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Crop type maps are essential for informing, assessing, and managing agricultural practices and food security. Traditionally, ground truth data for these maps is collected through field surveys, which are time-consuming, labor-intensive, and difficult to scale. As a result, large-scale crop type mapping faces challenges in obtaining comprehensive and high-quality reference data. Street view imagery and vehicle-based surveys offer alternative solutions for field inspections, providing scalable and cost-effective ways to collect crop type information.
This 2-hour workshop will enable participants to: 1. Explore the potential of street view imagery as a novel and emerging source for large-scale crop type ground truth data collection. 2. Understand CropSight, the GeoAI-driven workflow for retrieving object-based crop type ground truth information, including collecting geotagged street view images, extracting crop type labels from street view images, and delineating crop field boundaries corresponding to each retrieved label using satellite imagery.
3. Discuss key challenges, best practices, and opportunities for integrating street view imagery into remote sensing workflows.
Participants of all skill levels in Python are welcome, and we will use Google Colab to ensure accessibility and ease of use. To support hands-on learning, we will provide example datasets and Jupyter notebooks for the workshop.
Authors Zhijie Zhou (zhijiez2@illinois.edu), Tianci Guo (tiancig2@illinois.edu), Yin Liu (yinl3@illinois.edu), Chunyuan Diao (chunyuan@illinois.edu) Department of Geography and GIScience, University of Illinois Urbana-Champaign
AI enhances productivity—it helps us achieve faster results and gain deeper insights into the problems at hand. In this workshop, you will learn about AI capabilities in ArcGIS that empower educators to integrate AI seamlessly and responsibly into their work. Participants will explore practical applications of GeoAI, such as land use classification, object detection, and predictive modeling, using ArcGIS Online and ArcGIS Pro. Participants also will learn about AI Assistants in ArcGIS and how these assistants can increase productivity and improve workflows. Throughout the workshop, we’ll discuss how GeoAI and AI assistants might change GIScience education and how we can develop policies and practices to align AI use with our educational goals.
Learning goals:
Get familiar with what is possible with GeoAI and AI assistants
Get excited–AI in GIS is not just for advanced workflows
Understanding how people see, feel, and think about their surroundings (i.e., locality characteristics) is important, as this directly affects well-being, an essential component of sustainable development, and impacts levels of physical activity, which is crucial for public health. Efficient ways for identifying locality characteristics (e.g., disorder, safety) allow stakeholders, including policymakers and urban planners, to improve quality of life.
This 4-hour workshop offers a hands-on opportunity for participants to explore methods using language models, including BERT and our previous work SpaBERT and GeoLM, to capture shared locality characteristics through online text descriptions of places. Additionally, we will introduce in-context learning methods with vision large language models to understand locality characteristics using street view images.
Participants will learn to 1) process text descriptions with locations or street view images and then train tools to learn locality characteristics, 2) visualize localities using the learned characteristics, enabling comparative analysis between results from text and images, and 3) fine-tune tools to predict locality indices such as greenness or other human perception indexes.
This workshop is designed for researchers, practitioners, and anyone interested in learning how to capture locality characteristics from text and images.
Participants are encouraged to bring their own location-based text datasets they would like to test. Basic Python knowledge is required to complete the experiments.
Authors Jina Kim, Guanyu Wang, Michelle Pasco, Yao-Yi Chiang1 University of Minnesota, Minneapolis, MN, USA
Over the last three decades the environment, society, and GIScience have all changed dramatically. GIScience education has likewise evolved, but the race to keep up never ends. We invite participants to join an expanding community effort to build open GIScience learning materials that can be adapted to meet different educational needs and evolve with the constantly changing world.
In this workshop, we will work with participants to create GIScience teaching materials to solve applied problems of their choosing. We will briefly present milestones from an ongoing collaborative project between the UCSB Center for Spatial Studies and Esri and introduce participants to our approach to developing content for the GIS classroom. We will then invite participants to work with our team to develop teaching materials using our framework and resources. Working in small groups with our team members, participants will workshop their applied GIS problem, link that problem to GIScience concepts and pedagogical objectives, and begin to storyboard their lessons.
Participants will also engage in collective discussion of the ethical lessons that could be drawn from their problem and highlighted for students. Beyond this workshop, we invite participants to continue to collaborate with us, complete the development of their lessons, and share their lessons through an online community platform. Participants that chose to share their materials will receive full acknowledgement for their work and be highlighted as project contributors.
Before arriving for the workshop, we ask participants to dedicate about 2 hours reviewing shared materials and preparing their applied GIS problem.
Purpose Disparities between Black and White communities in the U.S. stem from a deep-rooted history of racist and discriminatory policies that have perpetuated inequities across multiple social dimensions. This form of racism, i.e., structural racism, has disproportionately impacted Black communities by limiting their access to housing, income and wealth through employment opportunities, education, voting, and healthcare. Despite widespread recognition of the impact of structural racism, a standardized methodology for quantifying its manifestation at the population/neighborhood level remains a challenge. To address this critical need, we developed an index of structural racism and discrimination (SRD), the first national, place-based, and community-informed measure.
Objectives and Activities This interactive workshop will introduce participants to the SRD Index, designed to evaluate the impact of structural racism across key social dimensions in states and counties over 40 years. Participants will gain insights into the index’s data sources, methodology, and applications. A live demonstration will highlight its interactive data visualization tool, illustrating how to interpret SRD impact scores across dimensions such as residential segregation, housing, income, healthcare, and incarceration. Hands-on activities will enable participants to explore geographical patterns and practical applications in research, policy development, and advocacy.
Expected Outcomes By the end of this workshop, participants will be equipped with the knowledge and skills to navigate and utilize the SRD Index tool, interpret its scores, download data, and apply related data in academic research, policy analysis, and advocacy efforts.
Workshop Materials Participants will need a laptop or a tablet to access the SRD Index tool (link will be provided). Printed or digital handouts summarizing key concepts will be available to support learning.
Authors Debs (Debarchana) Ghosh, Sabina Bhandari University of Connecticut, Department of Geography, Sustainability, Community, and Urban Studies
Devlon Nicole Jackson, Cheryl L. Knott University of Maryland School of Public Health, Department of Behavioral and Community Health
