フリーオープンソースソフトウェアを用いた分散型の空間解析およびリアルタイムセンサーオブザベーションのためのウェブサービスの開発

博士(創造都市)大阪市立大

GIS Oriented Service Optimization Tool For Fecal Sludge Collection

In developing countries most of the urban dwellers don’t have access to sewer system. People are mostly using “onsite” systems such as septic tanks or pit latrines that need to be emptied periodically, as the densely built urban environment won’t allow new pits to be dug every time they fill up. In the conventional fecal sludge collection systems, authorities are collecting the sludge from house to house and dump on the plant. Fecal sludge collection system is different from traditional vehicle routing and even from solid waste collection system in terms of dynamic collection points, urgency of getting the service and diversity of demand. Due to those vibrant factors authorities are facing proper networking and management problems. This research describes algorithms that can accommodate constraints and prioritized customers who need immediate service. The GPS log data of the fecal sludge collection truck that maintained by Nonthaburi Municipality, Thailand has been considered as the base data during the development of this application. Spatial analysis has been done using Geographic Information Systems (GIS).Tabu Search has been implemented in order to optimize. Basically two algorithms were produced for assisting fecal sludge collection systems. First algorithm was able to produce multiple trip for each vehicle if required considering all the customers having equal priority, time window. The second one was able to perform optimization that can accommodate priority along with the first one. Input for the algorithms were very simple; distance matrix having distance between each customers and plant, customer order with latitude, longitude, order unit, time window, priority and vehicles with capacity. Algorithms were able to produce better result than the actual operation or even from shortest path algorithm in term of distance. After optimization, efficiency of the algorithms were tested with the actual travelling distance. Travelling distance were reduced to half compare to actual cost and it ensured maximum utilization of vehicle capacity by allocating maximum number of customers in each route

Identification of roofing materials with Discriminant Function Analysis and Random Forest classifiers on pan-sharpened WorldView-2 imagery – a comparison

Identification of roofing material is an important issue in the urban environment due to hazardous and risky materials. We conducted an analysis with Discriminant Function Analysis (DFA) and Random Forest (RF) on WorldView-2 imagery. We applied a three- and a six-class approach (red tile, brown tile and asbestos; then dividing the data into shadowed and sunny roof parts). Furthermore, we applied pan-sharpening to the image. Our aim was to reveal the efficiency of the classifiers with a different number of classes and the efficiency of pan-sharpening. We found that all classifiers were efficient in roofing material identification with the classes involved, and the overall accuracy was above 85 per cent. The best results were gained by RF, both with three and with six classes; however, quadratic DFA was also successful in the classification of three classes. Usually, linear DFA performed the worst, but only relatively so, given that the result was 85 per cent. Asbestos was identified successfully with all classifiers. The results can be used by local authorities for roof mapping to build registers of buildings at risk

Spatial analysis of changes and anomalies of intense rainfalls in Hungary

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The making of a joint e-learning platform for remote sensing education : experiences and lessons learned

E-learning is widely used in academic education, and currently, the COVID-19 pandemic is increasing the demand for e-learning resources. This report describes the results achieved and the experiences gained in the Erasmus+ CBHE (Capacity Building in Higher Education) project “Innovation on Remote Sensing Education and Learning (IRSEL)". European and Asian universities created an innovative open source e-learning platform in the field of remote sensing. Twenty modules tailored to remote sensing study programs at the four Asian partner universities were developed. Principles of remote sensing as well as specific thematic applications are part of the modules, and a knowledge pool of e-learning teaching and learning materials was created. The focus was given to case studies covering a broad range of applications. Piloting with students gave evidence about the usefulness and quality of the developed modules. In particular, teachers and students who tested the modules appreciated the balance of theory and practice. Currently, the modules are being integrated into the curricula of the participating Asian universities. The content will be available to a broader public

Identifying Potential Area and Financial Prospects of Rooftop Solar Photovoltaics (PV)

In an urban area, the roof is the only available surface that can be utilized for installing solar photovoltaics (PV), and the active surface area depends on the type of roof. Shadows on a solar panel can be caused by nearby tall buildings, construction materials such as water tanks, or the roof configuration itself. The azimuth angle of the sun varies, based on the season and the time of day. Therefore, the simulation of shadow for one or two days or using the rule of thumb may not be sufficient to evaluate shadow effects on solar panels throughout the year. In this paper, a methodology for estimating the solar potential of solar PV on rooftops is presented, which is particularly applicable to urban areas. The objective of this method is to assess how roof type and shadow play a role in potentiality and financial benefit. The method starts with roof type extraction from high-resolution satellite imagery, using Object Base Image Analysis (OBIA), the generation of a 3D structure from height data and roof type, the simulation of shadow throughout the year, and the identification of potential and financial prospects. Based on the results obtained, the system seems to be adequate for calculating the financial benefits of solar PV to a very fine scale. The payback period varied from 7–13 years depending on the roof type, direction, and shadow impact. Based on the potentiality, a homeowner can make a profit of up to 200%. This method could help homeowners to identify potential roof area and economic interest