Abstract: Community Engaged Learning (CEL) is a pedagogical approach that involves students, community partners, and instructors working together to analyze and address community-identified concerns through experiential learning. Implementing community-engagement in geography courses and, specifically, in GIS courses is not new. However, while students enrolled in CEL GIS courses critically reflect on social and spatial inequalities, GIS tools themselves are mostly applied in uncritical ways. Yet, CEL GIS courses can specifically help students understand GIS as a socially constructed technology which can not only empower but also disempower the community. This contribution presents the experiences from a community-engaged introductory GIS course, taught at a Predominantly White Institution (PWI) in Virginia (USA) in Spring ’24. It shows how the course helped students gain a conceptual understanding of what is GIS, how to use it, and valuable software skills, while also reflecting about their own privileges, how GIS can (dis)empower the community, and their own role as a GIS analyst. Ultimately, the paper shows how the course supported positive changes in the community, equity in education, reciprocity in university/community relationships, and student civic-mindedness.

In this seminar, you will learn how to use Web AppBuilder to create powerful GIS apps that run on any device without writing a single line of code. You will also learn how to quickly build web apps with your data, selection of widgets, and the theme you choose, to make them available to your organization.This seminar was developed to support the following:ArcGIS OnlineWeb AppBuilder for ArcGISWeb AppBuilder for ArcGIS (Developer Edition) 1.0

The Geographic Information System (GIS) Solutions market is experiencing robust growth, driven by increasing adoption across diverse sectors. The market, estimated at $15 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a Compound Annual Growth Rate (CAGR) of approximately 8%. This growth is attributed to several key factors. Firstly, the rising need for precise spatial data analysis and visualization across industries like agriculture (precision farming), oil & gas (resource exploration and management), and construction (infrastructure planning and development) is driving demand. Secondly, advancements in GIS software and services, including cloud-based solutions and AI-powered analytics, are enhancing efficiency and accessibility. Thirdly, government initiatives promoting smart cities and infrastructure development are further boosting market expansion. The market is segmented by application (Agriculture, Oil & Gas, AEC, Transportation, Mining, Government, Healthcare, Others) and type (Software, Services), with software solutions currently holding a larger market share due to increasing digitization and data-driven decision-making. North America and Europe are currently the leading regional markets, benefiting from established infrastructure and high technology adoption rates, but Asia-Pacific is poised for significant growth driven by rapid urbanization and infrastructure development. Despite the promising growth trajectory, certain challenges remain. High initial investment costs for GIS software and implementation can be a barrier to entry for smaller businesses. Furthermore, the need for skilled professionals to effectively utilize and manage GIS data poses a considerable constraint. However, the ongoing development of user-friendly interfaces and accessible training programs is mitigating this issue. The competitive landscape is characterized by a mix of established players like ESRI, Hexagon, and Pitney Bowes, alongside emerging technology providers. These companies are actively investing in R&D and strategic partnerships to maintain their competitive edge and capitalize on the market's expansion. The long-term outlook for the GIS solutions market remains positive, with continuous innovation and expanding applications across various sectors paving the way for sustained growth throughout the forecast period.

Probability of Development, Northeast U.S. is one of a suite of products from the Nature’s Network project. Nature’s Network is a collaborative effort to identify shared priorities for conservation in the Northeast, considering the value of fish and wildlife species and the natural areas they inhabit.This index represents the integrated probability of development occurring sometime between 2010 and 2080 at the 30 m cell level. It was based on models of historical patterns of urban growth in the Northeast, including the type (low intensity, medium intensity and high intensity), amount and spatial pattern of development, and incorporates the influence of factors such as geophysical conditions (e.g., slope, proximity to open water), existing secured lands, and proximity to roads and urban centers. The projected amount of new development is downscaled from county level forecasts based on a U.S. Forest Service 2010 Resources Planning Act (RPA) assessment. A complementary product, Probability of Development, 2030, Northeast U.S., estimates the probability of development over a shorter time-scale.Note: based on revisions of the sprawl model, this version was revised in July 2017 to better reflect relatively higher probabilities of development in close vicinity to roads, which is most evident in rural areas.Description and DerivationThe derivation of the integrated probability of development layer was complex. Please consult the detailed technical documentation for a full description of the background data used, the computation of integrated probabilities from a stochastic model, and information about the related urban growth model. The following is a summary of the five major steps of the derivation: 1) Determining historical patterns of growthTo understand how past patterns of development have occurred, historical data from NOAA (for Maine and Massachusetts) and the Chesapeake Bay Watershed Landcover Data Series were obtained for the years 1984 (Chesapeake Bay only), 1996, and 2006. The data were used to model the occurrence of six different development transition types: New growthundeveloped to low-intensity (20-49% impervious surface; e.g., single-family homes)undeveloped to medium-intensity (50-79% impervious surface; e.g., small-lot single-family homes)undeveloped to high-intensity (80-100% impervious surface; e.g., apartment complexes and commercial/industrial development) Intensificationlow- to medium-intensitylow- to high-intensitymedium- to high-intensity Separate models were developed to represent development patterns at model points representing landscapes differing along two dimensions: intensity of development and amount of open water. Predictor variables in the models account for the intensity of existing development and landscape context (e.g. intensity and distance of nearest roads, amount of open water). Analysis of the historical data was based on dividing the landscape into “training windows,” 15km on a side, to determine the historical distribution of transition types and the total amount of historical development. 2) Application to current landscapesFuture patterns of development were projected based on the observed historical patterns. As the first step in this process, the entire Northeast was subdivided into 5km “application panes,” each of which was the center pane of a (3 x 3) “application window”, 15 km on a side. Each of these overlapping application windows was then matched to the three most similar training windows on the basis of intensity of development from the UMass integrated landcover layer, (derived in turn from the 2011 National Landcover Database and other sources), as well as geographic proximity, amount of open water, and density of roads. . For each application window, according to how it mapped on to the dimensions of development and open water modelled above, the relative probability of each of the six development transition types was determined on a scale of 30m cells. 3) Predictions for changing land-useFuture urban acreage by county was predicted as part of an assessment for the U.S. Forest Service 2010 Resources Planning Act. The derivation of this product, the new growth forecasted for the 70 years between 2010 and 2080 was transformed into demand in units of 30m cells. Demand for each county (or census Core Based statistical Area, where relevant) was allocated to the corresponding application windows based on the average of the total amount of historical development in the three matched training windows. 4) Combining models of past and predictions for the futureThe relative probability of a transition type occurring in each cell in a window was used to distribute the allocated demand of new growth throughout the window. The result was an actual probability of development for the transition occurring sometime between 2010- 2080 at the 30 m cell level. Already existing urban land-use was intensified (i.e., transitions 4-6) in proportion to historic patterns determined from the matched training windows, and distributed according to the probability of those transition types across the cells in the window. The combining of probabilities and demand to distribute development to cells was done for each transition type in turn; thus, each cell received a separate probability of being developed through each of the six transition types. Through the application of this process in every application window, an actual probability of development was determined for each cell with reference to nine slightly different contexts corresponding to each of the overlapping windows in which the pane was situated. 5) Smoothing and integrationAn additional step was used to create a smooth and continuous probability of development surface, not subject to abrupt differences along arbitrary boundaries. Cell by cell, actual probabilities of development from each of the overlapping windows were combined such that the closer to a window’s center a cell was located, the more weight the probability derived from it was given. Consequently, each cell had one weighted average probability that was part of a continuous probability of development surface for each transition type. Finally, the probability of development by each of six transition types was integrated for each cell. More weight was given to new growth, such that the probability of undeveloped land becoming urban had more impact than the probability of an intensification of development. The final product is a single layer of the integrated probability of development by 2080, extending across the entire Northeast on the scale of 30 m cells.Known Issues and Uncertainties As with any project carried out across such a large area, the Probability of Development dataset is subject to limitations. The results by themselves are not a prescription for on-the-ground action; users are encouraged to verify, with field visits and site-specific knowledge, the value of any areas identified in the project. Known issues and uncertainties include the following:Although this index is a true probability, it is best used in a relative manner to compare values from one location to anotherThe GIS data upon which this product was based, especially the National Land Cover Dataset (NLCD), are imperfect. Errors of both omission and commission affect the mapping of current development and in turn, models of the probability of future development. Likewise, the forecasts in the 2010 Resources Planning Act assessment, the basis of the projected demand for new growth, contains uncertainties. While the model is anticipated to generally correctly indicate where development is likely to occur, predictions at the cell level are not expected to be highly reliable.Users are cautioned against using the data on too small an area (for example, a small parcel of land), as the data may not be sufficiently accurate at that level of resolution.This model is built on the assumption that future patterns of development will match patterns in the past.It is important to recognize that the integrated probability of development is highest near existing roads, largely because the urban growth model does not attempt to predict the building of new roads and the development associated with them, nor does it incorporate county or town level planning for infrastructure. Because proximity to roads is an important and dominant predictor of development at the 30- m cell level in the model, the integrated probability of development surface is heavily weighted towards existing roads. It is not specifically designed to predict where a subdivision might be developed in the future.

Goal 4Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allTarget 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomesIndicator 4.1.1: Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sexSE_TOT_PRFL: Proportion of children and young people achieving a minimum proficiency level in reading and mathematics (%)Indicator 4.1.2: Completion rate (primary education, lower secondary education, upper secondary education)SE_TOT_CPLR: Completion rate, by sex, location, wealth quintile and education level (%)Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary educationIndicator 4.2.1: Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, by sexiSE_DEV_ONTRK: Proportion of children aged 36−59 months who are developmentally on track in at least three of the following domains: literacy-numeracy, physical development, social-emotional development, and learning (% of children aged 36-59 months)Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sexSE_PRE_PARTN: Participation rate in organized learning (one year before the official primary entry age), by sex (%)Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including universityIndicator 4.3.1: Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sexSE_ADT_EDUCTRN: Participation rate in formal and non-formal education and training, by sex (%)Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurshipIndicator 4.4.1: Proportion of youth and adults with information and communications technology (ICT) skills, by type of skillSE_ADT_ACTS: Proportion of youth and adults with information and communications technology (ICT) skills, by sex and type of skill (%)Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situationsIndicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregatedSE_GPI_PTNPRE: Gender parity index for participation rate in organized learning (one year before the official primary entry age), (ratio)SE_GPI_TCAQ: Gender parity index of trained teachers, by education level (ratio)SE_GPI_PART: Gender parity index for participation rate in formal and non-formal education and training (ratio)SE_GPI_ICTS: Gender parity index for youth/adults with information and communications technology (ICT) skills, by type of skill (ratio)SE_IMP_FPOF: Immigration status parity index for achieving at least a fixed level of proficiency in functional skills, by numeracy/literacy skills (ratio)SE_NAP_ACHI: Native parity index for achievement (ratio)SE_LGP_ACHI: Language test parity index for achievement (ratio)SE_TOT_GPI: Gender parity index for achievement (ratio)SE_TOT_SESPI: Low to high socio-economic parity status index for achievement (ratio)SE_TOT_RUPI: Rural to urban parity index for achievement (ratio)SE_ALP_CPLR: Adjusted location parity index for completion rate, by sex, location, wealth quintile and education levelSE_AWP_CPRA: Adjusted wealth parity index for completion rate, by sex, location, wealth quintile and education levelSE_AGP_CPRA: Adjusted gender parity index for completion rate, by sex, location, wealth quintile and education levelTarget 4.6: By 2030, ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracyIndicator 4.6.1: Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sexSE_ADT_FUNS: Proportion of population achieving at least a fixed level of proficiency in functional skills, by sex, age and type of skill (%)Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture’s contribution to sustainable developmentIndicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessmentTarget 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for allIndicator 4.a.1: Proportion of schools offering basic services, by type of serviceSE_ACS_CMPTR: Schools with access to computers for pedagogical purposes, by education level (%)SE_ACS_H2O: Schools with access to basic drinking water, by education level (%)SE_ACS_ELECT: Schools with access to electricity, by education level (%)SE_ACC_HNDWSH: Schools with basic handwashing facilities, by education level (%)SE_ACS_INTNT: Schools with access to the internet for pedagogical purposes, by education level (%)SE_ACS_SANIT: Schools with access to access to single-sex basic sanitation, by education level (%)SE_INF_DSBL: Proportion of schools with access to adapted infrastructure and materials for students with disabilities, by education level (%)Target 4.b: By 2020, substantially expand globally the number of scholarships available to developing countries, in particular least developed countries, small island developing States and African countries, for enrolment in higher education, including vocational training and information and communications technology, technical, engineering and scientific programmes, in developed countries and other developing countriesIndicator 4.b.1: Volume of official development assistance flows for scholarships by sector and type of studyDC_TOF_SCHIPSL: Total official flows for scholarships, by recipient countries (millions of constant 2018 United States dollars)Target 4.c: By 2030, substantially increase the supply of qualified teachers, including through international cooperation for teacher training in developing countries, especially least developed countries and small island developing StatesIndicator 4.c.1: Proportion of teachers with the minimum required qualifications, by education leveliSE_TRA_GRDL: Proportion of teachers who have received at least the minimum organized teacher training (e.g. pedagogical training) pre-service or in-service required for teaching at the relevant level in a given country, by sex and education level (%)

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

Snapshot img { margin: 10px !important; } AI In Geospatial Technology Market Size 2025-2029

The ai in geospatial technology market size is forecast to increase by USD 87.2 billion, at a CAGR of 25.3% between 2024 and 2029.

The global AI in geospatial technology market is expanding, driven by the exponential proliferation of geospatial data sources. This surge in data from satellites, drones, and sensors creates a compelling need for AI-driven solutions capable of processing and interpreting vast information streams. A significant development shaping the industry is the rise of geospatial foundation models and generative AI, which are democratizing advanced analytics through more intuitive, conversational interfaces. These advancements in ai in smart cities and geospatial analytics are enabling the development of sophisticated applications, including ai in simulation for urban planning and environmental modeling. However, the inherent complexity and quality issues of this data present considerable integration challenges that can slow adoption.The growth in AI in infrastructure and platforms as a service is pivotal, as it provides the scalable computing power necessary for these advanced applications. The increasing sophistication of autonomous AI is also a key factor, particularly in areas like remote sensing and dynamic monitoring. These capabilities are crucial for the artificial intelligence (AI) in IoT market, where real-time spatial intelligence is essential. Despite these advancements, the creation of high-quality, accurately labeled training data remains a significant bottleneck. This scarcity of reliable training material can hinder the performance of AI models, posing a persistent challenge to realizing the full potential of GeoAI solutions across various sectors, including the artificial intelligence (AI) in military market.

What will be the Size of the AI In Geospatial Technology Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market's evolution is shaped by the interplay between data proliferation and analytical sophistication, where advanced AI models for object detection and feature extraction are becoming essential. The integration of generative AI is redefining user interaction, enabling conversational GIS and making complex spatial analysis more accessible. This shift is particularly relevant for agentic AI in digital engineering, where natural language interfaces can streamline design and simulation workflows. However, progress is tempered by the ongoing need for high-quality ground truth data creation and robust data harmonization processes to ensure model accuracy and reliability.The development of geospatial foundation models signifies a move toward more versatile and scalable solutions, reducing the reliance on task-specific model training. This trend supports ai in learning and development by allowing for rapid fine-tuning for diverse applications, from environmental monitoring to infrastructure management. The utility of these models in ai in simulation is growing, as they can generate synthetic data and model future-state scenarios with greater fidelity. Progress in this area is closely tied to advancements in AI accelerators and cloud-based platform-as-a-service models that provide the necessary computational power.

How is this AI In Geospatial Technology Industry segmented?

The ai in geospatial technology industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. DeploymentCloud-basedOn-premisesEnd-userGovernment and defenseTransportation and logisticsNatural resourcesUtilitiesOthersTechnologyMachine learningComputer visionDeep learningNatural language processingGeographyNorth AmericaUSCanadaAPACChinaIndiaJapanSouth KoreaAustraliaEuropeGermanyUKFranceMiddle East and AfricaUAESouth AmericaBrazilArgentinaRest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.The cloud-based deployment model is the dominant and fastest-growing segment, driven by its scalability, cost-efficiency, and accessibility to high-performance computing. Organizations are increasingly migrating geospatial workflows to the cloud to manage the petabyte-scale datasets generated by modern remote sensing technologies. Cloud platforms offer an elastic environment for processing this data, a task often infeasible for on-premises systems. This model is democratizing access to sophisticated GeoAI capabilities, enabling organizations of all sizes to derive insights without extensive in-house resources.Leading public cloud providers are at the forefront of this trend, conti

A webpage intended to highlight the RUBA program and how to connect with its resources. This includes introducing to the Local Government Specialists (LGSs) at DCRA and which LGS services which communities, and an overview of different RUBA programs, grants, publications and trainings. Includes embeds or links to the following:LGS Headshots and Bios: LGS Headshots and Bios - Overview (arcgis.com)DCRA Local Government Assistance App: DCRA Local Government Assistance / RUBA Program (arcgis.com)RUBA Utility Management Training Courses Storymap: RUBA Utility Management Training Courses (arcgis.com)RUBA Publications Storymap: RUBA Publications (arcgis.com)RUBA Grant Report Summary Storymap: RUBA Grant Report Summary (arcgis.com)Best Practices Storymap: Best Practices (arcgis.com)

This map presents the full data available on the MLTSD GeoHub, and maps several of the key variables reflected by the Employment Services Program of ETD.Employment Services are a suite of services delivered to the public to help Ontarians find sustainable employment. The services are delivered by third-party service providers at service delivery sites (SDS) across Ontario on behalf of the Ministry of Labour, Training and Skills Development (MLTSD). The services are tailored to meet the individual needs of each client and can be provided one-on-one or in a group format. Employment Services fall into two broad categories: unassisted and assisted services.

Unassisted services include the following components:resources and information on all aspects of employment including detailed facts on the local labour marketresources on how to conduct a job search.assistance in registering for additional schoolinghelp with career planningreference to other Employment and government programs.

Unassisted services are available to all Ontarians without reference to eligibility criteria. These unassisted services can be delivered through structured orientation or information sessions (on or off site), e-learning sessions, or one-to-one sessions up to two days in duration. Employers can also use unassisted services to access information on post-employment opportunities and supports available for recruitment and workplace training.

The second category is assisted services, and it includes the following components:assistance with the job search (including individualized assistance in career goal setting, skills assessment, and interview preparation) job matching, placement and incentives (which match client skills and interested with employment opportunities, and include placement into employment, on-the-job training opportunities, and incentives to employers to hire ES clients), and job training/retention (which supports longer-term attachment to or advancement in the labour market or completion of training)For every assisted services client a service plan is maintained by the service provider, which gives details on the types of assisted services the client has accessed. To be eligible for assisted services, clients must be unemployed (defined as working less than twenty hours a week) and not participating in full-time education or training. Clients are also assessed on a number of suitability indicators covering economic, social and other barriers to employment, and service providers are to prioritize serving those clients with multiple suitability indicators.

About This Dataset

This dataset contains data on ES clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). This includes all assisted services clients whose service plan was closed in the 2015/16 fiscal year and all unassisted services clients who accessed unassisted services in the 2015/16 fiscal year. These clients have been distributed across Local Board areas based on the address of each client’s service delivery site, not the client’s home address. Note that clients who had multiple service plans close in the 2015/16 fiscal year (i.e. more than one distinct period during which the client was accessing assisted services) will be counted multiple times in this dataset (once for each closed service plan). Assisted services clients who also accessed unassisted services either before or after accessing assisted services would also be included in the count of unassisted clients (in addition to their assisted services data).

Demographic data on ES assisted services clients, including a client’s suitability indicators and barriers to employment, are collected by the service provider when a client registers for ES (i.e. at intake). Outcomes data on ES assisted services clients is collected through surveys at exit (i.e. when the client has completed accessing ES services and the client’s service plan is closed) and at three, six, and twelve months after exit. As demographic and outcomes data is only collected for assisted services clients, all fields in this dataset contain data only on assisted services clients except for the ‘Number of Clients – Unassisted R&I Clients’ field.

Note that ES is the gateway for other Employment Ontario programs and services; the majority of Second Career (SC) clients, some apprentices, and some Literacy and Basic Skills (LBS) clients have also accessed ES. It is standard procedure for SC, LBS and apprenticeship client and outcome data to be entered as ES data if the program is part of ES service plan. However, for this dataset, SC client and outcomes data has been separated from ES, which as a result lowers the client and outcome counts for ES.

About Local Boards

Local Boards are independent not-for-profit corporations sponsored by the Ministry of Labour, Training and Skills Development to improve the condition of the labour market in their specified region. These organizations are led by business and labour representatives, and include representation from constituencies including educators, trainers, women, Francophones, persons with disabilities, visible minorities, youth, Indigenous community members, and others. For the 2015/16 fiscal year there were twenty-six Local Boards, which collectively covered all of the province of Ontario.

The primary role of Local Boards is to help improve the conditions of their local labour market by:engaging communities in a locally-driven process to identify and respond to the key trends, opportunities and priorities that prevail in their local labour markets;facilitating a local planning process where community organizations and institutions agree to initiate and/or implement joint actions to address local labour market issues of common interest; creating opportunities for partnership development activities and projects that respond to more complex and/or pressing local labour market challenges; and organizing events and undertaking activities that promote the importance of education, training and skills upgrading to youth, parents, employers, employed and unemployed workers, and the public in general.

In December 2015, the government of Ontario launched an eighteen-month Local Employment Planning Council pilot program, which established LEPCs in eight regions in the province formerly covered by Local Boards. LEPCs expand on the activities of existing Local Boards, leveraging additional resources and a stronger, more integrated approach to local planning and workforce development to fund community-based projects that support innovative approaches to local labour market issues, provide more accurate and detailed labour market information, and develop detailed knowledge of local service delivery beyond Employment Ontario (EO).

Eight existing Local Boards were awarded LEPC contracts that were effective as of January 1st, 2016. As such, from January 1st, 2016 to March 31st, 2016, these eight Local Boards were simultaneously Local Employment Planning Councils. The eight Local Boards awarded contracts were:Durham Workforce Authority Peel-Halton Workforce Development GroupWorkforce Development Board - Peterborough, Kawartha Lakes, Northumberland, HaliburtonOttawa Integrated Local Labour Market PlanningFar Northeast Training BoardNorth Superior Workforce Planning Board Elgin Middlesex Oxford Workforce Planning & Development BoardWorkforce Windsor-Essex

MLTSD has provided Local Boards and LEPCs with demographic and outcome data for clients of Employment Ontario (EO) programs delivered by service providers across the province on an annual basis since June 2013. This was done to assist Local Boards in understanding local labour market conditions. These datasets may be used to facilitate and inform evidence-based discussions about local service issues – gaps, overlaps and under-served populations - with EO service providers and other organizations as appropriate to the local context.

Data on the following EO programs for the 2015/16 fiscal year was made available to Local Boards and LEPCs in June 2016:Employment Services (ES)Literacy and Basic Skills (LBS) Second Career (SC) Apprenticeship

This dataset contains the 2015/16 ES data that was sent to Local Boards and LEPCs. Datasets covering past fiscal years will be released in the future.

Notes and Definitions

NAICS – The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, the United States, and Mexico against the backdrop of the North American Free Trade Agreement. It is a comprehensive system that encompasses all economic activities in a hierarchical structure. At the highest level, it divides economic activity into twenty sectors, each of which has a unique two-digit identifier. These sectors are further divided into subsectors (three-digit codes), industry groups (four-digit codes), and industries (five-digit codes). This dataset uses two-digit NAICS codes from the 2007 edition to identify the sector of the economy an Employment Services client is employed in prior to and after participation in ES.

NOC – The National Organizational Classification (NOC) is an occupational classification system developed by Statistics Canada and Human Resources and Skills Development Canada to provide a standard lexicon to describe and group occupations in Canada primarily on the basis of the work being performed in the occupation. It is a comprehensive system that encompasses all occupations in Canada in a hierarchical structure. At the highest level are ten broad occupational categories, each of which has a unique one-digit identifier. These broad occupational categories are further divided into forty major groups (two-digit codes), 140 minor groups

Professional Development Section training bulletin manual focuses on community relations, legal issues, patrol procedures and officer safety.

ESPA Irrigated Lands 2019 was created for use in water budget studies in the ESPA study boundary. The area of interest was determined by Hydrology Section staff at IDWR, and a study boundary was given to GIS staff and used to clip the model output. The random forest (RF) model is a type of supervised machine learning algorithm requiring GIS staff to provide manually labeled training data. GIS staff also provide the RF model with several input features, typically raster datasets that help distinguish characteristics of irrigated lands. ESPA Irrigated Lands 2019 used the following as input features: • Landsat 8 [2] and Sentinel-2 [3] surface reflectance imagery (bands: SWIR 2, NIR, Blue, and calculated NDVI)• 10-meter digital elevation model 4• PRISM Climate Dataset 5• Height Above Nearest Drainage (HAND) [6]• IDWR METRIC [7] evapotranspiration dataset• Topographic Wetness Index, derived from the digital elevation modelFor additional information on processing Landsat and Sentinel-2 imagery, please see below. Additional datasets used only for labeling training data include Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), IDWR-provided Active Water Rights Place of Use, the Cropland Data Layer [8] for 2019, and the National Agriculture Imagery Program (NAIP) imagery [9] for Idaho 2019.The accuracy of the ESPA Irrigated Lands 2019 dataset was verified by several methods. Firstly, a validation test was conducted by withholding a subset of the training data to evaluate how well the model classified unseen information. Second, GIS staff ran several iterations of the model with variations of training data, with the goal of improving classification for areas consistently misclassified. This process requires GIS staff knowledge, aided by supplementary datasets, to review the area and make decisions. Once a model iteration is determined as ‘final’, a manual mask was created to correct any remaining misclassification in the dataset. Manual corrections for the ESPA Irrigated Lands 2019 dataset were focused on the area between Ashton and Lamont, where false positive labels of “irrigated” occurred on dryland-managed fields. Some areas classified as irrigated near Bellevue were masked out due to suspected wetland. A general wetland mask for the entire ESPA study boundary was also applied. Other manual corrections were made throughout the study area, specifically for pivot-irrigated fields not matching the NAIP field boundaries. Decisions made during manual masking were conservative, relying heavily on both the presence of an active water right and clear indications of artificial application of water as observed in satellite imagery.References:[1] https://developers.google.com/earth-engine/apidocs/ee-classifier-smilerandomforest[2] https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002[3] https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSS30_v002[4] https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m[5] Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J. & Pasteris, P.A. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28, 2031-2064. doi:10.1002/joc.1688[6] Donchyts, G., Winsemius, H., Schellekens, J., Erickson, T., Gao, H., Savenije, H., & van de Giesen, N. (2016). Global 30m height above the nearest drainage (HAND). Geophysical Research Abstracts, 18, EGU2016-17445-3. EGU General Assembly 2016.[7] https://data-idwr.hub.arcgis.com/documents/365d91be4da4407bbe3df11f242b34c7/about[8] https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL[9] https://developers.google.com/earth-engine/datasets/catalog/USDA_NAIP_DOQQ

Mountain Home Irrigated Lands 1987 was created for use in water budget studies in Mountain Home. The area of interest was determined by Hydrology Section staff at IDWR, and a study boundary was given to GIS staff and used to clip the model output. The random forest (RF) model is a type of supervised machine learning algorithm requiring GIS staff to provide manually labeled training data. GIS staff also provide the RF model with several input features, typically raster datasets that help distinguish characteristics of irrigated lands. Mountain Home Irrigated Lands 1987 used the following as input features: • Interpolated Landsat 5 [2] surface reflectance imagery (bands: SWIR 2, NIR, Blue, and calculated NDVI)• 10-meter digital elevation model 3• Height Above Nearest Drainage (HAND) [4]For additional information on the interpolation process for Landsat imagery, please see below. Additional datasets used only for labeling training data include IDWR-provided Active Water Rights Place of Use for 1987.The accuracy of Mountain Home Irrigated Lands 1987 dataset was verified by several methods. First, a validation test is done by withholding a subset of the training data to evaluate how well the model classifies unseen information. Second, GIS staff will run several iterations of the model with variations of training data, with the goal of improving classification for areas consistently misclassified. This process requires GIS staff knowledge, aided by supplementary datasets, to review the area and make decisions. Once a model iteration is determined as ‘final’, a manual mask is created to correct any remaining misclassification in the dataset. Consistent areas of misclassification for Mountain Home Irrigated Lands 1987 include the region south of Mountain Home between CJ Strike Reservoir and the city, pastures and roads within the Morley Nelson Birds of Prey Conservation Area, and wetlands that originate in the Boise Mountain’s foothills and run into the SRP. Misclassification largely occurred in areas with active water rights, but no visible irrigation in the satellite imagery. Final decisions made for these areas were conservative, relying heavily on both an active water right and clear indication of diverted water and artificial application of water to a given field.References:[1] https://developers.google.com/earth-engine/apidocs/ee-classifier-smilerandomforest[2] https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC05_C02_T1_L2[3] https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m[4] Donchyts, G., Winsemius, H., Schellekens, J., Erickson, T., Gao, H., Savenije, H., & van de Giesen, N. (2016). Global 30m height above the nearest drainage (HAND). Geophysical Research Abstracts, 18, EGU2016-17445-3. EGU General Assembly 2016.

Eastern Snake River Plain Irrigated Lands 2018 was created for use in water budget studies in the Eastern Snake River Plain. The area of interest was determined by Hydrology Section staff at IDWR, and a study boundary was given to GIS staff and used to clip the model output. The random forest (RF) model is a type of supervised machine learning algorithm requiring GIS staff to provide manually labeled training data. GIS staff also provide the RF model with several input features, typically raster datasets that help distinguish characteristics of irrigated lands. Eastern Snake River Plain Irrigated Lands 2018 used the following as input features: • Seasonally averaged Landsat 8 [2] and Sentinel-2 surface reflectance imagery 3• 10-meter digital elevation model 4• PRISM [5] 800 meter seasonal averaged climate data• IDWR METRIC [7] evapotranspiration dataset• Height Above Nearest Drainage (HAND) [6] • Topographic Wetness Index, derived from the digital elevation modelFor additional information on the averaging process for Landsat and Sentinel-2 imagery, please see below. Additional datasets used only for labeling training data include Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) [7], IDWR-provided Active Water Rights Place of Use, and the Cropland Data Layer [8] for 2018.The accuracy of the Eastern Snake River Plain Irrigated Lands 2018 dataset was verified by several methods. Firstly, a validation test is done by withholding a subset of the training data to evaluate how well the model classifies unseen information. Second, GIS staff will run several iterations of the model with variations of training data, with the goal of improving classification for areas consistently misclassified. This process requires GIS staff knowledge, aided by supplementary datasets, to review the area and make decisions. Once a model iteration is determined as ‘final’, a manual mask is created to correct any remaining misclassification in the dataset. Consistent areas of misclassification for Eastern Snake River Plain Irrigated Lands 2018 include the area between Ashton and Lamont, pastures within Chester, dry and fallow fields in southern Twin Falls county, and separation between wetlands and irrigated fields in Wood River and the Lost River Valleys. Misclassification largely occurred in areas with active water rights, but no visible irrigation or irrigation infrastructure in the satellite and aerial imagery available. Final decisions made for these areas were conservative, relying heavily on both an active water right and clear indication of diverted pressurized water and purposeful application of water to a given field.References:[1] https://developers.google.com/earth-engine/apidocs/ee-classifier-smilerandomforest[2] https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002[3] https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSS30_v002[4] https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m[5] Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J. & Pasteris, P.A. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28, 2031-2064. doi:10.1002/joc.1688[6] Donchyts, G., Winsemius, H., Schellekens, J., Erickson, T., Gao, H., Savenije, H., & van de Giesen, N. (2016). Global 30m height above the nearest drainage (HAND). Geophysical Research Abstracts, 18, EGU2016-17445-3. EGU General Assembly 2016.[7] https://data-idwr.hub.arcgis.com/documents/776cfc545e0944fc89a75d4777031bb4/about[8] https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDLInformation on averaged imagery:GIS staff average Landsat and Sentinel-2 imagery in two month increments to fill gaps of missing data. Images are averaged using the HLSS and HLSL datasets for March 1st through May 1st, May 1st through July 1st, July 1st through September 1st, and September 1st through November 1st. Averaging results in 4 total images that are entirely spectral data.

The Commonwealth of Kentucky through the Commonwealth Office of Technology's Division of Geographic Information (DGI) in conjunction with the Kentucky GIS Community has made available a wealth of GIS-related information, data sets and maps. These resources support education and training, research, and policy development for a multitude of organizations in Kentucky and across the United States.The Commonwealth Map is a statewide digital basemap available via the Internet for interactive mapping, geographic data querying, and downloading. As a collaborative effort of local, state, and federal partners, this initiative is designed to facilitate public, non-profit, and private sector GIS development, utilization, innovation, and data sharing.This web map also includes a great set of bookmarks prepared by the Kentucky Geography Network.Kentucky Division of Geographic Information: https://gis.ky.gov/Kentucky Geography Network: https://kygeonet.ky.govYou can access the Kentucky Commonwealth Map viewer here: https://kygeonet.ky.gov/tcm/ArcMap users can also access a ready to use map document (MXD file) for Kentucky that references this service. Click to launch. Requires ArcGIS 9.3 or more recent: MXD. This map document also includes the bookmarks prepared by the Kentucky Geography Network.More details about the Commonwealth Map of Kentucky map service used in this web map can be found here.

Data: "Focus Group Guide (Employees)", "Focus Group Guide (Leaders)", and Data Summary.

Task 1.3: Employee Learning and Development Training Programs and Current Practices

Task 1.4: Literature Review

Task 2.1: Measures, Data Collection Tools, and Protocols

Task 2.2: Test and Validate Measures – Pilot

Task 3.1: Data Coding, Cleaning, and Validation Procedures

Task 3.2: Data Analysis

Final Report: Measuring and Improving the Effectiveness of ADOT’s Employee Learning and Development Program

Compendium of Research: Measuring and Improving the Effectiveness of ADOT’s Employee Learning and Development Program

To maximize the benefits that the agency and employees gain from learning and development activities and to ensure a supportive organizational culture, ADOT undertook a research study to explore employee perceptions of the training opportunities offered at ADOT and the metrics that can be used to measure the effectiveness of the EBD training courses and programs. Tina Samartinean, ADOT's EBD administrator and the project champion, wanted a framework to incorporate best practices for adult learning and a strategy for continual improvement of the agency’s learning and development programs.

Task 1.3: Employee Learning and Development Training Programs and Current Practices

Task 1.4: Literature Review

Task 2.1: Measures, Data Collection Tools, and Protocols

Task 2.2: Test and Validate Measures – Pilot

Task 3.1: Data Coding, Cleaning, and Validation Procedures

Task 3.2: Data Analysis

Data Summary

Final Report: Measuring and Improving the Effectiveness of ADOT’s Employee Learning and Development Program

Compendium

Task 1.4: Literature Review

Task 1.3: Employee Learning and Development Training Programs and Current Practices

Task 2.1: Measures, Data Collection Tools, and Protocols

Task 2.2: Test and Validate Measures – Pilot

Task 3.1: Data Coding, Cleaning, and Validation Procedures

Task 3.2: Data Analysis

Final Report: Measuring and Improving the Effectiveness of ADOT’s Employee Learning and Development Program

Data Summary

Compendium of Research: Measuring and Improving the Effectiveness of ADOT’s Employee Learning and Development Program

Title 12 of the Local Government Code, Section 378.002 requires that the creation of the City of Dallas Neighborhood Empowerment Zones. City of Dallas Neighborhood Empowerment Zones promote an increase in economic development in the zones by promoting increased business and commercial activity, job retention and job growth by smaller businesses, increased occupancy of existing building space, reinvestment in existing building stock, and workforce development job training programs. Details about the data can be requested from Kevin Spath. Polygon features created by Ridvan Kirimli - ridvan.kirimli@dallascityhall.com. Backup if Ridvan is not available contact Kevin Spath - kevin.spath@dallascityhall.com.

Suryamitra Programme To create skilled manpower in the field of solar energy particularly in view of huge demand of trained persons to install, operate & maintain the SPV system under the National Solar Mission, Ministry launched Suryamitra Skill Development Programme in 2015 and assigned the task of coordination of the trainings to National Institute of Solar Energy (NISE) for creating skilled manpower for employment in Solar Power Projects with a target to develop 50,000 Suryamitras by 2019-2020 for the country. The programme follows the M/o Skill Development & Entrepreneurship norms. So far 31,092 no.of Suryamitras have been trained upto 31-13-2019. A number of 11,646 Suryamitras have been trained at 228 Training Centres (TCs) across different States during 2019-20 till 31-12-2019. The state-wise progress is shown in the map.The Suryamitra Skill Development Programme is designed with the objective to develop skilled and employable workforce (Suryamitras) catering to the needs of Solar PV industries. The duration of the Suryamitra Skill Development Programme is three months consisting of 600 hours including classroom training, lab practical, SPV plant exposure, On The Job Training (OJT), soft skills and entrepreneurship skills. Min. 10th Pass + ITI in Electrician/ Wireman/ Electronics Mechanic/Fitter/Sheet Metal. Each batch contains around 30 seats. At the end of the Suryamitra programme, the host institute facilitate the trainees for placement.This map layer is offered by Esri India, for ArcGIS Online subscribers without any manipulation in the source data. If you have any questions or comments, please let us know via content@esri.in.