Agenda

Agenda Data: map[string]interface {}{"days":[]interface {}{map[string]interface {}{"date":"Wednesday, April 30", "sessions":[]interface {}{map[string]interface {}{"duration":"90", "room":"Golden Cliff/Eagles Nest", "time":"7:30 AM - 9:00 AM", "title":"Breakfast", "type":"meal"}, map[string]interface {}{"description":"Welcome: Jed Sundwall | Keynote: Chris Holmes", "duration":"45", "room":"Ballroom 2 + 3", "time":"9:00 AM - 9:45 AM", "title":"Welcome and Keynote", "type":"plenary"}, map[string]interface {}{"description":"Julia Wagemann (Track 1), Aimee Barciauskas (Track 2), Brianna Pagán (Track 3)", "duration":"60", "room":"Ballroom 2 + 3", "time":"9:45 AM - 10:45 AM", "title":"Track Introductions: Why this is important?", "type":"plenary"}, map[string]interface {}{"duration":"30", "room":"Superior Lobby", "time":"10:45 AM - 11:15 AM", "title":"Morning Break", "type":"break"}, map[string]interface {}{"duration":"60", "time":"11:15 AM - 12:15 PM", "title":"SLOT 1", "tracks":[]interface {}{map[string]interface {}{"room":"Track 1", "title":"STAC (Wasatch A)"}, map[string]interface {}{"room":"Track 2", "title":"Cloud-Native Innovations 1: Cloud-Native Formats (Magpie A)"}, map[string]interface {}{"room":"Track 2", "title":"GeoAI: Tools (Magpie B)"}, map[string]interface {}{"room":"Track 3", "title":"Disaster Response Discussion (Wasatch B)"}}}, map[string]interface {}{"duration":"60", "room":"Golden Cliff/Eagles Nest", "time":"12:15 PM - 1:15 PM", "title":"Lunch", "type":"meal"}, map[string]interface {}{"duration":"90", "time":"1:15 PM - 2:45 PM", "title":"SLOT 2", "tracks":[]interface {}{map[string]interface {}{"room":"Track 1", "title":"Cloud-Native Data Formats (Wasatch A)"}, map[string]interface {}{"room":"Track 2", "title":"Cloud-Native Innovations 2: New Ideas (Magpie A)"}, map[string]interface {}{"room":"Track 2", "title":"Applications: Cloud-Native Geospatial in Practice (Magpie B)"}, map[string]interface {}{"room":"Track 3", "title":"The Where of it All Discussion (Wasatch B)"}, map[string]interface {}{"room":"Workshop", "title":"Interfacing with Cloud-Native Overture Data and the GERS Ecosystem (Ballroom 2)"}}}, map[string]interface {}{"duration":"15", "time":"2:45 PM - 3:00 PM", "title":"Transition", "type":"break"}, map[string]interface {}{"duration":"60", "time":"3:00 PM - 4:00 PM", "title":"SLOT 3", "tracks":[]interface {}{map[string]interface {}{"description":"Cloud-Native Geospatial data and ArcGIS are a long-standing duo for efficient desktop, web and mobile GIS collaboration through 2D, 3D and multidimensional data experiences. It starts with providing a comprehensive set of tools for imagery and remote sensing data management, analysis and visualization, making it easy to work with cloud-optimized formats such as COG, MRF, CRF, Zarr, and I3S. More recently a STAC user experience was introduced in ArcGIS Pro, along with STAC methods in ArcGIS Python APIs, further opening access to public and private data at the asset level. We'll showcase these capabilities, and how Esri continues to empower professionals by enhancing their ability to securely manage, publish, share and analyze geospatial data effectively using cloud-native deployment patterns that support small to massive scale operations.", "organization":"Esri", "room":"Track 1", "speaker":"David Wright", "title":"Cloud-Native Geospatial and ArcGIS (Wasatch A)"}, map[string]interface {}{"room":"Track 2", "title":"Cloud-Native Innovations 3: Performance and Scale (Magpie A)"}, map[string]interface {}{"room":"Track 2", "title":"GeoAI: Strategies (Magpie B)"}}}, map[string]interface {}{"duration":"30", "room":"Superior Lobby", "time":"4:00 PM - 4:30 PM", "title":"Afternoon Break", "type":"break"}, map[string]interface {}{"description":"Lynne Schneider", "duration":"30", "room":"Ballroom 2 + 3", "time":"4:30 PM - 5:00 PM", "title":"Plenary Talk", "type":"plenary"}, map[string]interface {}{"duration":"60", "room":"Ballroom 2 + 3", "time":"5:00 PM - 6:00 PM", "title":"Plenary Lightning Talks", "tracks":[]interface {}{map[string]interface {}{"room":"Ballroom 2 + 3", "title":"Plenary Lightning Talks"}}, "type":"plenary"}, map[string]interface {}{"duration":"90", "time":"6:15 PM - 7:45 PM", "title":"Welcome Happy Hour", "type":"social"}}}, map[string]interface {}{"date":"Thursday, May 1", "sessions":[]interface {}{map[string]interface {}{"duration":"90", "room":"Golden Cliff/Eagles Nest", "time":"7:30 AM - 9:00 AM", "title":"Breakfast", "type":"meal"}, map[string]interface {}{"description":"Julia Stewart Lowndes", "duration":"30", "room":"Ballroom 2 + 3", "time":"9:00 AM - 9:30 AM", "title":"Keynote", "type":"plenary"}, map[string]interface {}{"duration":"15", "time":"9:30 AM - 9:45 AM", "title":"Transition", "type":"break"}, map[string]interface {}{"duration":"90", "time":"9:45 AM - 11:15 AM", "title":"SLOT 1", "tracks":[]interface {}{map[string]interface {}{"room":"Track 1", "title":"Making Geospatial Workflows More Accessible (Wasatch A)"}, map[string]interface {}{"room":"Track 2", "title":"Systems: Innovations in System Design (Magpie A)"}, map[string]interface {}{"room":"Track 3", "title":"Beyond Open Data Discussion (Wasatch B)"}, map[string]interface {}{"room":"Track 1 Workshop", "title":"On-ramp to CNG: Part 1 (Ballroom 2)"}, map[string]interface {}{"room":"Earthmover Workshop", "title":"Earthmover Workshop (Ballroom 3)"}, map[string]interface {}{"room":"Coworking", "title":"Birds of a Feather & More (Magpie B)"}}}, map[string]interface {}{"duration":"30", "room":"Superior Lobby", "time":"11:15 AM - 11:45 AM", "title":"Morning Break", "type":"break"}, map[string]interface {}{"duration":"90", "time":"11:45 AM - 1:15 PM", "title":"SLOT 2", "tracks":[]interface {}{map[string]interface {}{"room":"Track 1", "title":"Scaling Geospatial Intelligence (Wasatch A)"}, map[string]interface {}{"room":"Track 2", "title":"Systems: Building a Cloud-Native Geospatial Ecosystem (Magpie A)"}, map[string]interface {}{"room":"Hacking", "title":"Birds of a Feather/Coworking (Magpie B)"}, map[string]interface {}{"room":"Track 3", "title":"Workforce Development Discussion (Wasatch B)"}, map[string]interface {}{"room":"Track 1 Workshop", "title":"On-ramp to CNG: Part 2 (Ballroom 2)"}, map[string]interface {}{"room":"Earthmover Workshop", "title":"Earthmover Workshop (Ballroom 3)"}, map[string]interface {}{"room":"Coworking", "title":"Birds of a Feather & More (Magpie B)"}}}, map[string]interface {}{"duration":"60", "room":"Golden Cliff/Eagles Nest", "time":"1:15 PM - 2:15 PM", "title":"Lunch", "type":"meal"}, map[string]interface {}{"duration":"90", "time":"2:15 PM - 3:45 PM", "title":"SLOT 3", "tracks":[]interface {}{map[string]interface {}{"room":"Track 1", "title":"Geospatial Workflows (Wasatch A)"}, map[string]interface {}{"room":"Track 2", "title":"Applications: Weather (Magpie A)"}, map[string]interface {}{"room":"Hacking", "title":"Birds of a Feather/Coworking (Magpie B)"}, map[string]interface {}{"room":"Track 1 Workshop", "title":"On-ramp to CNG: Part 3 (Ballroom 2)"}, map[string]interface {}{"room":"Coworking", "title":"Birds of a Feather & More (Magpie B)"}}}, map[string]interface {}{"duration":"30", "room":"Superior Lobby", "time":"3:45 PM - 4:15 PM", "title":"Afternoon Break", "type":"break"}, map[string]interface {}{"description":"Julia Wagemann (Track 1), Aimee Barciauskas (Track 2), Brianna Pagán (Track 3)", "duration":"15", "room":"Ballroom 2 + 3", "time":"4:15 PM - 4:30 PM", "title":"Recap from Track Leaders", "type":"plenary"}, map[string]interface {}{"description":"Builders Panel: Mo Sarwat, Amy Rose, Sean Gorman", "duration":"30", "room":"Ballroom 2 + 3", "time":"4:30 PM - 5:00 PM", "title":"Plenary Panel", "type":"plenary"}, map[string]interface {}{"duration":"90", "room":"Primrose", "time":"6:30 PM - 8:00 PM", "title":"Gala Dinner", "type":"social"}}}, map[string]interface {}{"date":"Friday, May 2", "sessions":[]interface {}{map[string]interface {}{"duration":"90", "room":"Golden Cliff/Eagles Nest", "time":"7:30 AM - 9:00 AM", "title":"Breakfast", "type":"meal"}, map[string]interface {}{"description":"Drew Breunig", "duration":"30", "room":"Ballroom 2 + 3", "time":"9:00 AM - 9:30 AM", "title":"Keynote & Discussion", "type":"plenary"}, map[string]interface {}{"duration":"20", "time":"9:30 AM - 9:50 AM", "title":"Set up for Activity", "type":"break"}, map[string]interface {}{"duration":"10", "time":"9:50 AM - 10:00 AM", "title":"Transition", "type":"break"}, map[string]interface {}{"duration":"90", "room":"Ballroom 2 + 3", "time":"10:00 AM - 11:30 AM", "title":"Open Discussions & Roundtables", "type":"plenary"}, map[string]interface {}{"duration":"30", "room":"Ballroom 2 + 3", "time":"11:30 AM - 12:00 PM", "title":"Group Reflection & Next Steps", "type":"plenary"}, map[string]interface {}{"duration":"30", "room":"Ballroom 2 + 3", "time":"12:00 PM - 12:30 PM", "title":"Closing Remarks", "type":"plenary"}, map[string]interface {}{"duration":"270", "time":"12:30 PM - 5:00 PM", "title":"Open Coworking & Hacking Space", "type":"social"}}}}}

Slots Data: map[string]interface {}{"Applications: Cloud-Native Geospatial in Practice (Magpie B)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"The EDITO (European Digital Twin of the Ocean) datalake is designed to support the management and delivery of vast marine and environmental datasets, enabling efficient access and analysis for a wide range of applications and stakeholders. This presentation focuses on the strategies and technologies implemented to ensure interoperability, scalability, and performance in a cloud-native environment.", "organization":"Flanders Marine Institute", "speaker":"Frederic Leclercq", "title":"Cloud-Optimized Formats for Scalable and Efficient Data Management in the EDITO Datalake"}, map[string]interface {}{"abstract":"The view from above provided by drone imagery has opened up new insights and opportunities across agriculture, geology, construction, mining, forest and wildfire management, ecological research, and disaster response. But as groups across the globe take to the skies, they each encounter similar big data management challenges around processing, analysis, storing, and sharing.", "organization":"Data Science Institute - University of Arizona", "speaker":"Jeffrey Gillan", "title":"Making the World's Drone Data Open & Cloud-Native"}, map[string]interface {}{"abstract":"I'd like to present how Orbital uses CNG technologies in our Terrascope platform. Real world examples include dark ship detection, global monitoring of GPS spoofing/jamming events and observations of ports/airports. These use cases require using both imagery (EO, SAR, Lidar, etc.) and geolocation data (AIS, ADSB, RF, etc.).", "organization":"Privateer/Orbital Insight", "speaker":"Charlie Savage", "title":"Building a Geospatial Platform with CNG Technologies"}, map[string]interface {}{"abstract":"At CTrees, we create machine learning models that integrate multiple data sources to produce high-resolution, time-series datasets on forest carbon and activity. Our outputs—raster-based estimates of carbon stocks, emissions, removals, and forest change—support projects ranging from jurisdictional analysis to deforestation monitoring.", "organization":"CTrees", "speaker":"Naomi Provost", "title":"Moving from Science to Product: Making Cloud-Native Geospatial Work for Us"}}}, "Applications: Weather (Magpie A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"Floodbase delivers near-real time (NRT) flood monitoring and expertise for parametric flood insurance and disaster relief programs worldwide. Cloud-native geospatial (CNG) technologies like COG, STAC, and geoparquet have enabled this work to scale effectively, despite complexities of diverse data sources and the constraints of real-time monitoring and customer-facing products.", "organization":"Floodbase", "speaker":"Sarah Zwiep", "title":"Why We Don't Use Zarr (Yet)"}, map[string]interface {}{"abstract":"A growing research community is turning to work directly with observational datasets to advance data-driven weather modeling and evaluation. These observations span diverse sensing modalities, from in situ surface stations, radiosondes, and aircraft data, to remotely sensed data across the electromagnetic spectrum.", "organization":"Brightband", "speaker":"Hans Mohrmann", "title":"A Cloud-Native Dataset of Atmospheric Observations for ML Applications"}, map[string]interface {}{"abstract":"Effective decision-making in complex systems requires synthesizing diverse perspectives to address multifaceted challenges under uncertainty. This study introduces a real-world inspired agentic Large Language Models (LLMs) framework, to simulate and enhance decision discourse—the deliberative process through which actionable strategies are collaboratively developed.", "organization":"University of Illinois at Urbana-Champaign", "speaker":"Antoine Dolant", "title":"Agentic LLM Framework for Adaptive Decision Discourse"}, map[string]interface {}{"abstract":"Radars play a critical role in meteorology due to their high spatio-temporal measurement resolution, enabling early detection and tracking of severe weather phenomena. These capabilities empower meteorologists to issue timely alerts and warnings, thereby safeguarding lives and reducing property damage.", "organization":"The University of Illinois at Urbana-Champaign", "speaker":"Alfonso Ladino", "title":"Efficient Weather Radar Data Management in Practice: FAIR Principles and Cloud-Native Solutions"}}}, "Beyond Open Data Discussion (Wasatch B)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"How do we create data products that can be relied upon by the research and commercial sectors? Discussion points include licensing, provenance, data ownership, ongoing maintenance, and the ability to determine the value of data products (in terms of economic benefit and societal good).", "organization":"Discussion Leaders", "speaker":"Marc Prioleau [Overture Maps Foundation], Vikram Gundeti [Foursquare], & Tom Lee [Mapbox]", "title":"Beyond Open Data"}}}, "Birds of a Feather & More (Magpie B)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"We have space available for groups to gather, discuss, make decisions, and get work done. To apply for a Birds of a Feather session, please submit a GitHub issue at https://github.com/cloudnativegeo/community-events/issues under the 'CNG Conference 2025 - Birds of a Feather' issue. ", "organization":"Various", "speaker":"Community", "title":"Birds of a Feather and Coworking Space"}}}, "Cloud-Native Data Formats (Wasatch A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"Data doesn't weigh but has gravity just the same. Gravity can be an apt metaphor to describe architectural choices driving the development cloud-native geospatial tools, formats, and specifications. This was especially true for the development of Cloud Optimized Point Cloud, whose niche is filled with very large, information-sparse datasets that require complex selectivity, backward compatibility, and high efficiency.", "organization":"Hobu, Inc", "speaker":"Howard Butler", "title":"Data Gravity Shapes the Architecture of Cloud-Native Geospatial"}, map[string]interface {}{"abstract":"The increasing availability and co-location of compute, storage, and cloud-optimized data is transforming the pace and nature of scientific discovery, and has the potential to democratize computationally-intensive scientific inquiry. However, the nature of these changes can place a significant burden on users looking to adopt new skills across a range of domains.", "organization":"University of Utah", "speaker":"Emma Marshall", "title":"Cloud-native geospatial datacube workflows with Xarray and Zarr"}, map[string]interface {}{"abstract":"Many level-3 datasets are distributed as collections of thousands of individual files or granules, making it difficult to address the data as a coherent datacube. Worse, the data is often stuck in pre-cloud archival file formats, precluding efficient access from object storage.", "organization":"Earthmover", "speaker":"Tom Nicholas", "title":"VirtualiZarr and Icechunk: How to build a cloud-optimised datacube of archival files in 3 lines"}, map[string]interface {}{"abstract":"An overview of cloud-native data formats for geospatial data, including COG, Zarr, and other emerging formats. Learn how these formats enable efficient data access and processing in the cloud.", "organization":"thriveGEO", "speaker":"Julia Wagemann", "title":"Sentinel's EOPF Toolkit: Driving Community Adoption of the Zarr data format for Copernicus Sentinel Data"}}}, "Cloud-Native Geospatial and ArcGIS (Wasatch A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"Cloud-Native Geospatial data and ArcGIS are a long-standing duo for efficient desktop, web and mobile GIS collaboration through 2D, 3D and multidimensional data experiences. It starts with providing a comprehensive set of tools for imagery and remote sensing data management, analysis and visualization, making it easy to work with cloud-optimized formats such as COG, MRF, CRF, Zarr, and I3S. More recently a STAC user experience was introduced in ArcGIS Pro, along with STAC methods in ArcGIS Python APIs, further opening access to public and private data at the asset level. We'll showcase these capabilities, and how Esri continues to empower professionals by enhancing their ability to securely manage, publish, share and analyze geospatial data effectively using cloud-native deployment patterns that support small to massive scale operations.", "organization":"Esri", "speaker":"David Wright", "title":"Cloud-Native Geospatial and ArcGIS"}}}, "Cloud-Native Innovations 1: Cloud-Native Formats (Magpie A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"At Earthmover, we're seeing a growing number of teams building planetary-scale earth-observation data cubes with Zarr. In the CNG community, Zarr has traditionally been recommended for weather and climate data, while Cloud-Optimized GeoTIFF (CoG) is recommended for geospatial rasters. So why are people choosing Zarr over the highly successful CoG/STAC architecture? This talk explains this trend and clarifies where Zarr is the right choice. The key takeaway is that Zarr brings significant advantages for level-3 data (harmonized spatiotemporal grid), while CoG/STAC remains the optimal choice for level-2 data (many individual scenes with different footprints).", "organization":"Earthmover", "speaker":"Lindsey Neild", "title":"Zarr for Cloud-Native Geospatial. When and Why?"}, map[string]interface {}{"abstract":"To enhance cloud interoperability, Google Earth Engine now supports Cloud-Optimized GeoTIFFs (COGs) hosted on Google Cloud Storage. Integrating COGs and achieving performance comparable to Earth Engine's traditional internally-stored assets presented a substantial technical challenge. This presentation will describe our COG support in detail, examine the technical challenges of integrating COGs with Earth Engine's infrastructure, and provide an analysis of the suitability of COGs for various Earth Engine applications. We will also briefly discuss our exploration of Zarr support, highlighting the format's perceived strengths and weaknesses for Earth Engine applications.", "organization":"Google", "speaker":"Sai Cheemalapati", "title":"Cloud-Native Geospatial in Earth Engine: COGs and Beyond"}, map[string]interface {}{"abstract":"CEOS-ARD is likely the largest collection of specifications for geospatial analysis-ready data (ARD) that is adopted by a wide range of data providers. Currently, each specification - usually one for each product type - consists of a hand-crafted specification document with similar \"boilerplate\" and the actual requirements. With the addition of more product types the maintenance effort of these documents is growing significantly and the consistency across documents is decreasing.", "organization":"Independent Software Engineer and Consultant", "speaker":"Matthias Mohr", "title":"The Future of ARD: Composable Building Blocks"}}}, "Cloud-Native Innovations 2: New Ideas (Magpie A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"In a new project centered around a CONUS-wide, high-resolution, vector dataset, we're exploring using CNG vector formats for our data visualization and distribution strategies. Ideally, our analysis outputs, visualization layer, and distribution strategies could all use the same underlying cloud-native data. GeoParquet has emerged as an exciting potential format for this, but several open questions remain.", "organization":"CarbonPlan", "speaker":"Shane Loeffler & Raphael Hagen", "title":"Exploring questions around visualization and data distribution of vector CNG formats"}, map[string]interface {}{"abstract":"Since its release in May 2021, the SpatioTemporal Asset Catalog (STAC) specification has been widely adopted by the public and private sectors as a core technology for indexing and searching geospatial assets. Part of its success has stemmed from its wide ecosystem of open source tooling in a variety of languages.", "organization":"Development Seed", "speaker":"Pete Gadomski", "title":"Right-sizing STAC: An exploration of STAC API backends, including stac-geoparquet"}, map[string]interface {}{"abstract":"STAC (SpatioTemporal Asset Catalogs) is a specification for defining and searching any type of data that has spatial and temporal dimensions. STAC has seen significant adoption in the earth observation community. Zarr is a specification for storing groups of cloud-optimized arrays. Zarr has been adopted by the earth modeling community (led by Pangeo).", "organization":"Element 84", "speaker":"Julia Signell", "title":"STAC <> Zarr"}, map[string]interface {}{"abstract":"In the rapidly evolving landscape of geospatial data, cloud-native vector data has emerged as a critical component, supported by cloud-native databases and warehouses such as BigQuery, Redshift, and Snowflake, as well as formats like GeoParquet. While SaaS providers like CARTO offer robust tools for large-scale geospatial analysis, there is a growing interest in building custom web mapping applications that leverage these technologies without the associated fees.", "organization":"Camptocamp", "speaker":"Florent Gravin", "title":"Leverage cloud-native vector data in your webmapping application, simply"}}}, "Cloud-Native Innovations 3: Performance and Scale (Magpie A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"The USGS EROS is exploring the transition to the Zarr format. This trade study examines the effects of data processing methods, product archiving, and metadata management at scale as the USGS plans for the future storage of over 200 Petabytes of data in the cloud over the next 15 years. The study will discuss the Zarr structure, compression, processing benefits, visualization, and geospatial readiness.", "organization":"KBR, contractor to USGS", "speaker":"Zachariah Dicus", "title":"Zarr: Landsat Trade Study at Scale"}, map[string]interface {}{"abstract":"During this talk we'll set out to answer a simple question;are Cloud-Optimized GeoTiffs actually scaleable? We'll examine the math and theory behind COG access patterns, look at benchmarks of current software, and discuss potential improvements! You can expect to learn about the COG data format, blob stores, caching, and distributed system design!", "organization":"Regrow", "speaker":"Jeffrey Albrecht", "title":"Are COGs Actually Scaleable?"}, map[string]interface {}{"abstract":"Cloud-native geospatial is predicated on efficient access to cloud storage. Instead of downloading files wholesale, clients need to be able to quickly retrieve portions of files via range requests. But interfacing with multiple clouds is a pain. Using the official Python libraries for each cloud — such as `boto3` for AWS and similar counterparts for Google Cloud Storage and Azure Storage — requires creating your own abstraction layer on top. The most common Python library to abstract access to object storage, `fsspec`, doesn't naturally fit cloud storage providers because it attempts to abstract _files_, not the stateless HTTP requests that cloud operations are much more similar to. `fsspec` can also be hard to use. For example, it's missing static typing support; both a necessity for today's production-caliber Python applications and a big productivity boost for typing-enabled IDEs. This talk will introduce [Obstore](https://developmentseed.org/obstore/latest/), a new Python library to interface with Amazon S3, Google Cloud Storage, and Azure Storage. Powered by a Rust core, it's the simplest, highest-throughput Python interface to cloud object storage. It provides significantly higher throughput than alternatives like fsspec or aioboto3, while being simpler to install and use.", "organization":"Development Seed", "speaker":"Kyle Barron", "title":"Super-fast cloud storage operations with Obstore"}}}, "Disaster Response Discussion (Wasatch B)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"OpenAerialMap (OAM) addresses the need for openly licensed, high-resolution imagery in disaster response, where current access is often fragmented and slow. OAM is moving to a STAC-based architecture to become the main access point for all post-disaster imagery made available in cloud-native format by providers.", "organization":"Humanitarian OpenStreetMap Team", "speaker":"Cristiano Giovando", "title":"OpenAerialMap: Streamlining Access to Open Disaster Imagery"}, map[string]interface {}{"abstract":"With the increasing frequency and severity of natural disasters, the need for efficient and automated disaster monitoring systems has become critical. This study presents a satellite-based disaster monitoring system utilizing cloud computing, developed to enhance the rapid detection and analysis of various disaster events such as flooding, earthquakes, oil spills, and illegal maritime activities.", "organization":"Seoul National University", "speaker":"Duk-jin Kim", "title":"Satellite-Based Disaster Monitoring System Utilizing Cloud Computing"}, map[string]interface {}{"abstract":"After Cristiano Giovando and Duk-jin Kim give their talks, we will continue a conversation around disaster response and its relation to geospatial.", "organization":"Development Seed", "speaker":"Brianna Pagán", "title":"Discussion"}}}, "Earthmover Workshop (Ballroom 3)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"This 3-hour long workshop is designed as an on-ramp to using the Zarr data format for cloud-native geospatial datacube analysis. Guided by experience running similar workshops for the earth, ocean, atmosphere, & climate sciences, we focus on the conceptual underpinnings of array data analytics using Zarr & Xarray.\n\nOur learning goals are:\n\n1. Understand the Zarr chunked n-dimensional array format.\n2. Understand how the Zarr data model complements the Raster data model underlying the GDAL/GeoTIFF/STAC ecosystem.\n3. Learn how to navigate the two data models by building cloud-native datacubes from an input GeoTIFF dataset.\n4. Understand how to use Xarray and the surrounding ecosystem for geospatial analytics\n\nEach hour will contain 30 minutes of instruction, 20 minutes of semi-structured hands-on coding time with support from instructors, and 10 minutes break. Below we list areas of focus for each hour:\n\n**Hour 1:** Introduction to Zarr & Xarray. Compare and contrast with the raster data model.\n\n**Hour 2:** Building your own Zarr data cube from an input GeoTIFF dataset. We will have examples of a small number of data ingestion workflows (approx. 3) that attendees can choose from to build their own data cube. This hour will be mostly hands-on coding with 20 minutes of discussion to address attendee questions and any misconceptions.\n\n**Hour 3:** Analytics with Zarr data cubes using Xarray including raster-vector joins and vector data cubes using a zonal statistics example. End with one or two demos of cloud-native data cube workflows using Zarr. Options include:\n\n- AI/ML training using a Zarr datacube\n- Building a production-grade serverless continental-scale datacube pipeline with Zarr\n\nThese proposed demos are also the subject of a parallel talk submission. The focus here will be pedagogical, the focus there is demonstrative.\n", "organization":"Earthmover", "speaker":"Deepak Cherian, Joe Hamman, Emma Marshall, Tom Nicholas, Lindsey Nield", "title":"Zarr, Icechunk, & Xarray for cloud-native geospatial data-cube analysis"}}}, "GeoAI: Strategies (Magpie B)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"As Earth System Models (ESMs) continue to produce massive datasets at increasingly fine resolutions, climate scientists face challenges in efficiently evaluating and interpreting these large-scale, multi-resolution outputs against observational data. This presentation introduces an advanced analytical framework, combining Hierarchical Data Analysis (HDA), Topological Data Analysis (TDA), and Retrieval-Augmented Generation (RAG) integrated with Large Language Models (LLMs), to streamline the evaluation of climate model performance across spatial scales.", "organization":"Jet Propulsion Laboratory, NASA", "speaker":"Hugo Lee", "title":"Multiscale Geospatial and AI-Driven Approaches for Climate Model Evaluation"}, map[string]interface {}{"abstract":"As we continue developing geospatial access for the 21st century, maps alone will not provide sufficient context. We need to re-examine and address the usability and trustworthiness of data. Geospatial data is inherently complex and diverse, where robust infrastructure and intelligent systems have long been the solution to unlock its full potential.", "organization":"University of Alaska Fairbanks", "speaker":"Erin Trochim", "title":"Questioning Candor: AI-Driven Frameworks for Geospatial Data"}, map[string]interface {}{"abstract":"NASA generates petabytes of geospatial and climate data daily to uncover environmental patterns and predict future scenarios. Understanding long-term climate trends is essential for identifying risks, assessing mitigation strategies, and making informed decisions at planetary scales.", "organization":"University of Utah", "speaker":"Aashish Panta", "title":"Scalable Web-Based Exploration and RAG-enhanced Insights for NASA's Downscaled Climate Data"}}}, "GeoAI: Tools (Magpie B)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"Looking for datasets to fine tune your generativeAI applications? Come to this talk where we will cover how to use datasets in the Registry of Open Data on AWS with the Amazon Bedrock service.", "organization":"Amazon Web Services", "speaker":"Chris Stoner", "title":"Using AWS Open Data with Amazon Bedrock"}, map[string]interface {}{"abstract":"Dive into the technical and financial challenges and solutions behind building a scalable geospatial AI tool. From processing global imagery mosaics to publishing, cataloging and searching billions of embeddings. We'll cover how defaulting to cloud-native technologies and geospatial standards allows us to build global scale solutions on a non-profit budget.", "organization":"Earth Genome", "speaker":"Kwin Keuter & Brad Andrick", "title":"Scaling EarthIndex: AI Meets Cloud-Native Geospatial"}, map[string]interface {}{"abstract":"Google Earth Engine (EE) hosts nearly 100PB of public remote sensing data. Together with its powerful processing language, this presents an ideal platform for deploying Generative AI agents. We have developed several agents to assist users in the often arduous journey of using EE. These agents help users find the most appropriate datasets, learn how to use them, and even write and debug EE code. Additionally, agents can autonomously solve EE tasks—but how well? One of our ongoing projects involves benchmarking agent performance on a carefully selected variety of real-world tasks.", "organization":"Google", "speaker":"Simon Ilyushchenko", "title":"Google Earth Agents: Building and Benchmarking AI Assistants"}}}, "Geospatial Workflows (Wasatch A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"Since December 2020, the United States Geological Survey (USGS) Landsat Collection 2 data archive has been available in an uncompressed Cloud Optimized GeoTIFFs (COG) format on Amazon Web Services (AWS) Simple Storage Services (S3).", "organization":"USGS", "speaker":"Tonian Robinson", "title":"Accessing and Processing Landsat Data in the Cloud"}, map[string]interface {}{"abstract":"Heat risk poses major threats to human and environmental health and safety. According to an Atlantic Council study, there are currently more than 8,500 deaths annually associated with daily average temperatures above 90 degrees Fahrenheit (32 degrees Celsius).", "organization":"Amazon Web Services", "speaker":"Guyu Ye", "title":"Modeling Sustainable Urban Spaces on AWS"}, map[string]interface {}{"abstract":"", "organization":"Safe Software", "speaker":"Dean Hintz", "title":"Model-based data transformation workflows to support loading and automation pipelines for cloud-native with FME"}}}, "Interfacing with Cloud-Native Overture Data and the GERS Ecosystem (Ballroom 2)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"Join us for a hands-on session exploring how to access and integrate Overture Maps Foundation's data within a cloud-native environment. This workshop will cover: An overview of Overture's datasets\nBest practices for working with large-scale geospatial data in the cloud\nA practical introduction to the Global Entity Reference System (GERS)—our approach to linking and enriching geospatial datasets\nWe'll walk through real-world implementation examples, demonstrating how organizations can associate their own data with Overture's open base layers. Whether you're a geospatial developer, data engineer, or GIS practitioner, this session will equip you with the knowledge and tools to make the most of open, interoperable, and scalable geospatial data.", "organization":"Overture Maps Foundation", "speaker":"Dana Bauer & Jennings Anderson", "title":"Interfacing with Cloud-Native Overture Data and the GERS Ecosystem"}}}, "Making Geospatial Workflows More Accessible (Wasatch A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"In this talk, I will present the ongoing work of the GeoJupyter community, an open and collaborative effort to reimagine geospatial interactive computing experiences for education, research, and industry. We aim to combine the approachability and playfulness of desktop GIS tools, the flexibility and reproducibility of coding-driven GIS methods, and the collaborative and storytelling power of Jupyter to enable more researchers, educators, and learners to confidently engage with geospatial data.", "organization":"Schmidt DSE @ UC Berkeley", "speaker":"Matt Fisher", "title":"Exploring more approachable geospatial data workflows as a community"}, map[string]interface {}{"abstract":"In this demo, we will showcase AI tools such as NASA Earthdata search and download, as well as Python code generation in Jupyter Notebooks. These tools are designed to help geospatial analysts, especially beginners, quickly locate relevant datasets and streamline their analysis.", "organization":"Datalayer", "speaker":"Eric Charles", "title":"AI Agent for Jupyter to simplify Earthdata Discovery and Analysis"}, map[string]interface {}{"abstract":"Tethys Platform is an open-source framework that enables scientists to build and deploy GIS web applications for Earth science research, ensuring that findings reach the end users who can benefit from them. It integrates proven open-source components—OpenLayers, CesiumJS, Bokeh, Plotly, GeoServer, THREDDS, and PostGIS—through an SDK that simplifies common web GIS development workflows such as data visualization, exploration, and analysis.", "organization":"Aquaveo | Tethys Geoscience Foundation", "speaker":"Nathan Swain", "title":"Tethys Platform: Bridging Earth Science and Cloud-Native Geospatial Technology"}, map[string]interface {}{"abstract":"DHUD is responsible for delivering the land and built environment geospatial data (some 250 datasets) for South Australia. Some key datasets include cadastre, planning zones, address and suburbs. This presentation explores the delivery of this foundational geospatial data through a serverless geospatial data lake architecture built on AWS.", "organization":"Department for Housing and Urban Development (DHUD), South Australia Government", "speaker":"Greg van Gaans", "title":"Delivery of Enterprise Geospatial Services using a Cloud-Native Geospatial Approach"}}}, "On-ramp to CNG: Part 1 (Ballroom 2)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"The cloud-native geospatial paradigm has the potential to make Earth observation analysis accessible to more people, more easily. In the simplest terms, instead of downloading data before performing an analysis, it's now possible to stream data directly from the cloud.\nThis workshop will explore this new capability using hands-on practical exercises, introducing participants to the incredible global datasets available online and an opinionated suite of tools that can be used to access them. At the end of the workshop, participants will have gained insight into how this cloud-native geospatial paradigm can simplify working with Earth observation data, along with practical examples to assist in implementing learnings going forward. The workshop will include a real-world use case documenting land productivity metrics, which are used as part of monitoring for the UN sustainable Development Goal indicators for 15.3.1. We'll explore this metric using NASA's Harmonized Landsat and Sentinel data accessed through Earthdata.", "organization":"CNG Workshop", "speaker":"Alex Leith", "title":"Cloud-Native Geospatial for Earth Observation"}}}, "On-ramp to CNG: Part 2 (Ballroom 2)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"Ever wonder what GDAL is doing under the hood when you read a GeoTIFF file? Doubly so when the file is a Cloud-optimized GeoTIFF (COG) on a remote server somewhere? Have you been wondering what this new GeoZarr thing is all about and how it actually works? Then there's the whole Kerchunk/VirtualiZarr indexing to get cloud-native access for non-cloud-native data formats, what's that about?\nCloud-native geospatial is all the rage these days, and for good reason. As file sizes grow, layer counts increase, and analytical methods become more complex, the traditional download-to-the-desktop approach is quickly becoming untenable for many applications. It's no surprise then that users are turning to cloud-based tools such as Dask to scale out their analyses, or that traditional tooling is adopting new ways of finding and accessing data from cloud-based sources. But as we transition away from opening whole files to now grabbing ranges of bytes off remote servers it seems all the more important to understand exactly how cloud-native data formats actually store data and what tools are doing to access it.\nThis workshop aims to dig into how cloud-native geospatial data formats are enabling new operational paradigms, with a particular focus on raster data formats. We'll start on the surface by surveying the current cloud-native geospatial landscape to gain an understanding of why cloud-native is important and how it is being used, including:\nthe core tenets of cloud-native geospatial formats cloud-native data formats for both raster and non-raster geospatial data SpatioTemporal Asset Catalogs (STAC) and how STAC is used for raster data discovery and access high-level tooling like odc-stac that can leverage STAC and Dask to scale processing of cloud-native data Then we'll get hands-on and go deep to build up an in-depth understanding of how cloud-native raster formats work. We'll examine the COG format and read a COG from a cloud source by hand using just Python, selectively extracting data from the image without any geospatial dependencies. We'll repeat the same exercise for geospatial data in Zarr format to see how that compares to our experience with COGs. Lastly we'll turn our attention to Kerchunk/VirtualiZarr to see how these technologies might allow us to optimize data access for non-cloud-native formats.", "organization":"CNG Workshop", "speaker":"Jarret Keifer", "title":"Deep Dive into Cloud-Native Geospatial Raster Formats"}}}, "On-ramp to CNG: Part 3 (Ballroom 2)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"Ever wonder what GDAL is doing under the hood when you read a GeoTIFF file? Doubly so when the file is a Cloud-optimized GeoTIFF (COG) on a remote server somewhere? Have you been wondering what this new GeoZarr thing is all about and how it actually works? Then there's the whole Kerchunk/VirtualiZarr indexing to get cloud-native access for non-cloud-native data formats, what's that about?\nCloud-native geospatial is all the rage these days, and for good reason. As file sizes grow, layer counts increase, and analytical methods become more complex, the traditional download-to-the-desktop approach is quickly becoming untenable for many applications. It's no surprise then that users are turning to cloud-based tools such as Dask to scale out their analyses, or that traditional tooling is adopting new ways of finding and accessing data from cloud-based sources. But as we transition away from opening whole files to now grabbing ranges of bytes off remote servers it seems all the more important to understand exactly how cloud-native data formats actually store data and what tools are doing to access it.\nThis workshop aims to dig into how cloud-native geospatial data formats are enabling new operational paradigms, with a particular focus on raster data formats. We'll start on the surface by surveying the current cloud-native geospatial landscape to gain an understanding of why cloud-native is important and how it is being used, including:\nthe core tenets of cloud-native geospatial formats cloud-native data formats for both raster and non-raster geospatial data SpatioTemporal Asset Catalogs (STAC) and how STAC is used for raster data discovery and access high-level tooling like odc-stac that can leverage STAC and Dask to scale processing of cloud-native data Then we'll get hands-on and go deep to build up an in-depth understanding of how cloud-native raster formats work. We'll examine the COG format and read a COG from a cloud source by hand using just Python, selectively extracting data from the image without any geospatial dependencies. We'll repeat the same exercise for geospatial data in Zarr format to see how that compares to our experience with COGs. Lastly we'll turn our attention to Kerchunk/VirtualiZarr to see how these technologies might allow us to optimize data access for non-cloud-native formats.", "organization":"CNG Workshop", "speaker":"Jarret Keifer", "title":"Deep Dive into Cloud-Native Geospatial Raster Formats"}}}, "Plenary Lightning Talks":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"organization":"VorGeo", "speaker":"Tyler Erickson", "title":"Mapping the CNG Ecosystem"}, map[string]interface {}{"organization":"Pachama", "speaker":"Martha Morrissey", "title":"Helping Restore Nature with Cloud-Native Geospatial"}, map[string]interface {}{"organization":"Humanitarian OpenStreetMap Team", "speaker":"Jessie Pechmann", "title":"Cloud-Native Geo for Good: Empowering Humanitarian Action with Accessible Data"}, map[string]interface {}{"organization":"Center for Nonlinear Studies", "speaker":"Arvind Mohan", "title":"Bridging \"Nerdville\" and \"Fieldville\": A Vision for Resilient Data Infrastructure for Real-World AI in Crisis Management"}, map[string]interface {}{"organization":"State of Maryland", "speaker":"Natalie Evans Harris", "title":"Maryland State Chief Data Officer Lightning Talk"}, map[string]interface {}{"organization":"The World Bank", "speaker":"Benjamin P. Stewart", "title":"The World Bank Lightning Talk"}, map[string]interface {}{"organization":"CARTO", "speaker":"Michal Migurski", "title":"CARTO Lightning Talk"}, map[string]interface {}{"organization":"The New York Times", "speaker":"Tim Wallace", "title":"The New York Times Lightning Talk"}}}, "STAC (Wasatch A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"Discover the power of STAC (SpatioTemporal Asset Catalog) in this presentation, which simplifies its core concepts and provides a clear overview of the current state of the STAC ecosystem. Learn about best practices, the latest advancements, available public STAC catalogs, and how to navigate the myriad extensions available", "organization":"Element 84", "speaker":"Matthew Hanson", "title":"Introduction to STAC"}, map[string]interface {}{"abstract":"Overture maps wants the CNG community to be able to find our data easily. We also want to enable developers to write awesome tools to use our data! We think STAC can help us accomplish both of these things. I'll outline how Overture went from hand-rolling our own release manifests to discovering the STAC format, and how we're working towards publishing STAC representations of our monthly data releases.", "organization":"Overture Maps Foundation & Meta", "speaker":"Benjamin Clark", "title":"STAC-ing GeoParquet"}, map[string]interface {}{"abstract":"The intricate syntax of OGC's Common Query Language (CQL2-JSON) introduces a steep learning curve, hindering widespread adoption within the SpatioTemporal Asset Catalog (STAC) community. Its verbose JSON structure makes programmatic query generation challenging, impeding the STAC goal of seamless geospatial data discovery. To address this, I developed CQLAlchemy, a Python Object Document Mapper library that bridges the gap between CQL2-JSON's power and STAC's vision of user-friendly access.", "organization":"Umbra", "speaker":"David Raleigh", "title":"Effortless STAC Queries: CQLAlchemy and the Power of Autocompletion"}}}, "Scaling Geospatial Intelligence (Wasatch A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"A key aspect to capacity building for cloud-native geospatial data is considering professionals that are experienced with cloud-native formats but new to geospatial data. As a community, it is important to think about how we can make it easier for such professionals to feel confident in the accuracy, suitability, and stability of the geospatial methods supporting their workflows.", "organization":"Esri", "speaker":"Noah Slocum", "title":"Spatial analysis at scale with ArcGIS GeoAnalytics Engine and Apache Spark"}, map[string]interface {}{"abstract":"Inferring objects and detecting change in satellite imagery was once reserved for companies with the talent, money, and time to build, manage, and run sophisticated, self-managed machine learning (ML) inference solutions against satellite data.", "organization":"Wherobots", "speaker":"Damian Wylie", "title":"Extract insights from satellite imagery at scale with WherobotsAI"}, map[string]interface {}{"abstract":"What is the essence of cloud-native geospatial? It might not be a SaaS, but instead simple primitives that are simple to publish anywhere. This is a hands-on workshop to making global raster and vector data publishable and accessible, through the lens of the Protomaps open source project.", "organization":"Protomaps", "speaker":"Brandon Liu", "title":"Minimum Viable CNG"}}}, "Systems: Building a Cloud-Native Geospatial Ecosystem (Magpie A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"Democratization requires cost absorption, whether by vendors or communities. This talk reframes the cloud-native conversation through the lens of the Google Earth Engine (GEE) Community Catalog, an initiative that has grown since 2020 to host 3,800+ datasets, over half a petabyte of data, and serve millions of users worldwide.", "organization":"Desert Research Institute | Spatial Bytes", "speaker":"Samapriya Roy", "title":"Cloud-Native Commons: Lessons from Building the Earth Engine Community Catalog"}, map[string]interface {}{"abstract":"This presentation showcases a powerful architecture for dynamically consuming Overture Maps data at scale, without requiring local storage. By placing a robust mapping solution—GeoServer Cloud—at the forefront, we enable not only efficient and parallelized access to Overture Maps through distributed WFS services but also the ability to enrich data access with advanced business logic.", "organization":"Geotekne", "speaker":"Jose Macchi", "title":"Unleashing Massive Cloud Power: Scalable & Enriched Access to OvertureMaps with GeoServerCloud"}, map[string]interface {}{"abstract":"The growth of space-derived geospatial data is exponential. And the rise of hyperspectral satellite imaging is not only expanding Earth Observation (EO) applications, it is drastically increasing the volume of data you have to downlink, transfer, process, and store.", "organization":"Tilebox", "speaker":"Lukas Bindreiter", "title":"Tooling Designed for the Complexities of Space Data: A Hyperspectral Data Workflow"}, map[string]interface {}{"abstract":"Geospatial datasets are growing rapidly, often exceeding 100TB and reaching petabyte scale. While many of these datasets are publicly available, unlocking their full potential requires scalable infrastructure, efficient computation, and a diverse set of tools.", "organization":"Coiled", "speaker":"Sarah Johnson", "title":"Scaling Cloud-Native Geospatial Workflows with Dask, Xarray, and Coiled"}}}, "Systems: Innovations in System Design (Magpie A)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"The first obstacle when developing geospatial data pipelines is finding relevant input data and organizing output data in the form of a catalog. Current storage engines for spatio-temporal metadata are often limited to indices either on the space dimension or on the time dimension, but not on both.", "organization":"Tilebox", "speaker":"Lukas Bindreiter", "title":"Beyond Points: Efficient Spatio-Temporal Polygon Indexing for Scalable Catalogs"}, map[string]interface {}{"abstract":"From when the word \"computers\" referred to buildings of people to the design of the Von Neumann architecture (i.e. the CPU), the history of computing is marked by inventions in service of predicting the weather. Machine learning has recently added to this legacy with a revolution in weather forecasting.", "organization":"Open Athena AI Foundation", "speaker":"Alexander Merose", "title":"Why machine learning people should think about databases: lessons from AI weather models"}, map[string]interface {}{"abstract":"Cloud-native geospatial promises the ability to scale your workloads to massive datasets with ease. As the datasets become larger and your workload becomes more computationally intensive, accelerated computing can save us from ever increasing runtimes.", "organization":"NVIDIA", "speaker":"Tom Augspurger", "title":"GPU-accelerated Cloud-Native Geospatial"}, map[string]interface {}{"abstract":"This talk explores Apache Iceberg's new native geospatial support (Iceberg Geo), tackling challenges in large-scale spatial data management. It covers Iceberg Geo's development, design, and goals, highlighting its impact on both geospatial and Iceberg communities.", "organization":"Wherobots", "speaker":"Matthew Powers", "title":"Introducing geospatial support in Apache Iceberg"}}}, "The Where of it All Discussion (Wasatch B)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"Where will geospatial projects live in the future? Given shifts in research funding, what organizations will house open-source projects and other resources needed by the geospatial community? How do we reliably create and maintain data needed for planetary-scale challenges? What are sustainable funding models?", "organization":"Discussion Leaders", "speaker":"Katie Baynes [NASA], Lena Trudeau [Inclined Analytics], & Jen Marcus [Taylor Geospatial Engine]", "title":"The Where of it All"}}}, "Workforce Development Discussion (Wasatch B)":map[string]interface {}{"presentations":[]interface {}{map[string]interface {}{"abstract":"How do we grow our community on purpose, particularly helping poor people get paying jobs? Funding models.", "organization":"Community Discussion", "speaker":"Community Discussion", "title":"Workforce Development"}}}}