In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

This dataset holds all materials for the Inform E-learning GIS course

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.

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

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.

Seattle Parks and Recreation Golf Course locations. SPR Golf Courses are managed by contractors.Refresh Cycle: WeeklyFeature Class: DPR.GolfCourse

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

This course demonstrates how to select, modify, create, and share web applications using ArcGIS Online. ArcGIS Online offers many different options for creating web applications that share web maps, web scenes, and spatial functions. But how do you decide which web application best meets your requirements? Each web application option implements different functions and showcases a specific look and feel. You can choose a web application that meets your organization's functional requirements, apply your organization's look and feel, and share your web map without writing any code.Two workflows will be introduced for creating web applications using ArcGIS Online:Applying your web map to an existing template applicationCreating your own web application using Web AppBuilder for ArcGISAfter completing this course, you will be able to do the following:Identify the components of a web application.Create a web application from an existing configurable app template.Create a web application using Web AppBuilder for ArcGIS.Use ArcGIS Online to deploy a web application.

The goal of the Lead Safe Certificate program is to prevent lead poisoning by ensuring that all rental homes built prior to 1978 are compliant with the city's Lead Safe Ordinance and maintained free of lead hazards. Any home built before 1978 is reasonably presumed to contain lead-based paint. Residential rental units built before 1978 must have a Lead Safe Certification from the City of Cleveland’s Department of Building and Housing. The Lead Safe Certification is only valid for two years, after which rental property owners must re-apply for certification. For more information about the City's Lead Safe Certification program, please visit this Building & Housing page. RelatedLead Safe Certificate Explorer Data GlossarySee the Attributes section below for details about each column in this dataset.ContactCity of Cleveland, Building and Housing Lead Compliance Program Update FrequencyWeekly on Sundays at 7 AM EST (6 AM during daylight savings)

ArcGIS Living Atlas of the World is a rich and growing collection of valuable geographic maps and data from organizations around the globe. Access to Living Atlas content is part of your ArcGIS Online organizational subscription. In this course, you will discover and use Living Atlas maps and layers that are ready to use for instruction. You will explore ways to connect Living Atlas content to the subjects that you teach.

A public feature layer view used to share natural spaces set aside for recreation or the protection of wildlife or natural habitats.

This dataset is OBSOLETE as of 11/18/2024 and will be removed from ArcGIS Online on 11/18/2025.An updated version of this dataset is available at Certified Sustainable Buildings | Open Data Portal | City of Cambridge.A map of the updated data can be found in two places:Certified Sustainable Buildings Map | Open Data Portal | City of CambridgeSustainable Buildings Map - City of Cambridge, MAThis point layer shows the location of sustainable buildings in Cambridge. For inclusion in this layer, a building must do at least one of the following: qualify for the City’s Article 22 regulatory process; be certified by Passive House; be certified by Enterprise Green Communities; or be certified by LEEDunder a LEED version that requires the whole building to meet sustainability standards. Some buildings meet two or more of these criteria. Additionally, this layer contains information about other certifications (Energy Star, Fitwel, and WELL) that may apply to the included buildings. If an included building participates in the City’s BEUDO regulatory process, this layer provides two key emissions figures for the building. Information provided about the applicable sustainable building programs for qualifying buildings includes certification levels, certification types, ratings, or scores. This layer includes data from City and non-City sources.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription BldgID type: Stringwidth: 50precision: 0 Unique ID for database from GIS.

Latitude type: Doublewidth: 8precision: 38 Geographic coordinate from GIS Bldg ID centroid file.

Longitude type: Doublewidth: 8precision: 38 Geographic coordinate from GIS Bldg ID centroid file.

Article22_SystemLevelEquivalenc type: Stringwidth: 150precision: 0

Article22 type: Stringwidth: 3precision: 0 "Yes" indicates Article 22 building.

BEUDO_TotalGHGEmissionsIntensit type: Doublewidth: 8precision: 38

BEUDO type: Stringwidth: 3precision: 0 "Yes" indicates BUEDO building.

BEUDO_SourceEUI type: Doublewidth: 8precision: 38 A critical variable for reporting about BEUDO.

EnergyStar type: Stringwidth: 3precision: 0 "Yes" indicates EnergyStar building.

EnergyStar_CountYearsCert type: SmallIntegerwidth: 2precision: 5 Number of years certified. EnergyStar certification may be renewed annually.

EnergyStar_LastYearCert type: Stringwidth: 4precision: 0 Year of last certification.

EnergyStar_LastCertScore type: SmallIntegerwidth: 2precision: 5 Most recent EnergyStar score.

EnterpriseGC type: Stringwidth: 3precision: 0 "Yes" indicates Enterprise Green Communities building.

EnterpriseGC_CertTemplate type: Stringwidth: 100precision: 0 Certification version.

EnterpriseGC_PointsAchieved type: SmallIntegerwidth: 2precision: 5 Enterprise Green Communities score.

Fitwel type: Stringwidth: 3precision: 0 "Yes" indicates Fitwel building.

Fitwel_StarRating type: SmallIntegerwidth: 2precision: 5 Numerical Fitwel rating.

LEED type: Stringwidth: 3precision: 0 "Yes" indicates LEED building.

LEED_TotalCerts type: SmallIntegerwidth: 2precision: 5 Number of certifications applying to the whole building. The LEED fields contain details about certifications that are "whole-building," not referring to one part of the building only or or to building operations.

LEED_LastCertDate type: Datewidth: 8precision: 0 Date of last certification applying to the whole building.

LEED_LastSystemVersion type: Stringwidth: 100precision: 0 Certification version and rating system.

LEED_LastCertLevel type: Stringwidth: 50precision: 0 LEED certifictation level at which whole building is certified. Certified/Silver/Gold/Platinum: Does not not include "registered" buildings.

PassiveHouse type: Stringwidth: 3precision: 0 "Yes" indicates Passive House building.

PassiveHouse_CertVersion type: Stringwidth: 100precision: 0 Certification version.

WELL type: Stringwidth: 3precision: 0 "Yes" indicates WELL building.

WELL_Version type: Stringwidth: 50precision: 0 Certification version.

WELL_ProjectType type: Stringwidth: 150precision: 0 WELL project type.

WELL_CertLevel type: Stringwidth: 50precision: 0 Certification level. Certified Pilot/Compliance/Bronze/Silver/Gold/Platinum or Health-Safety Rated: Does not include "registered" or "precertified" buildings.

created_date type: Datewidth: 8precision: 0

last_edited_date type: Datewidth: 8precision: 0

We are sharing all urban big data we have for the old city of Beijing (around 62 sqkm in area). The inventory and GIS layers are as follows.To access these data, please join our online MOOC course BIG DATA AND URBAN PLANNING and they are available for downloading when you have registered the course in the below link.URL: http://www.xuetangx.com/courses/course-v1:TsinghuaX+70000662+2019_T1/aboutI would suggest you cite the following papers as a courtesy for using our data.Long Y. Redefining Chinese city system with emerging new data[J]. Applied Geography, 2016, 75: 36-48.

Conservation Biology Field Courses Market Outlook

According to our latest research, the global conservation biology field courses market size in 2024 stands at USD 1.42 billion, reflecting the expanding emphasis on environmental education and field-based learning worldwide. The market is experiencing a robust growth trajectory, with a compound annual growth rate (CAGR) of 7.3% projected from 2025 to 2033. By the end of 2033, the market is expected to reach USD 2.68 billion. This notable growth is primarily driven by increasing demand for experiential learning, the critical need for biodiversity conservation, and the integration of technology in field education.

One of the primary growth factors for the conservation biology field courses market is the rising global awareness about biodiversity loss and climate change. As environmental challenges become more complex and urgent, educational institutions, NGOs, and governmental agencies are prioritizing hands-on learning experiences that equip participants with practical conservation skills. This shift toward field-based education is further supported by international frameworks such as the United Nations’ Sustainable Development Goals (SDGs), which emphasize the importance of education in achieving environmental sustainability. Consequently, both undergraduate and graduate programs are increasingly incorporating field courses into their curricula, resulting in heightened enrollment rates and expanding market opportunities.

Another significant driver is the evolution of pedagogical approaches in conservation science. There is a growing recognition that classroom-based theoretical instruction alone is insufficient to address real-world conservation challenges. Field courses provide immersive experiences that foster critical thinking, problem-solving, and collaboration among participants. This educational transformation is not limited to universities; professional development programs and short-term workshops are also gaining traction among early-career scientists, conservation practitioners, and policy makers. The adoption of hybrid and online delivery modes has further democratized access, enabling participants from remote or underserved regions to engage in high-quality field-based learning.

Technological advancements also play a pivotal role in shaping the conservation biology field courses market. The integration of digital tools such as GIS mapping, remote sensing, and mobile data collection platforms has revolutionized fieldwork, making it more efficient and data-driven. These innovations enhance the learning experience, allowing students and professionals to analyze complex ecological data in real time and contribute meaningfully to ongoing conservation projects. Moreover, partnerships between academic institutions, research organizations, and technology providers are fostering the development of cutting-edge curricula that address current and emerging conservation issues, further fueling market growth.

From a regional perspective, North America and Europe currently dominate the conservation biology field courses market, accounting for over 60% of the global market share in 2024. These regions benefit from well-established educational infrastructures, strong funding support, and a mature ecosystem of conservation organizations. However, the Asia Pacific region is emerging as a significant growth engine, driven by rapid biodiversity loss, increasing governmental investment in environmental education, and the expansion of international collaborations. Latin America and the Middle East & Africa are also witnessing rising interest, particularly in areas with high conservation value and pressing ecological challenges. This regional diversity presents unique opportunities for market players to tailor their offerings to local needs and contexts.

Course Type Analysis

The course type segment in the conservation biology field courses market is broadly categorized into undergraduate, graduate, professional development, and short-te

Bucknell's summer 2015 "New Orleans in 12 Movements" course aims to help students view New Orleans' natural environment, built infrastructure, and human experience in an integrated way. The course is co-taught by faculty from 3 departments and includes a week of field work in New Orleans. In this course, students will develop an integrated, holistic understanding of how the city of New Orleans has evolved over time. To support this learning, students have been provided an ArcGIS Online web-based map containing key cultural and historic information about New Orleans selected by their instructors. This interactive tool will enable them to explore New Orleans’ natural environment, built infrastructure and human experience through a variety of lenses. Faculty will use the map to deliver presentations and course materials to students. Students will use their own copy of the map to take notes, complete and deliver course assignments, and add their own materials to the course collection. Link to ArcGIS Online resource guide for class: click hereLink to data dictionary for NOLA class map layers: click hereLink to class website/blog: click here

Bucknell's summer 2015 "New Orleans in 12 Movements" course aims to help students view New Orleans' natural environment, built infrastructure, and human experience in an integrated way. The course is co-taught by faculty from 3 departments and includes a week of field work in New Orleans. In this course, students will develop an integrated, holistic understanding of how the city of New Orleans has evolved over time. To support this learning, students have been provided an ArcGIS Online web-based map containing key cultural and historic information about New Orleans selected by their instructors. This interactive tool will enable them to explore New Orleans’ natural environment, built infrastructure and human experience through a variety of lenses. Faculty will use the map to deliver presentations and course materials to students. Students will use their own copy of the map to take notes, complete and deliver course assignments, and add their own materials to the course collection. Link to ArcGIS Online resource guide for class: click hereLink to data dictionary for NOLA class map layers: click hereLink to class website/blog: click here

Bucknell's summer 2015 "New Orleans in 12 Movements" course aims to help students view New Orleans' natural environment, built infrastructure, and human experience in an integrated way. The course is co-taught by faculty from 3 departments and includes a week of field work in New Orleans. In this course, students will develop an integrated, holistic understanding of how the city of New Orleans has evolved over time. To support this learning, students have been provided an ArcGIS Online web-based map containing key cultural and historic information about New Orleans selected by their instructors. This interactive tool will enable them to explore New Orleans’ natural environment, built infrastructure and human experience through a variety of lenses. Faculty will use the map to deliver presentations and course materials to students. Students will use their own copy of the map to take notes, complete and deliver course assignments, and add their own materials to the course collection. Link to ArcGIS Online resource guide for class: click hereLink to data dictionary for NOLA class map layers: click hereLink to class website/blog: click here

The City of Detroit’s Civil Rights, Inclusion & Opportunity Department (CRIO) runs the Detroit Business Opportunity Program (DBOP). It processes applications, maintains an online register, and annually certifies and recertifies Detroit Based Businesses (DBB), Detroit Headquartered Businesses (DHB), Detroit Resident Businesses (DRB), Detroit Small Businesses (DSB), Detroit Based Micro Businesses (DBMB), Detroit Start-Ups (DSU), Minority-Owned Business Enterprises (MBE), and Woman-Owned Business Enterprises (WBE). Depending on the certification, businesses qualify for the following benefits: appreciation events, networking and capacity building opportunities, equalization credits, and visibility on the register. This dataset is updated weekly and is limited to certifications that were active as of the time when the dataset was last updated.The City of Detroit website provides more information about the Detroit Business Opportunity Program. A list the subset of 3-Digit NIGP Commodity codes that applicants may select to describe their business in the NIGP Code field is available from CRIO.

This Web Map Service (WMS) is the INSPIRE view service for the datasets in the INSPIRE data theme 'hy'.This dataset contains the watercourses of the Grand-Duchy of Luxembourg. The dataset is structured according to the INSPIRE Annex I Theme - Hydrography. The data is derived from the "BD-L-TC" - datasets.

This layer shows the location of designated centres under Financial Assistance Scheme for designated evening adult education courses in Hong Kong. It is a set of the data made available by the Education Bureau under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.