Encouraging active learning when teaching geospatial sciences

In today’s world,higher education,especially in technical sciences,is crucial when speaking about a change in values and attitudes towards sustainability.Engaging students in learning and training process as well as developing their research skills and creative capacity is challenging.This study inquiries into the current academic activities and role of used educational technologies and methodologies for encouraging active learning in both undergraduate and postgraduate programs when teaching any engineering course in general,and geospatial sciences in particular.The result of the study suggests the importance of interdisciplinary project and problem-based learning,building partnership with universities,students and industrial companies,monitoring,developing,and implementation of new educational technologies.

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.

Taking the pulse of COVID-19:a spatiotemporal perspective

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the U.S.The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and Brazil.In response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global citizens.This perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

The challenges of developing an open source, standards-based technology stack to deliver the latest UK climate projections

To improve the understanding of local and regional effects of climate change,the UK government supported the development of new climate projections.The Met Office Hadley Centre produced a sophisticated set of probabilistic projections for future climate.This paper discusses the design and implementation of an interactive website to deliver those projections to a broad user community.The interface presents complex data sets,generates on-the-fly products and schedules jobs to an offline weather generator capable of outputting gigabytes of data in response to a single request.A robust and scalable physical architecture was delivered through significant use of open source technologies and open standards.

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.

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.