AndreaBozzo/ceres-open-data-index huggingface dataset:
The Earth as Art Tutorial

Satellites orbiting the Earth capture an incredible variety of views or images
of the Earth’s surface. Many of these satellite images are simply intriguing to
view because of the mesmerizing beauty of river deltas, mountains, and other
landscapes within the images. Some images may even remind you of actual
famous works of art!

This tutorial is divided into three instructional sessions, each focusing on a
stage of the Earth as Art development process. Both Art/Color and Image
Format are covered within this first seession. Topics in the second session include Image Download, Image Manipulation and Image Format – Ready to
Print. The third session comprises Guidelines for Printing, Material Sources,
Product Development, and Gallery Exhibit Planning. 

Organization: LouisianaView
Remembering Charles J. Robinove Poster

Project EROS was envisioned to be a Department of the Interior program that
utilized resources data from aircraft and spacecraft to facilitate the assessment and
management of land, mineral, and water resources. The EROS, or Earth Resources
Observation Satellite, Program was formally established by Secretarial Order in 1967.
Under the EROS Program, Chuck became the Associate Program Manager. In this
position, Chuck participated in international remote sensing workshops, facilitated
remote sensing research, and coordinated program development. He witnessed his
years of hard work and diligence when ERTS-A (Landsat 1) was launched on July 23, 1972.
The year 1972 also brought happiness to Chuck in his personal life when he married
Elizabeth Anne Schauer on July 9, 1972, just two weeks prior to the launch of ERTS-A

Organization: UtahView
L2FHM-OMA: Landsat- and Lidar-based Forest Height Map for Oahu Mountainous Areas

HawaiiView combined island-wide Landsat-8 cloud-free mosaic and 2013 lidar data to map vegetation height over Oahu’s mountainous areas (OMA). We first estimated vegetation height over lidar coverage areas, then used the data over the overlap areas to create a model of predicting lidar-based height from Landsat imagery, and finally predicted height over mountains using the model and the Landsat-8 mosaic.

Organization: HawaiiView
Kansas University Field Station NEON Data Viewer

KansasView also developed an AGOL web mapping application
to provide users the ability to explore and visualize the National
Science Foundation’s National Earth Observation Network
(NEON) Airborne Observation Platform (AOP) remote sensing
data collected at the KU Field Station (KUFS) along with Sentinel2, Landsat 8, and aerial image services (mid right). KansasView
previously supported processing of tiled NEON AOP data to
create data mosaics that were then made available through the
online tool.


Organization: KansasView
Sentinel Green Report

The Sentinel Green Report application leverages Google Earth Engine to measure and
monitor vegetation across the United States. There are four maps created from the Sentinel-2 archive. 1) Greenness Map represents NDVI, which is a surrogate for photosynthetically active plant biomass, for a user-defined composite period; Difference Map 1-compares NDVI to the previous composite period within the same year to illustrate recent vegetation change; Difference Map 2-compares NDVI to same period from the previous year to examine year-over-year vegetation change; Difference Map 3-compares current NDVI to the average NDVI from previous years to examine vegetation change relative to the recent average. These maps can be used for a wide range of applications such as crop monitoring and disaster assessment.

Organization: KansasView
Kansas Ecological Systems WebMap

This Web Mapping Application was shows the recently developed Kansas Ecological Systems Map and associated field sites used for the mapping endeavor.

The development of the Ecological Systems Map was a collaborative effort between the following state agencies and Universities:
Kansas Department of Wildlife and Parks (provided funding)
Nebraska Fish and Game (provided funding)
University of Missouri
University of Kansas
University of Nebraska-Lincoln
Chardon State

Data Sources include:
Field sites in Kansas and Nebraska
Sentinel Satellite Imagery
NRCS Soils data
Kansas Forest Service
USGS Topography
Ecoregions 

Organization: KansasView
Exploring the Landscape of K12 GIS Education in Iowa

Annual funding (2020-2021) from  AmericaView  allowed IowaView staff to survey Iowa public school K-12 superintendents and teachers to understand how GIS and remote sensing were being used in K-12 school districts and classrooms in Iowa. The surveys also provided a starting point for outreach and a way to assess if there is a need to improve knowledge and access to available resources such as Esri’s free educational licensing and educational materials as a way to build a foundation of GIS education throughout the state.

Organization: IowaView
2019 Earth Observation Day Poster

The 2019 poster highlights 12 remarkably beautiful Landsat images from around the globe.  The reverse side describes how earth observations through the Landsat program enhance our understanding and engagement with the geosciences.  Featured images include scenes of India, California, Chile, Brazil, South Dakota, China, Siberia, Antarctica, Mississippi, Niger, Texas, and Lake Eire.

Organization: AmericaView
Methods in Open Science

The primary goal of this course is to explore methods that scientists and analysts use to process, explore, and analyze data; document, present, and communicate results; and generate useful knowledge from raw data using reproducible methods.

This course has several broad components. The more conceptual material is predominantly presented using web-based slides and modules. Annotations and text are provided throughout in the Notes section to the right of the slide. We also include some embedded videos, some interactive web content, links to web resources, references to publications, and book recommendations.

We have also provided materials associated with learning to code and analyze data in both the Python and R languages and computational environments. This material is intentionally redundant: you can complete the assignments in the language of your choosing or attempt them in both environments. In the world of data science, Python and R are both used, and many practitioners are bilingual. So, we have included examples in both languages. This material is presented as webpages with code examples and explanations, videos, examples, and assignments. Please see the sequencing document for our suggested order in which to work through the material. We have also provided PDF versions of the lectures with the notes included.

This course is meant for those who have no prior experience working with data analytics or coding. So, it is fine if these concepts are completely new to you. We have tried to focus on key concepts and hands-on applications. Once you have completed this course, you may want to take a deeper dive into some of the broad topics discussed.

Organization: West Virginia View
Digital Cartography

Cartography is the knowledge associated with the art, science, and technology of maps. Maps portray spatial relationships among selected phenomena of interest and increasingly are used for analysis and synthesis. Through digital cartography and web mapping, however, it is possible for almost anyone to produce a bad map in minutes. Although cartography has undergone a radical transformation through the introduction of digital technology, fundamental principles remain. Doing computer cartography well requires a broad understanding of graphicacy as a language (as well as numeracy and literacy). This course provides an introduction to the principles, concepts, software, and hardware necessary to produce good maps, especially in the context (and limitations) of geographic information systems (GIS) and the web.

You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of cartography projects to reinforce the material. Lastly, you will complete term projects. Please see the sequencing document for our suggestions as to the order in which to work through the material. We have also provided PDF versions of the lectures with the notes included.

Organization: West Virginia View
Remote Sensing

This course explores the theory, technology, and applications of remote sensing. It is designed for individuals with an interest in GIS and geospatial science who have no prior experience working with remotely sensed data. Lab exercises make use of the web and the ArcGIS Pro software. You will work with and explore a wide variety of data types including aerial imagery, satellite imagery, multispectral imagery, digital terrain data, light detection and ranging (LiDAR), thermal data, and synthetic aperture RaDAR (SAR). Remote sensing is a rapidly changing field influenced by big data, machine learning, deep learning, and cloud computing. In this course you will gain an overview of the subject of remote sensing, with a special emphasis on principles, limitations, and possibilities. In addition, this course emphasizes information literacy, and will develop your skills in finding, evaluating, and using scholarly information.

You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises to reinforce the material. Lastly, you will complete paper reviews and a term project. We have also provided additional bonus material and links associated with surface hydrologic analysis with TauDEM, geographic object-based image analysis (GEOBIA), Google Earth Engine (GEE), and the geemap Python library for Google Earth Engine. Please see the sequencing document for our suggested order in which to work through the material. We have also provided PDF versions of the lectures with the notes included.

Organization: West Virginia View
Open-Source Spatial Analytics (R)

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course.

Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material.

After completing this course you will be able to:

prepare, manipulate, query, and generally work with data in R.
perform data summarization, comparisons, and statistical tests.
create quality graphs, map layouts, and interactive web maps to visualize data and findings.
present your research, methods, results, and code as web pages to foster reproducible research.
work with spatial data in R.
analyze vector and raster geospatial data to answer a question with a spatial component.
make spatial models and predictions using regression and machine learning.
code in the R language at an intermediate level.

Organization: West Virginia View
GIScience

In this course, you will explore the concepts, principles, and practices of acquiring, storing, analyzing, displaying, and using geospatial data. Additionally, you will investigate the science behind geographic information systems and the techniques and methods GIS scientists and professionals use to answer questions with a spatial component. In the lab section, you will become proficient with the ArcGIS Pro software package.

This course will prepare you to take more advanced geospatial science courses.

You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises, assignments, and less guided challenges. Please see the sequencing document for our suggestions as to the order in which to work through the material. To aid in working through the lecture modules, we have provided PDF versions of the lectures with the slide notes included. This course makes use of the ArcGIS Pro software package from the Environmental Systems Research Institute (ESRI), and directions for installing the software have also been provided. If you are not a West Virginia University student, you can still complete the labs, but you will need to obtain access to the software on your own.

Organization: West Virginia View
Client-Side Web GIS

The World Wide Web has become a valuable means to display, collect, and share geospatial data and maps. This course will explore the use of web technologies for developing web map applications. Students will learn to produce audience appropriate maps in the web environment using a variety of technologies and methods.

This class will specifically focus on client-side technologies and is broken into three broad components. First, you will learn to design maps and apps using the tools made available by the Environmental Systems Research Institute (ESRI) including ArcGIS Online, ArcGIS Web AppBuilder, ArcGIS ExperienceBuilder, and ArcGIS Pro. Next, you will explore client-side web programming technologies and languages. You will learn to define web content using hypertext markup language (HTML), style web content using cascading style sheets (CSS) and Bootstrap, and add functionality to a website using JavaScript and jQuery. Lastly, you will learn to produce web maps using the ESRI ArcGIS API for JavaScript and the free and open-source Leaflet JavaScript API. Through exploring these topics, you will also learn how the web works and how geospatial data are stored for use in the web environment. You will also develop some coding skills specific to HTML, CSS, and JavaScript. By the end of this course, you will be able to design a multipage website that includes web maps.

After completing this course you will be able to:

describe how the web works using client-server architecture.
produce an audience appropriate web map using ArcGIS Online.
publish data and maps for use in the web environment.
describe geospatial data formats for use on the web and when each is most appropriate.
solicit crowd sourced data using feature services to collect volunteered geographic information (VGI).
produce and publish a web map application using Web AppBuilder for ArcGIS or ArcGIS ExperienceBuilder.
develop multipage websites that are well styled and responsive using HTML, CSS, and Bootstrap.
add functionality to web pages and web maps using JavaScript and jQuery.
produce customized web maps using the ArcGIS API for JavaScript and the Leaflet JavaScript API.

Organization: West Virginia View
Open-Source GIScience Online Course

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:

1. apply QGIS to visualize, query, and analyze vector and raster spatial data.
2. use available resources to further expand your knowledge of open-source technologies.
3. describe and use a variety of open data formats.
4. code in Python at an intermediate-level.
5. read, summarize, visualize, and analyze data using open Python libraries.
6. create spatial predictive models using Python and associated libraries.
7. use SQL to perform table and spatial queries at an intermediate-level.

Organization: West Virginia View
Geospatial Deep Learning Seminar Online Course

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:

1. explain how ANNs work including weights, bias, activation, and optimization.
2. describe and explain different loss and assessment metrics and determine appropriate use cases.
3. use the tensor data model to represent data as input for deep learning.
4. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization.
5. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data.
6. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs.
7. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.
8. explain how object and instance segmentation are different from traditional CNNs and semantic segmentation and how they can be used to generate bounding boxes and feature masks for each instance of a class.
9. use ArcGIS Pro to perform object detection (to obtain bounding boxes) and instance segmentation (to obtain pixel-level instance masks).

Organization: West Virginia View
Landsat Collage Hands-On Activity

The Landsat series of satellites has been collecting visually stunning and scientifically valuable images of Earth since 1972. While Landsat data have played a critical role in the study of our land resources, images from these data have provided some of the most beautiful and unique views of our planet.

From folded red mountain ridges, yellow dunes dotted with brilliant pools of blue water, and swirling clouds of blue-green phytoplankton, these images reveal a hidden world of color, textures, patterns, and movement.
AmericaView Education and Outreach StoryMap

The AmericaView Education and Outreach Committee was established to serve as a trusted source for Earth observation educational resources and partnerships. The Committee has an active mission of creating and disseminating educational materials, resources, and tools to learners of all ages. This StoryMap exemplifies the work conducted by the AmericaView network and by StateView members across the nation that supports the visions and missions of AmericaView and the AmericaView Education and Outreach Committee. 

AmericaView and StateView education and outreach activities are grouped into five categories: Earth Observation Day, Earth As Art, K-16 and Community Education, Workforce and Professional Development, and Disaster and Emergency Response.

Organization: AmericaView
Open Source Workforce Development Training Courses

This course series was created by AmericaView and West Virginia View to support workforce development training at the United States Geological Survey (USGS). The first two courses introduce principles and key concepts in remote sensing and GIScience. They serve as background material for those not familiar with remote sensing and/or GIScience. There are no labs or assignments associated with these modules. The remaining modules cover working with and analyzing geospatial and remotely sensed data using open-source software tools including QGIS, R, and Python. The QGIS content also includes examples using GRASS, SAGA, and Orfeo Toolbox. The R and Python materials assume no prior knowledge of coding in the R or Python languages. The final three courses include lecture modules, assignments, and all required data. The materials presented here were augmented from materials made available on the West Virignia View website. If you would like to learn more or investigate other geospatial data science topics (digital cartography, web GIS, and geospatial deep learning), please check out this content.

Organization: West Virginia View