where weeds are located using the proposed two-stage procedure. Main results of this contribution show that the proposed system is suitable for weed detection in orange, and similar, groves. ?? 2014 IAgrE. |
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Thesis & M. Thesis
2020 |
Rooftop-Place Suitability Analysis for Urban Air Mobility Hubs: A GIS and Neural Network Approach Masters Thesis Universidade Nova De Lisboa, 2020. |
2014 |
Two-stage procedure based on smoothed ensembles of neural networks applied to weed detection in orange groves Journal Article Biosystems Engineering, 123 , pp. 40–55, 2014, ISSN: 1537-5110, (IF: 1.619, Q1). |