Using Geospatial Information Systems in Analyzing Urbanization Impacts on Stream Habitats in Southern Mississippi Coastal Ecosystem

The proliferation of urban development with concentration in population and human-environment interaction has intensified around urban environments. This has resulted in the degradation of urban environments, overuse of natural resources and widespread pollution of ecosystems, The patterns of design initiatives continue to follow unsustainable path with impacts on stream ecosystems. Accordingly, the paper adopts geospatial information systems and sustainability principles for the identification and sequential mapping of stressors impeding natural systems in Southern Mississippi. The results not only reveal that the study area experienced some significant changes in its watershed environments, but the stream habitat ecosystem remains under stress. The recommendations for mitigating the problems range from policy considerations to the adoption of ecosystem approach.

An ethics assessment list for geoinformation ecosystems: revisiting the integrated geospatial information framework of the United Nations

To achieve sustainable development goals,georeferenced data and geographic information systems play a crucial role.Yet,the way in which these data and systems are summoned upon rests on positivist assumptions which overlook both epistemological and ethical concerns.This is epitomized by the integrated geospatial information framework(IGIF)of the United Nations,which,from the perspective of sustainable development,aims to provide guidance for the management of geoinformation and related tools,considering these as mirrors of the physical world.In this respect,the article has three main goals.First,it delivers an epistemological and ethical critique of the IGIF,by highlighting its internal tensions.Second,it suggests how the IGIF and similar geoinformation initiatives can benefit from an ethical reflection that allows to conduct georeferenced practices in a fair(er)way.Third,it designs an ethics assessment list for self-evaluating the ethical robustness of geoinformation initiatives as ecosystems.

Characterization of Small-Scale Farmers and Assessment of Their Access to Crop Production Information in Selected Counties of Kenya

Small-scale farming accounts for 78% of total agricultural production in Kenya and contributes to 23.5% of the country’s GDP. Their crop production activities are mostly rainfed subsistence with any surplus being sold to bring in some income. Timely decisions on farm practices such as farm preparation and planting are critical determinants of the seasonal outcomes. In Kenya, most small-scale farmers have no reliable source of information that would help them make timely and accurate decisions. County governments have extension officers who are mandated with giving farmers advisory services to farmers but they are not able to reach most farmers due to facilitation constraints. The mode and format of sharing information is also critical since it’s important to ensure that it’s timely, well-understood and usable. This study sought to assess access to geospatial derived and other crop production information by farmers in four selected counties of Kenya. Specific objectives were to determine the profile of small-scale farmers in terms of age, education and farm size;to determine the type of information that is made available to them by County and Sub-County extension officers including the format and mode of provision;and to determine if the information provided was useful in terms of accuracy, timeliness and adequacy. The results indicated that over 80% of the farmers were over 35 years of age and over 56% were male. Majority had attained primary education (34%) or secondary education (29%) and most farmers in all the counties grew maize (71%). Notably, fellow farmers were a source of information (71%) with the frequency of sharing information being mostly seasonal (37%) and when information was available (43%). Over 66% of interviewed farmers indicating that they faced challenges while using provided information. The results from the study are insightful and helpful in determining effective ways of providing farmers with useful information to ensure maximum benefits.

Sustainable development and geospatial information:a strategic framework for integrating a global policy agenda into national geospatial capabilities

The 2030 Agenda for Sustainable Development provides a new global policy to guide the way countries collectively manage and transform the social,economic,and environmental dimensions of people and the planet over the next 15 years.Achieving sustainable development presents all countries and the global policy community with a set of significant development challenges that are almost entirely geographic in nature.Many of the issues impacting sustainable development can be analyzed,modeled,and mapped within a geographic context,which in turn can provide the integrative framework necessary for global collaboration,consensus and evidence-based decision-making.However,and despite significant advances in geospatial information technologies,there is a lack of awareness,understanding and uptake,particular at the policy and decision-making level,of the vital and integrative role of geospatial information and related enabling architectures such as National Spatial Data Infrastructures.This paper reasons that the role of geospatial information in contributing to sustainable development has not adequately been described by either the sustainable development policy practice or by the geospatial professional community.This lack of policy and guidance,with commensurate critical gaps and connection points with national geospatial frameworks,is a visible impediment to developing countries and those most affected by the challenges and need to achieve sustainable development.The global geospatial community now has a unique opportunity to integrate and connect geospatial information into the global development agenda in a more holistic and sustainable manner,specifically in contributing their data resources toward measuring and monitoring the 17 Sustainable Development Goals,and their 169 associated targets,through the global indicator framework that anchors the 2030 Agenda for Sustainable Development.This paper introduces and discusses a new strategic framework for linking a global policy to national geospatial capabilities.

GeoScope:Full 3D Geospatial Information System Case Study

Aiming at the integrative management and comprehensive applications of large-scale 3D geospatial information covering the full 3D space of a city, this paper briefly introduces the design and implementation of a full 3D GIS platform： GeoScope, which provides a professional solution for the massive full three-dimensional geospatial data integration, management, analysis, visualization, and applications. GeoScope is characterized by： （1） extendible software architecture based on the hierarchical message bus, facilitates multimodal integrative applications of 2D GIS and 3D GIS; （2） unified 3D city models, support multiscale se- mantic representation of outdoor ＆ indoor and aboveground ＆ underground 3D objects; （3） high-efficient 3D geospatial database engine, supports integrated management of massive 3D geospatial data for real-time applications; and （4） high-performance visualization engine exploiting the massively parallel computation architecture of modem GPUs, supports real-time realistic rendering of large-scale complicated 3D geospatial environments. The successful pilot application of GeoScope is also illustrated with the 3D city models of 8494 knl2 of the whole Wuhan City, the largest city in middle China.

Geospatial Information Service Based on Digital Measurable Image——Take Image City·Wuhan as an Example

t LBS （location-based service） is a remarkable outcome of the development from GIS to geospatial information service. Faced by the requirements of geospatial information from the masses and the opportunity provided by the next generation lnternet and Web 2.0, a new model of geospatial information service based on DMI （digital measurable image） is presented. First, the con- cept of LBS and the opportunities of Web 2.0 are introduced, then the characteristic of DMI is discussed. Taking the Image City.Wuhan as an example, the function ofgeospatial information service based on DM! is introduced. Finally, the feasibility for its industrialization is discussed.

Perspectives on geospatial information science education:an example of urban planners in Southern Africa

Since the introduction of geographic information systems(GIS)in the 1960s,it has evolved tremendously to an extent that it permeates our daily lives.Initially,GIS usage started in the developed countries and now increasingly filtered to developing countries.The town planning profession was one of the early adopters of GIS.Geospatial information is a useful source of data that is needed in urban planning.In these days of the New Urban Agenda 2030,smart cities are even more required from planners in using geospatial information to face urban challenges such as sustainable urban development and climate change.Although GIS has promised a lot for urban planning,it has not reached its full potential.Moreover,many studies have focused on developed countries with limited studies on geospatial information application in municipalities and GIS education from a perspective of developing countries.In this study,a survey on the usage of geospatial information science(GSIS)in two cities,namely Bulawayo in Zimbabwe and Ekurhuleni in South Africa,was conducted,and an overview of the state of GIS curricula in planning schools is discussed.The results indicate that considerable progress has been made in the application of geospatial information in municipal planning;however,there are impediments limiting the full utilization of geospatial information in local municipalities.These impediments include:inadequate GIS curricula in planning schools,lack of resources,and lack of political will.These challenges manifest differently in well-resourced municipalities and those with limited resources.The study proposes planning-relevant GIS curricula to improve the level of GIS use in planning practice.

Virtual globe-based integration and sharing service method of GeoSpatial Information

How to integrate and disseminate globally distributed multi-source and heterogeneous spatial information is an open problem in integration and sharing service of geographic information. Here we propose a new service architecture suitable for integra- tion and sharing of distributed multi-source geographic information. We also propose a global virtual pyramid model, which can be applied in 3D virtual globes. In view of the difficulty of web multi-node geographic information sharing service, we propose a web multi-node service aggregation method, integrated in our autonomously developed virtual globe platform Geo- Globe and introduced in the National Platform for Common GeoSpatial Information Services named ＂T1ANDITU＂. It achieves 2D and 3D integration for geographic information service.

Value of incorporating geospatial information into the prediction of on-street parking occupancy - A case study

In light of growing urban traffic,car parking becomes increasingly critical for cities to manage.As a result,the prediction of parking occupancy has sparked significant research interest in recent years.While many external data sources have been considered in the prediction models,the underlying geographic context has mostly been ignored.Thus,in order to study the contribution of geospatial information to parking occupancy prediction models,road network centrality,land use,and Point of Interest(POI)data were incorporated in Random Forest(RF)and Artificial Neural Network(ANN,specifically Feedforward Neural Network FFNN)prediction models in this work.Model performances were compared to a baseline,which only considers historical and temporal input data.Moreover,the influence of the amount of training data,the prediction horizon,and the spatial variation of the prediction were explored.The results show that the inclusion of geospatial information led to a performance improvement of up to 25%compared to the baseline.Besides,as the prediction horizon expanded,predictions became less reliable,while the relevance of geospatial data increased.In general,land use and POI data proved to be more beneficial than road network centrality.The amount of training data did not have a significant influence on the performance of the RF model.The ANN model,conversely,achieved optimal results on a training input of 5 days.Likely attributable to varying occupancy patterns,prediction performance disparities could be identified for different parking districts and street segments.Generally,the RF model outperformed the ANN model on all predictions.

Earthquake risk assessment in NE India using deep learning and geospatial analysis

Earthquake prediction is currently the most crucial task required for the probability,hazard,risk mapping,and mitigation purposes.Earthquake prediction attracts the researchers'attention from both academia and industries.Traditionally,the risk assessment approaches have used various traditional and machine learning models.However,deep learning techniques have been rarely tested for earthquake probability mapping.Therefore,this study develops a convolutional neural network(CNN)model for earthquake probability assessment in NE India.Then conducts vulnerability using analytical hierarchy process(AHP),Venn's intersection theory for hazard,and integrated model for risk mapping.A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators.Prediction classification results and intensity variation were then used for probability and hazard mapping,respectively.Finally,earthquake risk map was produced by multiplying hazard,vulnerability,and coping capacity.The vulnerability was prepared by using six vulnerable factors,and the coping capacity was estimated by using the number of hospitals and associated variables,including budget available for disaster management.The CNN model for a probability distribution is a robust technique that provides good accuracy.Results show that CNN is superior to the other algorithms,which completed the classification prediction task with an accuracy of 0.94,precision of 0.98,recall of 0.85,and F1 score of 0.91.These indicators were used for probability mapping,and the total area of hazard(21,412.94 km^(2)),vulnerability(480.98 km^(2)),and risk(34,586.10 km^(2))was estimated.

Geostructural stability assessment of cave using rock surface discontinuity extracted from terrestrial laser scanning point cloud

The use of terrestrial laser scanning（TLS） in the caves has been growing drastically over the last decade.However, TLS application to cave stability assessment has not received much attention of researchers.This study attempted to utilize rock surface orientations obtained from TLS point cloud collected along cave passages to（1） investigate the influence of rock geostructure on cave passage development, and（2）assess cave stability by determining areas susceptible to different failure types. The TLS point cloud was divided into six parts（Entry hall, Chamber, Main hall, Shaft 1, Shaft 2 and Shaft 3）, each representing different segments of the cave passages. Furthermore, the surface orientation information was extracted and grouped into surface discontinuity joint sets. The computed global mean and best-fit planes of the entire cave show that the outcrop dips 290° with a major north-south strike. But at individual level, the passages with dip angle between 26° and 80° are featured with dip direction of 75°-322°. Kinematic tests reveal the potential for various failure modes of rock slope. Our findings show that toppling is the dominant failure type accounting for high-risk rockfall in the cave, with probabilities of 75.26%, 43.07%and 24.82% in the Entry hall, Main hall and Shaft 2, respectively. Unlike Shaft 2 characterized by high risk of the three failure types（32.49%, 24.82% and 50%）, the chamber and Shaft 3 passages are not suffering from slope failure. The results also show that the characteristics of rock geostructure considerably influence the development of the cave passages, and four sections of the cave are susceptible to different slope failure types, at varying degrees of risk.

The Australian approach to geospatial capabilities;positioning,earth observation,infrastructure and analytics:issues,trends and perspectives

This paper examines the current state of three of the key areas of geospatial science in Australia:positioning;earth observation(EO);and spatial infrastructures.The paper discusses the limitations and challenges that will shape the development of these three areas of geospatial science over the next decade and then profiles what each may look like in about 2026.Australia’s national positioning infrastructure plan is guiding the development of a nation-wide,sub decimeter,real-time,outdoor positioning capability based on multi-GNSS and in particular the emerging precise point positioning−real-time kinematic(PPP-RTK)capability.Additional positioning systems including the ground-based Locata system,location-based indoor systems,and beacons,among others are also discussed.The importance of the underpinning role of a next generation dynamic datum is considered.The development of Australia’s first EO strategy is described along with the key national needs of the products of remote sensing.The development of massive on-line multi-decadal geospatial imagery data stores and processing engines for co-registered stacks of continuous base-line satellite imagery are explored.Finally,perspectives on the evolution of a future spatial knowledge infrastructure(SKI)emerging from today’s traditional spatial data infrastructures(SDIs)are provided together with discussion of the growing importance of geospatial analytics for transforming whole supply chains.

Advances of geo-spatial intelligence at LIESMARS

The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Science(GSIS)problems.These include image matching,image target detection,change detection,image retrieval,and for generating data models of various types.This paper discusses the connection and synthesis between AI and GSIS in block adjustment,image search and discovery in big databases,automatic change detection,and detection of abnormalities,demonstrating that AI can integrate GSIS.Moreover,the concept of Earth Observation Brain and Smart Geo-spatial Service(SGSS)is introduced in the end,and it is expected to promote the development of GSIS into broadening applications.

A capability matching and ontology reasoning method for high precision OGC web service discovery

Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type,version,time,space,and scale has become a challenge in the integration of geospatial information services.A new method for retrieving Open Geospatial Consortium(OGC)Web Service(OWS)that deals with this challenge using page crawling,link detection,service capability matching,and ontology reasoning,is described in this paper.Its major components are distributed OWS,the OWS search engine,the OWS ontology generator,the ontology-based OWS catalog service,and the ontology-based multi-protocol OWS client.Experimental results show that the execution time of this proposed method equals only 0.26 of that of Nutch’s method.In addition,the precision is much higher.Moreover,this proposed method can carry out complex OWS reasoning-based queries.It is being used successfully for the Antarctica multi-protocol OWS portal of the Geo-Information Web Service Portal of the Polar.

A brief overview of current status of European spatial data infrastructures-relevant developments and perspectives for Bulgaria

The paper aims to present a concise overview of the current status of the national spatial data infrastructures(SDI)of the European Union(EU)member states combined with specific peculiarities for Bulgaria.Some major challenges within the progress of the EU SDIs establishing,which is regulated by the European Directive INSPIRE(Infrastructure for spatial information in Europe)toward establishment of a SDI for environmental policies and activities,are marked out.Available comparative analyses of the main indicators for metadata,data-sets,and data services provided by EU member states are briefly discussed as a special attention is given to the Bulgarian progress.Recent achievements on accelerating the process of implementing the recommendations of the INSPIRE Directive in Bulgaria are outlined.

Modelling and predicting the spatial dispersion of skin cancer considering environmental and socio-economic factors using a digital earth approach

Almost all causative factors of diseases depend on location.The Digital Earth approach is suitable for studying diseases globally.Geospatial information systems integrated with statistical models can be used to model the relationship between a disease and its causative factors.Through modelling,the most important causative factors can be extracted and the epidemiology of the disease can be observed.In this paper,skin cancer(the most common type of cancer)has been modelled based on its causative factors,including climate factors,people’s occupations,nutrition habits,socio-economic factors,and usage of chemical fertiliser.To fit the model,a data framework was first designed,and then data were gathered and processed.Finally,the disease was modelled using Generalised Linear Models(GLM),a statistical model based on the location of the factors.The results of this study identify the most important causative factors together with their relative priority.Furthermore,a model was used to predict the change in skin cancer occurrences caused by a change in one of its causative factors.This work illustrates the ability of the model to predict disease occurrence.Thus,by using this Digital Earth approach,skincancer can be studied in all the key countries around the world.

Universal workflow-based high performance geo-computation service chain platform

Based on web services and the Activiti 5.0 workflow engine,we built a workflow-based web service chain processing flow platform to simplify the operations of complex geospatial information processing applications in the area of high performance geocomputation.The proposed framework includes a web portal and a convenient mode for similar specific applications.We also designed a general service publishing template for high performance commutating(HPC)that can reduce platform differences among different HPC systems and facilitate HPC techniques to improve processing efficiency.Finally,we used a digital elevation model to verify and test the platform.The results indicated that the proposed service chain platform offered convenience and efficiency when processing massive data with HPC.

Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification,case study:Fars domain,Iran

It is well agreed that geologic risk occurs during hydrocarbon exploration because diverse uncertainties accompany the entire hydrocarbon system parameters such as the source rock,reservoir rock,trap and seal rock.In order to overcome such attributes with uncertainties,a number of soft computing methods are used.Information granules could be provided by the Rough Fuzzy Set Granulation(RFSG)with a thorough quality evaluation.This is capable of attribute reduction that has been claimed to be essential in investigating the hydrocarbon systems.This paper is an endeavor to recommend a Geospatial Information System(GIS)-based model with the aim of categorizing the hydrocarbon structures map consistent with the uncertainty range concepts of geologic risk in the rough fuzzy sets and granular computing.The model used the RFSG for the attribute reduction by a Decision Logic language(DLlanguage).The RFSG was employed in order to classify hydrocarbon structures according to geological risk and extract the fuzzy rules with a predefined range of uncertainty.In order to assess the precisions of the fuzzy decisions on the hydrocarbon structure classification,the fuzzy entropy and fuzzy cross-entropy are applied.The proposed RFSG model applied for 62 structures as the training data,average fuzzy entropy has been calculated as 0.85,whereas the average fuzzy cross-entropy has been calculated 0.18.As it can be discerned,just seven structures had cross-entropies greater than 0.1,while three structures were larger than 0.3.It is implied that the precision of the proposed model is about 89%.The results yielded two reductions for the condition attributes and 11 fuzzy rules being filtered by the granular computing values.