4^th International Conference on GIS and Remote Sensing September 27-28, 2018 Berlin, Germany

Petr Vanicek got his Engineering Degree in Surveying from Czech Technical University in Prague in 1959, his PhD in Mathematical Physics from Czechoslovak Academy of Sciences in 1968, his DrSc in Mathematical and Physical Sciences from Czech Academy of Sciences in 1993 and his Professor Emeritus from Univerisity of New Brunswick, Canada in 2002. He lectured for 29 years at the University of New Brunswick and retired in 1999. He has published well over 500 books, papers and reports in reputed journals and has been serving as an Editor-in-Chief on editorial boards of reputable journals.

The presentation is going to introduce first the idea of a height system as a conglomerate of the reference surface, also known as a datum and properly defined heights. Then the kinds of height systems used in practice are going to be discussed. These are three: the geodetic system consisting of the reference ellipsoid as a datum and geodetic heights (also incorrectly often called “ellipsoidal heights”); the classical system that consists of the geoid as a datum and orthometric heights (above the sea level) and; the Molodensky system composed of the quasi-geoid as a datum and normal heights (referred to the quasi-geoid). The geodetic system is used when heights are being determined by satellites; classical system has been used throughout the world since individual nations introduced their national height systems and Molodensky’s system is now being used in Russia and several European countries. Further, the question of realization of a height system in practice will be discussed. This discussion is going to deal with mean sea level (MSL), sea surface topography (SST), the way observed heights and height differences are transformed into proper heights and height differences and the role of gravity in these transformations, the role of potential numbers and dynamic heights. Next, the properties of height systems that make them appropriate for practice will be shown: first and utmost, the system must be congruent, i.e., the datum and heights taken together must give us, as closely as possible, the Geodetic heights, the heights must be holonomic to allow us to adjust loops of height differences and the system must be useful in practice. The clear winner is the classical system. The geoid is a physically meaningful (equipotential) and convex surface whose shape best approximates the shape of MSL with the increased availability of gravity and topographical density data the geoid can be determined to an accuracy about 2.5 cm (standard deviation) except for high mountains. Rigorous orthometric heights are holonomic and can be computed with a quite high accuracy. Even though they are not physically meaningful (water can flow up the orthometric slope), orthometric heights are eometrically meaningful, yet they are close enough to dynamic heights to be useful in most applications in practice. The geodetic system is not useful in practice; for example, in this system the height of the sea shore varies between -100 and +100 metres which would make life very difficult for port builders as well as lots of other people. The Molodensky system became quite popular in Europe since about 1980’s as it is easy to work with, locally and regionally. Globally, its reference surface, the quasi-geoid, is a fairly complex surface with folds, sharp edges and other unfriendly features, utterly inapplicable as a reference system. In practice, the quasi-geoid is obtained as a byproduct of geoid computation, obtained by adding an approximate correction to the geoid. Hence, our vote must go against this system as well.

Leanne Sulewski is a Spatial Analyst at the U.S. Department of Defense. She obtained her PhD in Geography from the University of South Carolina in 2013, specializing in GIS and Remote Sensing. Her publications include a variety of cartographic contributions to historical and scholarly works, including Schultz's The Papers of Eliza Lucas Pinckney and Harriott Pinckney Horry. Her scholarly research on population and land use dynamics in Southeast Asia has been published in the American Congress of Surveying and Mapping bulletin. Along with articles, she has collaboratively written a chapter on human migration in a proposed book due to be published in 2019. Her research interests include population and land use dynamics in Southeast Asia, and developing cartographic visualization and spatial analytic techniques.

The rapid generation of temporally enabled spatial point information is a key feature of nearly every discipline from epidemiology to criminology. These points can often become cumbersome to display in unison, providing the user little information aside from overall distribution and perhaps qualitative clustering. Even in a web-based environment, this type of display can be distracting for the user. In static graphic, these issues are easily ameliorated by combining the characterization of these points into attributed groupings such as pie charts and bar charts. In a regularly updated database, how can we dynamically provide customers with a qualitative and quantitative sense of magnitude and space? Using a case study derived from Armed Conflict location & Event Data (ACLED), this presentation features a novel approach to convey basic patterns in regularly updated temporal point data using scheduled Python scripts in a server environment. The presentation will review an application that automatically updates service-enabled web maps, depicting violent acts event instances in Africa in 2018. By automating task and summarizing information, this approach ensures the users are receiving the most up-to-date information, without relying on analysts to update the service. Additionally, this approach provides value-added, qualitative and quantitative summarized information without requiring the customer to manipulate the raw data manually.

Yuanze Chen has beening pursuing Master’s Degree in Computational Science and Engineering at the Department of Informatics, Technical University of  Munich, Germany, since 2016. He is currently working on his Master’s thesis in collaboration with Esri. His research concerns, remote sensing image processing based on deep learning methods.

Deep learning has been used successfully in computer vision problems, e.g. image classification, semantic segmentation and so on. We use deep learning in conjunction with ArcGIS to implement a model with advanced convolutional neural networks (CNN) for lithological mapping in the Mount Isa region (Australia). The area is ideal as there is only sparse vegetation and besides freely available Sentinel-2 and ASTER data, several geophysical datasets (radiometric data and magnetometry) are available from exploration campaigns. By fusing the data and thus covering a wide spectral range as well as other geophysical properties of rocks, we aim at significantly improving classification accuracies. We developed an end-to-end deep learning model inspired by the family of U-Net architectures, which was especially designed to effectively solve semantic segmentation problems in computer vision similar to lithological classification. We spatially resampled and fused multi-sensor remote sensing data with different bands and geophysical data into an image cube as input for our model. The model classifies each pixel of multiband imagery into different types of rocks according to a defined probability threshold. The connection between ArcGIS and the deep learning libraries was achieved by using the Python API for ArcGIS and implementing the workflow into Jupyter Notebooks. Preliminary results based on the Sentinel-2 bands alone are very promising with accuracies of around 60%. By including geophysical data and ASTER spectral bands that perfectly capture major absorption features of clay minerals and mafic minerals such as pyroxenes and carbonates, we should be able to improve significantly.

Gloria Bordogna is a Senior Researcher of CNR IREA since 2001, Member of EUSFLAT and IFSA Fellow since 2017. Her research activity concerns the modeling and management of imprecision and uncertainty within information retrieval systems and database management systems by means of soft computing. Her current interests are spatio-temporal analytics, quality assessment of crowdsourced geotagged information, and flexible fusion of geo big data.

One of the main problems of territorial decision makers when using multisource geo big data, and in particular remote sensing derived products, is the so called data overloading situation in which they might experience redundancy and possibly, inconsistency of data, which may cause them to doubt on the reliability and suitability of the data for taking decisions. Flexible and adaptive methods for big geo data synthesis are thus needed to overcome such impasse. Such methods should be capable to extract from multisource data, both compact maps, namely environmental status indicators maps, to understand the status of the environment, and metadata to understand the risk they can run when taking decisions on the basis of the information derived from the indicators maps. This means providing decision makers with a measure of the risk-prone and risk-adverse decision attitude modelled by the function generating the synthetic indicator map and an estimation of the omission and commission errors that this function can produce. In this contribution, we propose an adaptive fuzzy approach for the flexible generation of synthetic indicators maps from remote sensing and in situ geotagged observations. It exploits fuzzy operators to fuse multiple evidences extracted from remote sensing images. The semantic of the operator initially chosen to suit a desired decision attitude of the stakeholder is optimized via a learning algorithm which exploits in situ observations so as to generate an indicator map that minimizes both omission and commission errors. The method will be illustrated by considering the case study of flooded area mapping in a region in North of Italy.

Judit Rubio-Delgado is a PhD student in Physical Geography and Researcher at the Department of Mathematics of the University of Extremadura (Spain). She is a

Specialist in GIS and Remote Sensing and is working on the analysis of satellite images applied to precision agriculture. She has published several papers in JCR journals and in international conferences.

Chlorophyll (Chl) is an important biophysical parameter related to photosynthesis and micronutrient content in plants. The contents of Chl and nitrogen (N) present significant relationships in several crops. N is the main nutrient for the plant growth process and its content varies according to the phenological stage. Remote sensing data can provide specific local information of Chl content as an approximation of the N status of the plant. The goal of this study was to relate the content of Chl in olive-trees to their phenological stages using Sentinel-2A satellite images. TCARI/OSAVI index was used to estimate the leaf Chl content in four olive groves located in the Southwest of Spain. Satellite images from July 2016 to February 2018 were analyzed and TCARI/OSAVI indexes were computed monthly. Results indicated similar variation of the leaf Chl content index along the studied period for the four olive groves. Estimated Chl contents were lower during summer and autumn seasons, coinciding with the highest N consumption stages of the olive-tree (fruit set/early fruit growth and autumm growth). Estimated Chl contents were higher during winter season, coinciding with the nutrient acumulation period of the olive-tree (harvesting and latency stages). Minimum estimated Chl content was recorded in November 2016 and maximum in January 2017. The next step will be to calibrate TCARI/OSAVI values using spectral signatures of the  live-tree leaf reflectances measured in field.

Dietrich Heintz worked in the past seven years in the field of Remote Sensing with spaceborne SAR data, precision farming and crop insurance. Until 2017, he was an employee of Allianz Re in Zurich where he was In-Charge for maintaining field-trials and reference data analysis to develop map products for crop insurance schemes and precision farming applications. In this time he worked in close cooperation with sarmap, which is a technical leader in SAR data processing and software development. He has worked in applied research institutes in the field of Agriculture and Forestry (1996–2008) as well as in the field of Environmental Monitoring, GIS and Remote Sensing with Optical Data. He passed a distance learning course at UNIGIS Salzburg (2001–2003). He is an experienced Organic Horticulturist in Vegetable Production (1983- 1987) and has worked as an employee, first in a home for retarded children (Michaelshof in Hepsisau) and later in Zambia in a development aid project (1990 -1994). There he introduced organic vegetable production, invented a scheme to fertilize with algas from a reservoir over the irrigation system and trained school-leavers.

Statement of the Problem: Farmers who want to use precision farming technology need a navigation system and an on-board computer to apply fertilizers or other supplies, site specific. Moreover, they need a reliable source of actual and frequent geodata as reference for their management decision. In crop insurance schemes, the claims process is laborious and hence cost intensive. Repeatable results in loss estimation are hard to achieve. Remote sensing technology can help to identify areas within the plots with different gradual change before and after the event. This data can guide the loss adjuster to the relevant spots and later aggregate the loss ratio per area. For claims handling, it is crucial to have a reliable data supply to analyze the change caused by an event. Appropriate Technology: Spaceborne SAR (synthetic aperture radar) data penetrates clouds and guarantees reliable data supply throughout the year. The repetition rate is worldwide 12 days/Europe six days. Sentinel-1 SAR data has a ground resolution of 20x20 m, which is suitable for cropland monitoring. Significance: Sentinel-1 data delivers a C-band radar backscatter in two polarizations. The signal changes according to structure of the surface and humidity in the soil and plants. The two signals have to be transformed into a scale that indicates fresh biomass as proxy for decision making. Moreover, other information like phenological stage of cropland, growth dynamic or crop-type differentiation are valuable information for certain use cases and can be derived from SAR data. Conclusion: Sentinel-1 SAR data can be transformed into map products for specific business processes in the field of precision farming and crop insurance. The same technology can be useful for crop monitoring in the field of disaster management and early warning systems.

Sintha Prima Widowati Gunawan is currently a PhD candidate at Osaka University, Graduate School of Engineering, Division of Sustainable Energy and Environmental Engineering, Green Engineering for Global Environment Laboratory under the supervision of Professor Takashi Machimura and Professor Takanori Matsui. She has been a Junior Lecturer at the University of National Development in Yogyakarta, Indonesia, majoring in Urban and Regional Development Planning based on Environmental Management. She finished her Bachelor’s Degree in Architecture Engineering at the University of Gadjah Mada, Indonesia in 2005 and Master’s Degree in Urban Environmental Management at the Asian Institute of Technology, Thailand in 2008.

A single action of changing land use (LU) in peri-urban areas looks trivial, but cumulative effect of numerous individual acts would pose significant threat to the people and its surroundings. In Yogyakarta Special Region, Indonesia, the LU change trend was analyzed by observing Landsat 5 Thematic Mapper (TM) images in 2000, 2005 and 2011 with the aid of remote sensing technology and machine learning technique. First, a LU classifier was built by the stochastic gradient boosting method with nested 10-fold cross validation on R 3.4.0 using the 10416 normalized pixels of four spectral bands in Landsat 5 TM 2005 and 16 LU classes supervised by a provincial LU map in 2004. Kappa coefficient value was 0.34, showing overestimation of paddy field as the largest and widely variated class that attracted other LUs pixels. Kappa was improved to 0.50 by decomposing the paddyfield into four equal sized sub-classes by normalized difference vegetation index (NDVI). Aggregating the classified LUs into a simple urban/non-urban classification increased accuracy into 77.1% with positive best fit relation. The classifier was then employed for urban/non-urban distinction in all observation years by using separately normalized digital numbers in the same manner; thus, the urban/non-urban maps were mutually comparable. Result of classification showed urban areas were increasing from 24.4% in 2000 through 29.8% in 2005 to 32.04% in 2011, and this change was dominated by the housing class increase and paddy field decrease. This empirical evidence shall alert the local government to control LU change permitfor the individuals to curb the sprawl in peri-urban areas.

Andris Klepers has completed his PhD from University of Latvia, with International Exchange Studies in the University of Copenhagen and Trier University in Germany, and Post-doctoral Reserach from the University of Eastern Finland. He is the full time Researcher at the Institute of Social, Economic and Humanities Research, Vidzeme University of Applied Sciences, Latvia. He has published 16 scientific papers and is currently working on his research entitled, “Tourism Intelligence Latvia—using advantages of ICT development to create GIS solutions-based tourism intelligence platform providing analyses and forecasts of pan-Baltic multi-destination levels.”

The need to understand critical factors for competitiveness of tourist destinations and their temporal changes has grown considerably in recent two decades. This has become an especially relevant issue for tourism places located in the transition economies of post-Soviet space, particulary Baltic States, where different multi-level destination and entrepreneural networking forms are created on the basis of centrally planned economies infrastructure. New tourism system has been created and there have been unprecedented global changes as a result of the collapse of the Soviet Union in 1991. The recent development of GIS analysis tools has stressed the meaning and role of space, spatiality and contextuality of tourism places in new ways, providing valuable insight about comprehensive structural changes and development factors. This research aims to analyze the historical transformation process of the hospitality industry and destination management in Baltic states: Estonia, Latvia and Lithuania. Spatial contexts and changes are discussed and development directions are highlighted, providing common problematics in relation to path-dependent and path-creative perspectives. Rapid changes in the tourist accommodation sector are mapped with multi-factor spatial analysis method, characterizing new trends of international and domestic tourism demand. Forecasting abilities of spatial development will be discussed in context of tourism flow analyses and possibilities to create sustainable network of three-level destination management system in all three Baltic States.

Salman Zubair completed his PhD in Geography (GIS) in 2014 from the University of Karachi. At present, he is an Assistant Professor in the Department of Geography, University of Karachi, Pakistan. He is the Author of more than 15 research papers, published in reputed journals. He is also the Member of different national amd international geographical asscoiations. He has presented his research work as an Oral Presentor in more than 30 national and international conferences.

Spatial efficiency estimation of public bus service in densely populated cities is an obligatory step to define sustainable public transportation network. Some of the urban centers in developing countries facing disproportionate amount of attention in recent discourse on urban public transport system. This research aims to present the spatial efficiency of selected public buses, commuting to different parts of an urban center (i.e. city Karachi–Pakistan), using detour index (DI) algorithm and network analysis tool in GIS environment, enabled to estimate the load of the ever increasing number of commuters to already disadvantaged public vehicles in the city. Cartographic results showed higher DI values for long and well deviated course of routes passing through highly populated parts of urban area. On the contrary, lesser DI values were observed for bus routes passing through the lighter density areas located at peripheral parts of the city. Highest spatial efficiency values on the map illustrates that very few public buses pass through core areas of the city, accommodating more than hundred thousand passengers, adding to their hardships. Statistically, DI values had a significant (p <0.005) impact on spatial efficiency of bus routes. This situation exhibits the poor quality of bus service, sub-standard vehicles and clumsy routes. It is strongly suggested that urban centers need circular railway or mass transit system to cater the movement of millions of people. This would help to reduce the ongoing transport related issues to a greater extent.

Agustin Fernandez Eguiarte is responsible for the Informatics Unit for the Atmospheric and Environmental Sciences (UNIATMOS) of the Centro de Ciencias de la Atmósfera of UNAM and is a Geomatics Professor at the Facultad de Ingeniería of UNAM. In September 2014, he received the international Latin America Geospatial World Excellence Award granted by Geospatial Media and Communications. His research interests include, processing and quality control of continental and oceanic climatic-environmental data, GIS, marine and continental cartography, data bases, geospatial repositories and metadata, open data and interoperability and map servers on the internet.

The countries in the Intra-Americas Sea, particularly those located in the Gulf of Mexico and the Caribbean Sea, are exposed to the impacts of tropical cyclones that cause important human and economic losses to the region. The quantification of these impacts might be used as a preventive tool for risk reduction if it is used to evaluate the territory exposed to natural hazards and if it is made available in a friendly and simple way to concerned authorities and vulnerable people. Based on data of 2,117 tropical cyclones trajectories downloaded from the International Best Track Archive for Climate Stewardship for the period of June 1851 to November 2015 in the North Atlantic Basin, influence ratios were established for each trajectory: 150 km for wind and 350 km for rain, according to the publication by D.R. Chavas and K.A. Emanuel: “A QuikSCAT climatology of tropical cyclone size”. The number of strikes of the influence ratios overlapped on the coastline was counted. Maps were created with the total number of impacts of tropical cyclones on the coasts of countries located in the Gulf of Mexico and Western

Caribbean Sea. In the case of the 249,122 rural, 50,821 semi-urban and 4,562 urban localities of México, a similar process to that done for the coastline was completed. Maps were also created with the number of impacts of tropical cyclones on more than 3,000 localities.

Valentina Janev graduated in Electrical Engineering from the University of Ljubljana, Slovenia and received the PhD degree in the field of Semantic Web technologies from the University of Belgrade, School of Electrical Engineering and Computer Science. Recently, her research activity concerns Linked, Open and Big Data and implementation of EU policies in these domains. She serves as a reviewer of respectable international journals including International Journal on Semantic Web and Information Systems (IGI Global); International Journal of Digital Earth, Enterprise Information Systems, Knowledge Management Research and Practice, Information Systems Management (Taylor & Francis), Artificial Intelligence Review (Springer) and Data Mining and Knowledge Discovery (Wiley).

In the last few years, with the rise of the open data movement, a large and increasing number of governments and organizations have started to make information freely available and easily accessible online. In order to increase transparency, the information is also published as Linked Open Data. From the government systems perspective, the Linked Data approach can be observed as a technique for making the data interoperable and ready for consumption. In order to harmonize approaches used for describing the datasets, semantic services or repositories, the European Commission, in collaboration with the W3C consortium, has accepted a set of standard vocabularies that should be used to build public administration services. This presentation aims to discuss the challenges related to managing spatial and temporal information in Linked Data format and introduce the opportunities in terms of data aggregation/integration and creation of information mashups and decision-support tools on top of Linked Data. Being relatively a new field, currently there is a lack of tools that enable efficient exploration and analysis of linked geospatial statistical datasets. Therefore, ESTA-LD (Exploratory Spatio-Temporal Analysis) tool was developed to address some of the Linked statistical Data management issues, such as crossing the statistical and the geographical dimensions, producing statistical maps, visualizing different measures, and comparing statistical indicators of different regions through time. The modeling approach that was adopted so that the published data conform to the established standards for representing statistical, spatial and temporal data in Linked Data format will be also introduced. The main contribution of the research is related to the delivery of state-of-the-art open-source tools for retrieving, quality assessment, exploration and analysis of statistical Linked Data that is made available through a SPARQL endpoint.

Waqas Wajid is currently working in Landscape Architect firm L+P GmbH situated in Munich, he completed his masters from Anhalt university if applied sciences in landscape architect and done civil engineering from SSUET Karachi in 2010, he has ample work experience in construction industry. Also, he published an article and won a poster competition in DLA 2018

Background: Karachi is one of the biggest metropolitan cities of the world but lacks in basic urban infrastructure like mobility and recreational activities, etc. The author has tried to analyze the challenges and their coping strategies within constraints of its complications through ESRI ArcGIS by data available on the internet and open source platforms. The aim is to allocate and manage resources according to its urban districts. The criteria were urbanism, population, and recreational spaces. Methodology: The methods adopted to improve these urban complexities are Geo Design (Steinitz). GeoDesign applies system thinking to large problems of planning. Another approach was Spatial Planning through Remote Sensing: As the population is on a verge, so it requires a planning which helps the municipality to investigate and create an assessment out of it. For developing those spatial planning a platform of ESRI ArcGIS is widely used in a modern world. The city of Karachi has not yet adopted the same techniques yet. In this thesis form that particular platform shapefiles of each district will be created, by spotting out the best location which fulfills the practical engineering requirement specifically, topography of the region will determine the development and by just implementing a few changes in stereotype construction of the streets which is mainly its profile, the same area would act as reservoir during pluvial flooding. Which is a by-product of urbanization, drainage in terms of sewerage and stormwater will be compensated for this particular spatial planning. Conclusion: Cites are complex social engine where various ethnicities merge for gain economical and livable gains. The hypothesis of this research was: the developing cities could learn from developed ones and implement modern techniques in their developing phase. For Karachi in my thesis, I suggested strategies which could solve the various problem related to Urban infrastructure. Allocating the services in a district according to the demand which could perform multiple tasks simultaneously. interestingly while working professionally I found many of my results are standard planning practice in Munich. In a nutshell, these planning strategies could make Karachi a more livable city than before.

Peddada Jagadeeswara Rao is working as a Professor and Chairman, Board of Studies, Department of Geo-Engineering and Centre for Remote Sensing, College of Engineering (A), Andhra University. He has published research articles on groundwater resources, watershed management, solid waste management and HIV/AIDS in reputed national and international journals. He has guided 10 Research Scholars for their PhDs and he has been a consultant on water resources, remotesensing and GIS.

The present study is focused on spatio-temporal changes of land use/land cover and mangroves on identification of coastal erosion zones along the 339 km long coastline of the Krishna-Godavari (KG) delta region, Andhra Pradesh, India. This study analyzed multi-date satellite imagery of Landsat 4, 5, 7 and 8 TM and ETM sensors of 2002, 2011 and 2017 which reveals rapid changes in land use/land cover, deforestation of mangroves and coastal erosion. There is a decrease in area of about 28 km2 of mangroves and an increase in coastal erosion of 6.485 km2 is delineated from 2002 to 2011. Contrary to this, from the year 2011 to 2017 there is a considerable increase of 93 km2 in mangroves. During 2002 to 2017, severe beach erosion of 0.184 km2 and 0.418 km2 occurred at Uppada-Konapapapeta coast and Nilarevu river mouth, respectively. Similarly, an accretion of 0.526 km2 is observed at Vakalapudi in Godavari delta sea coast. In Krishna delta, the coast near Machilipatnam is getting eroded. No major coastal erosion is observed on the Krishna delta coast in comparison to Godavari delta coast. The results of this show that mangrove degradation and coastal erosion is taking place along the K-G delta region and suggests construction of sea walls, beach nourishment and prevention of the conversion of mangrove areas into aquacultural and agricultural land; these are the mostsuitable measures to arrest the coastal erosion.

Andrews Kwasi Afforo Odoom is a GIS specialist with over six years of working experience. He is an MSc GIS and Environmental Management Graduate from the

University of Brighton, UK. His working experiences includes GIS Teaching Assistant (University of Ghana), Mapping Engineer (Newmont Ghana Gold Limited) and GIS Technician (Ambiental Technical Solutions). He is currently the GIS Business Manager at Losamills Consult Limited, a leading mapping and engineering company in Ghana

Fly-tipping is one of the most devastating global hazards which offer significant threat to human health and environment. Hence, creating a readily read and accessible map for devising mitigation strategies at the local level is important. The study examined the integration of GIS to develop vulnerability map for aiding efficient surveillance of illegal dumping in Bassetlaw District, United Kingdom. The methodology involved three processes: (i) the integration of principal component analysis (PCA) and ordinary least square regression (OLS) to identify key geographical factors driving illegal dumping; (ii) developing criteria for ranking each factor and; (iii) building of geostatistical model into geographical information system to delineate the attractiveness of illegal dumping across the landscape. Using multiplicative index model, a composite vulnerability map was created from five key drivers for dumping in the district including population density, proximity to road, income, health and crime. The result revealed local disparities of illegal dumping due to varying geographical conditions. It also indicated that about 80% of incidences occurred within seven meters of road and that majority of waste type illegally dumped in the district was household waste (53%). Evaluation of the model was employed to examine and validate the effectiveness of the model output. By comparing the result with field incidence data, the map was successful in identifying districts with high-risk to illegal dumping. The result provides essential information for environmental managers to better understand and analyze illegal dumping and formulate remedial strategies.

Katerina Mekhlis has completed her PhD at the age of 26 from St. Petersburg State University, master degree at UAB (Barcelona), postmaster program Viadrina University (Germany). She is a CEO of NeoCityLab an IT GIS company specialised in data processing of laser scanning. She has an experience as a project leader for system implementation (incl. system design and implementation of ERP systems: SAP, Navision, Axapta etc.) in International Corporations such as Heineken, Henkel, Fraport, other companies.

This article is devoted to a new approach to the development of a system of 3D visualization of urban spaces and automated recognition of urban objects based on laser scanning data, photo panoramas and construction of three-dimensional models of cities (by identification of separate objects in city panoramas in automated mode and recovering from it 3D city models). The system may become an integrational platform for smart city, connecting all the data in one view point. It is also a useful tool for urban city management for city planning and maintenance. This kind of platform can be a very useful to perform urban city management, planning tasks as well as make a significant improvement in communication between citizens and institutions. To create this type of solution, the following problems were solved through automated data processing from mobile scanning equipment of standard type (for example Trimble, Topcon, etc.). Approaches for urban environment visualization were found to make it in the most perceivable way for different users and purposes (municipalities and citizen, games, education, augmented reality, etc.). Object recognition from point cloud in automated mode was developed to be used for a city modelling and other practical tasks (urban asset inventory, control and maintenance over the urban infrastructure objects and buildings, etc). Tools

to integrate data from all sources to create a “one window” solution – a Smart City platform. Several algorithms for analyzing the city environment are available and new can ones be developed by requirement (navigation, measurement of objects in panoramas, objects embedding for territory planning purposes; objects identification; objects comparison, data comparison, retrospective analysis to compare different time periods, etc.). The proposed solution is operative as an average European city can be scanned and processed within 2–3 weeks. This solution is very light to be used in mobile and web applications and will be appropriate for any user without the need of any special training to use the system.

Napoleon Kurantin is a Senior Lecturer and Head of Department of Development Policy in the GIMPA School of Public Service and Governance (GSPSG) at the Ghana Institute of Management and Public Administration. He was the Coordinator of the GIMPA–Ghana Armed Forces Command and Staff College (GAFCSC) Master’s Degree in Governance and Leadership; Coordinator of GIMPA–Kofi Annan International Peacekeeping Training Center (KAIPTC) Master of Arts Degree in Conflict, Peace and Security (MCPS) and was the Acting Director of GIMPA–HOTCATT. In his current position, he teaches courses in Research Methods (Advanced Quantitative Methods), Geo-spatial Statistics, Geospatial Governance Framework (Security and Good Governance), Natural Resources Management, Defence and Intelligence Management, Conflict and Crisis Management, Theories of Economic Development, Economic Science, Practice of Development Economics, Strategies and Management of Development, Environmental Economics and Management, Strategic Management and Leadership, Planning and Regional Economic Science. He has been with GIMPA, GAFCSC as Academic Directing Staff and KAIPTC as an Adjunct Lecturer for the past eight years

Poverty-alleviation programme is at the thrust of the agenda in the development strategies of the Government of Ghana. This study presents an integrated approach towards the development of a prototype of geo-spatial system (GIS) for the implementation of One District, One Factory (1D1F) in support of regional economic development and decision-making relative to poverty alleviation programmes. Based on the process of decentralization, poverty assessment is formulated at three administrative levels: district, regional and national (Central Government) to define and evaluate poor communities in the country. The main drawback to such a bottom-up approach is its emphasis mainly on socio-economic data-sets and manual techniques. GIS is hereby considered as a process and technique that strengthens the effectiveness of poverty assessment by changing bottom-up to a comprehensive integrated approach providing a stronger tool that enables the inclusion of spatial data-sets for the sitting of 1D1F, relative to regional economic development. To achieve the main goal of developing a prototype of GIS for the implementation of 1D1F policy in support of regional economic development and poverty alleviation programmes, this study adopts the Structured System of Development Methodology based on three phases namely: problem definition, system design and implementation. At the phase of problem definition, the proposal takes into consideration the problems of poverty assessment processes, the requirement of the users of such information to ensure an improved system; the outcome shows that in addition to socio-economic information, spatial data is included in poverty assessment and analysis that could be supported by GIS. At the second system design phase, the system design was undertaken on the basis of process modeling and data modeling using GIS. The integrated operation has at its thrust, the goal of overcoming the current lack of spatial data-sets. The last phase of data modeling is premised on the development of geo-database that integrates both socioeconomic and spatial data-sets in support of 1D1F. The prototype used Microsoft Access and ArcView software, respectively. Henceforth, a prototype of GIS is developed to investigate and examine the effectiveness of an integrated system geared towar the implementation of 1D1F in support of regional economic development and poverty alleviation programmes with Ghana as a laboratory.

Shogufa Popal has completed her Bachelor’s Degree from Kabul University in 2012 and her Master’s Degree from the University of Tokyo in 2018. Currently, she is a Lecturer in the Department of Forestry and Natural Resources, Faculty of Agriculture, Kabul University.

Deforestation and forest degradation are among significant environmental issues in Afghanistan but has not been studied intensively due to insecurity, confined budget, lack of expertise, and limited accessibility to new technology. In such a situation, remote sensing technology offers practical and economical means to acquire reliable, consistent, and up-to-date information for assessing forest cover and monitoring its spatial and temporal dynamics. Therefore, this study aims to detect forest cover change in six districts of Nuristan Province over the past three decades (1998–2016) using object-based classification of Landsat satellite imagery. The specific objectives to achieve the overall purpose of this study are: (i) ascertaining the current status of the forest cover, (ii) mapping forest cover in 1998, 2008, and 2016, and (iii) detecting forest cover change between 1988– 2008 and 2008–2016. The research methodology comprises of: (i) pre-processing of Landsat images using TNTmips (ii) objectbased image classification using eCognition Developer 9.0, (iii) mapping land and forest cover change, and (iv) quantifying land cover dynamics together with forest cover loss and gains. Overall, the results showed that although deforestation has not occurred on a large scale (7.26 km2) in the study area from 1998 to 2016, the forests have been continuously degraded during the study period, converting from dense broad-leaved forest to sparse as well as sparse to other vegetation areas, which can be  defined as “forest degradation”. Meanwhile, the overall accuracy for the maps were relatively high (>91 %).

Khalida Tadjer graduated Civil engineering at Mouloud Mammeri university (Tizi-ouzou, Algeria) then continued her studies (Master and Doctorate) in the Department of Civil Engineering, University of Blida, Algeria. At present, Khalida Tadjer is a assistant professor in the Department of Civil Engineering, University of Blida, Algeria. Her research activity concerns the assessment of seismic vulnerability of urabain area, the aim is to reduce the damage caused by the earthquake. She has several scientific papers and talks in different international conference.

The seismic vulnerability of an urban area is of a great deal for local authorities especially those facing earthquakes. So, it is important to have an efficient tool to assess the vulnerability of existing buildings. Blida is located in the north part of Algeria, an area prone to seismicity. It is classified zone III according to the Algerian Seismic Code (RPA99 version 2003). The town is among the oldest cities in the north. Build especially during the colonial period, Blida is characterized by vulnerable urban conditions with dense buildings and narrow roads. Using geographic information systems (GIS), the seismic vulnerability of Blida is assessed. First the vulnerability indexes of buildings are calculated, then making seismic scenarios. Damage rates are determined taking into account the seismotectonic aspect of the region and the vulnerability curves of structures commonly found in Blida. The rates of damage caused by the earthquake considered in the scenario highlighted the high vulnerability of Blida. These results can allow elaborating strategic countermeasure plans for the earthquake risk mitigation in the city.