Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from a workshop that was conducted on October 19-23, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from a workshop that was conducted on August 17-21, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

You have been assigned a new project, which you have researched, and you have identified the data that you need.The next step is to gather, organize, and potentially create the data that you need for your project analysis.In this course, you will learn how to gather and organize data using ArcGIS Pro. You will also create a file geodatabase where you will store the data that you import and create.After completing this course, you will be able to perform the following tasks:Create a geodatabase in ArcGIS Pro.Create feature classes in ArcGIS Pro by exporting and importing data.Create a new, empty feature class in ArcGIS Pro.

The Python language offers an efficient way to automate and extend geoprocessing and mapping functionality. In ArcGIS 10, Python was fully integrated into ArcGIS Desktop with the addition of the Python window and the ArcPy site package. This course introduces Python scripting within ArcGIS Desktop to automate geoprocessing workflows. These skills are needed by GIS analysts to work efficiently and productively with ArcGIS for Desktop.After completing this course, you will be able to:Create geoprocessing scripts using the ArcPy site package.Identify common scripting workflows.Write Python scripts that create and update data.Create a script tool using built-in validation.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from workshops that were conducted on February 19-21 and October 6-7, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

One important reason for performing GIS analysis is to determine proximity. Often, this type of analysis is done using vector data and possibly the Buffer or Near tools. In this course, you will learn how to calculate distance using raster datasets as inputs in order to assign cells a value based on distance to the nearest source (e.g., city, campground). You will also learn how to allocate cells to a particular source and to determine the compass direction from a cell in a raster to a source.What if you don't want to just measure the straight line from one place to another? What if you need to determine the best route to a destination, taking speed limits, slope, terrain, and road conditions into consideration? In cases like this, you could use the cost distance tools in order to assign a cost (such as time) to each raster cell based on factors like slope and speed limit. From these calculations, you could create a least-cost path from one place to another. Because these tools account for variables that could affect travel, they can help you determine that the shortest path may not always be the best path.After completing this course, you will be able to:Create straight-line distance, direction, and allocation surfaces.Determine when to use Euclidean and weighted distance tools.Perform a least-cost path analysis.

Materials created by James Baker in June 2014 for the 108 Mapping Data course of the British Library Digital Scholarship Training Programme.

Do you spend a lot of time repeating workflows, such as copying data, editing files, and setting up map documents? Did you know that you can use Python to automate data reproduction, data management, map document display, and many of your other daily tasks in ArcGIS?This course provides the building blocks needed to use Python. You will create and run scripts using these building blocks and can apply them directly inside of ArcGIS and to your own workflows.After completing this course, you will be able to:Determine where to write and run a Python script.Differentiate Python language elements and determine where to apply them.Follow a script workflow.Develop a Python script to run statements and functions.Solve common syntax errors.

ArcGIS has many analysis and geoprocessing tools that can help you solve real-world problems with your data. In some cases, you are able to run individual tools to complete an analysis. But sometimes you may require a more comprehensive way to create, share, and document your analysis workflow.In these situations, you can use a built-in application called ModelBuilder to create a workflow that you can reuse, modify, save, and share with others.In this course, you will learn the basics of working with ModelBuilder and creating models. Models contain many different elements, many of which you will learn about. You will also learn how to work with models that others create and share with you. Sharing models is one of the major advantages of working with ModelBuilder and models in general. You will learn how to prepare a model for sharing by setting various model parameters.After completing this course, you will be able to:Identify model elements and states.Describe a prebuilt model's processes and outputs.Create and document models for site selection and network analysis.Define model parameters and prepare a model for sharing.

Maps exist to convey information to people, whether that information is how to get from one point to another or how many oil fields are located in a given region. Effective cartography can convey that information efficiently to map users.In this course, you will be introduced to a five-step workflow for designing and creating maps. This workflow can be applied to any map or output medium (print or digital). This course will cover all steps of the workflow in general terms, emphasizing the first two steps: the cartographic planning process and data evaluation.After completing this course, you will be able to perform the following tasks:Identify and describe the cartographic workflow steps.Explain cartographic design controls and how they drive map creation.Apply the planning step of the cartographic workflow.Evaluate data sources to determine applicability.Discuss why basemap and operational layers are important.Assign the correct coordinate system to data based on the geographic extent and map objective.Assess the level of detail required for a map and apply generalization techniques when appropriate.

ArcGIS Technology for Mapping COVID-19 (Esri Training).Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic. This plan will teach you the core ArcGIS technology necessary to understand, prepare for, and respond to COVID-19 in your community or organization.More information about Esri training..._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

The informed consent request and workshop survey questions given to participants after the workshop each day for 4 consecutive days.

SONG, Carol X., Rosen Center for Advanced Computing, Purdue University, 155 South Grant Street, Young Hall, West Lafayette, IN 47907

Science gateways are becoming an integral component of modern collaborative research. They find widespread adoption by research groups to share data, code and tools both within a project and with the broader community. Sustainability beyond initial funding is a significant challenge for a science gateway to continue to operate, update and support the communities it serves. MyGeoHub.org is a geospatial science gateway powered by HUBzero. MyGeoHub employs a business model of hosting multiple research projects on a single HUBzero instance to manage the gateway operations more efficiently and sustainably while lowering the cost to individual projects. This model allows projects to share the gateway’s common capabilities and the underlying hardware and other connected computing resources, and continued maintenance of their sites even after the original funding has run out allowing time for acquiring new funding. MyGeoHub has hosted a number of projects, ranging from hydrologic modeling and data sharing, plant phenotyping, global and local sustainable development, climate variability impact on crops, and most recently, modeling of industry processes to improve reuse and recycling of materials. The shared need to manage, visualize and process geospatial data across the projects has motivated the Geospatial Data Building Blocks (GABBs) development funded by NSF DIBBs. GABBs provides a “File Explorer” type user interface for managing geospatial data (no coding is needed), a builder for visualizing and exploring geo-referenced data without coding, a Python map library and other toolkits for building geospatial analysis and computational tools without requiring GIS programming expertise. GABBs can be added to an existing or new HUBzero site, as is the case on MyGeoHub. Teams use MyGeoHub to coordinate project activities, share files and information, publish tools and datasets (with DOI) to provide not only easy access but also improved reuse and reproducibility of data and code as the interactive online tools and workflows can be used without downloading or installing software. Tools on MyGeoHub have also been used in courses, training workshops and summer camps. MyGeoHub is supporting more than 8000 users annually.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from a workshop that was conducted on February 26-28, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

This document shows just the questions we asked the applicants who applied to participate in this Georeferencing for Research Use workshop. We used a Google Form to deliver these questions and collect responses. It is both an application and serves as our pre-workshop survey.

What do you need to do with your GIS data? Do you need to create earthquake hazard maps, find a location for your new business, or locate municipal utility lines? Perhaps you need to integrate your organization's data into a single system that will streamline resource management.At the core of all these projects lies the need to represent and store data in a way that supports meaningful, accurate analysis and organizational workflows. The geodatabase is the native data storage format for ArcGIS. It offers many advantages for modeling, analyzing, managing, and maintaining GIS data.With a geodatabase, you can create GIS features that mimic real-world feature behavior, apply sophisticated rules and relationships between features, and access all of your data from a centralized location. This course introduces the basic components of the geodatabase that will allow you to begin organizing your data to meet your GIS project needs.After completing this course, you will be able to:Describe the components of the geodatabase.Create geodatabase schema.Design and create a geodatabase.

Quivira National Wildlife Refuge was established in 1955, and a detailed vegetation map was not available for management purposes. With the present development of a biological program and Comprehensive Conservation Plan (CCP), a baseline vegetation map of the refuge was identified as a necessity. Development of the vegetation map and associated report was a multi-step process. Aerial photography (NAIP, 2008) was used with eCognition to create polygons of different plant communities based on the likeness of surrounding pixels in the area. Prior to ground-truthing, the following activities were accomplished: training on vegetation mapping using GIS (previous experience and National Conservation Training Center course), creation of an vegetation association and alliance dichotomous key, development of a refuge plant key and identification skills, and preparation of maps for ground truthing. Once out in the field dominant plants were identified for appropriate vegetation alliance and association classification, plant specimens were collected for the refuge herbarium as necessary and additional observations and photos were gathered for the report. Over the course of the project, classification data was entered into a GIS and polygons were appropriately modified to create the final map. At Quivira, results found a total of 42 alliances and 43 associations.The most dominant plants throughout the refuge in 2008 based on canopy cover were saltgrass, plum, little bluestem and cottonwood. The number of alliances and associations found on the refuge show high species diversity.

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into building and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Building is useful in applications such as high-quality 3D basemap creation, urban planning, and planning climate change response.Building could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Building in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.The model is trained with classified LiDAR that follows the The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 6 BuildingApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Auckland, Christchurch, Kapiti, Wellington Testing dataset - Auckland, WellingtonValidation/Evaluation dataset - Hutt City Dataset City Training Auckland, Christchurch, Kapiti, Wellington Testing Auckland, Wellington Validating HuttModel architectureThis model uses the SemanticQueryNetwork model architecture implemented in ArcGIS Pro.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.984921 0.975853 0.979762 Building 0.951285 0.967563 0.9584Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 75~%, Test: 25~%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-137.74 m to 410.50 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-17 to +17 Maximum points per block8192 Block Size50 Meters Class structure[0, 6]Sample resultsModel to classify a dataset with 23pts/m density Wellington city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story