👉 Live demo : https://giro3d.org/latest/examples/sun-exposure.html

Maggie and Jake detail the collaborative efforts of the Trails Stewardship Initiative, a working group bringing together land managers (NPS, USFS, BLM), app companies, and the mapping community to standardize trail schemas and rendering guidelines. They showcase pilot projects in Washington and Utah that improved data for over 300,000 miles of trails, and introduce new tools like "Layercake"—which republishes OSM data as GeoParquet for traditional GIS workflows—and the Digital Trail Ambassador program designed to bridge the gap between volunteer mappers and professional land managers.

Highlights:
🛰️ The OSM Foundation & US Chapter: An overview of how the global geo-database is organized, from the servers in Ireland to the only US chapter with paid staff
🔄 The "Blue Dot" Problem: How post-COVID surges in outdoor recreation led to search-and-rescue issues and irresponsible use of unofficial "social trails"
🧩 Schema Standardization: Navigating the complex needs of different federal agencies to create high-priority tags for trail operators, closure status, and private access
⚙️ Rendering Guidelines: Encouraging app companies to distinguish between official trails and unofficial social paths to prevent environmental degradation
🌐 Pilot Success: A look at the pilot mapping campaign in Utah’s Bryce County and its impact on responsible recreation and land management
🏢 Layercake & Open Trail Map: New tools for exporting OSM data into cloud-native formats like GeoParquet and visualizing trail data gaps
🏗️ Digital Trail Ambassadors: A new program recruiting volunteers to act as liaisons between local park rangers and the global mapping community

For more content like this check out www.projectgeospatial.com

#OpenStreetMap #FOSS4G #Trails #OutdoorRecreation #OSM #GIS #NationalParks #PublicLands #GeoParquet #CommunityMapping #Safety #OpenSource #ProjectGeospatial #DataStewardship #Conservation

The presentation provides a deep dive into the course structure, which uses a Project-Based Learning (PBL) framework to move students from introductory GIS software to high-level spatial analysis in R and Python. The students share their experience reproducing a high-profile study on hazardous heat exposure in prisons, detailing the technical hurdles they encountered—such as missing data grids, inconsistent code scripts, and undocumented environments—and how overcoming these obstacles deepened their understanding of geographic validity and the full research life cycle.

Highlights:
🛰️ The Reproducibility Crisis: Why most GI science studies can't be repeated and the difference between computational reproduction and broader verification
🔄 Project-Based Learning (PBL): Moving beyond step-by-step lab tutorials to sustained, authentic inquiry on real-world research problems
🧩 Open Science Research Compendium: A look at a standardized structure for organizing raw data, metadata, procedures, and computational environments
⚙️ Building a Pre-Analysis Plan: The importance of registering a research strategy in a computational notebook before executing any code
🌐 Real-World Case Study: Reproducing nature sustainability research on extreme heat risk in US carceral facilities using R and T-MAP
🏢 Identifying Threats to Validity: How students interrogated the coarse 4x4 resolution of climate data against localized prison coordinates
🏗️ Creating Public Products: Transforming messy research into living, citable web reports and digital archives with DOIs

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #OpenScience #GIS #Reproducibility #Rstats #SpatialAnalysis #DataScience #MiddleburyCollege #HigherEducation #ProjectBasedLearning #GIscience #GitHub #ResearchEthics #ProjectGeospatial

Drawing from his early career in the Oracle ecosystem to his global travels with the PostgreSQL community, Tim discusses how open-source collaboration fosters professional growth, diversity, and innovation. He highlights the ubiquity of PostgreSQL—often hidden behind the scenes of major ride-sharing, banking, and betting applications—and details how its flexibility, high-transaction performance, and spatial capabilities through PostGIS make it the database of choice for modern enterprise environments.

Highlights:
🛰️ The Meaning of Community: Moving beyond mere "user groups" to a true open-source community centered on trust, responsiveness, and collective help
🔄 Lessons from the DBA Quest: A storytelling look at a DBA’s journey through Oracle and MySQL before finding reliability and community at the gates of PostgreSQL
🧩 The Successful Village: Why a powerful community is the defining factor behind every successful open-source project and how it empowers individuals
⚙️ Bridging Corporate & Community: Using customer feedback to drive improvements in open-source software that benefit both businesses and the greater good
🌐 PostgreSQL Trend: A look at how major hyperscalers (Azure, Google, AWS) and startups alike are migrating to PostgreSQL for its extensible, high-transaction power
🏢 Hidden Geospatial Power: Real-world examples of PostgreSQL and PostGIS powering ATM services, ride-share routing, and state-regulated sports betting apps
🏗️ The Future of Leadership: Why leaders of the future are those who support others and the importance of active participation in the FOSS community

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #PostgreSQL #PostGIS #OpenSource #CommunityBuilding #DatabaseMigration #GIS #BigData #SoftwareDevelopment #ProjectGeospatial #Collaboration #TechInnovation #SoftwareMaintenance

The presentation details a sophisticated architecture that combines Knowledge Graphs for data discovery with Large Language Models (LLMs) for data analysis. By translating natural language queries into a structured, custom formal language, the system can autonomously navigate thousands of NetCDF files, perform complex spatial-temporal computations using Xarray and Dask, and deliver high-level insights directly to the user.

Highlights:
🛰️ The Missing Link: How AI and LLMs are finally connecting massive data silos with natural language user needs
🔄 Knowledge Graphs and Ontologies: Leveraging RDF and schema-based metadata to make NOAA's vast data catalogs discoverable
🧩 Virtual Zarr and Kerchunk: Transforming decades of legacy NetCDF files into high-performance, cloud-native virtual stores
⚙️ Structured Reasoning: Why 84.2 uses a custom formal language and Pydantic models instead of raw Python code generation for AI safety
🌐 Data Analysis Agents: A look at the multi-agent system that delegates tasks between data search, analysis, and user orchestration
🏢 Performance Optimization: Achieving a 40x speedup by storing Zarr metadata in Parquet format to handle massive time-series data
🏗️ Real-World Demos: Watch the system calculate sea surface temperature anomalies and compare temperatures across geographical regions in real-time

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #LLM #KnowledgeGraph #NOAA #AI #CloudNative #Xarray #Dask #NetCDF #Zarr #DataScience #MachineLearning #ProjectGeospatial #DataDiscovery

Thomas shares his firsthand experience at Planet, where managing custom GDAL versions and GPU-supported libraries like TensorFlow required hours of manual configuration. He demonstrates how Pixi automates this process by merging PyPI and Conda dependency trees into a single, reliable lock file, ensuring your local environment perfectly matches your CI/CD and production Docker images.

Highlights:
🛰️ What is Pixi? A deep dive into the "fusion of UV and Conda" that supports multiple platforms and languages
🔄 Beyond Python: Managing system-level binaries (like GDAL) that standard tools like UV or Poetry can't handle alone
🧩 Feature Sets: How to test your project against multiple GDAL versions (3.8 through 3.11) simultaneously using Pixi features
⚙️ Eliminating Makefiles: Using Pixi Tasks to centralize environment activation and CI/CD commands in one file
🌐 Docker Optimization: Building sleek, high-performance images by separating build essentials from runtime environments
🏢 Mono-repo Support: Managing conflicting dependencies and different Python versions within the same repository

For more content like this check out www.projectgeospatial.com

#Pixi #FOSS4G #Geospatial #Python #GDAL #Planet #OpenSource #SoftwareEngineering #Docker #Conda #PackageManagement #Rasterio #ProjectGeospatial #GIS #DevOps #DataScience

Calvin breaks down the anatomy of shapefiles—from the .shp and .dbf to the often-overlooked .shx, .prj, and .cpg files—and explains the engineering required to make them "cloud optimized." By implementing a QIX (spatial index) file and leveraging Hilbert curves for spatial ordering, he shows how to structure geospatial data so users only download the specific parts they need, significantly reducing data overhead and server requests.

Highlights:
🛰️ What is a Shapefile? A breakdown of the three to five files that make up the "yesterday's format of tomorrow"
🔄 Anatomy of Cloud Optimization: How byte-range requests allow for downloading specific parts of a file without the whole package
🧩 The Power of the QIX Index: Using tree-based nodes to skip irrelevant data and jump directly to spatial bounding boxes
⚙️ Big Endian vs. Little Endian: Navigating the "split the baby" compromise in shapefile data structures
🌐 The Hilbert Curve Advantage: Why space-filling curves are the superior method for keeping spatially close shapes adjacent in the file
🏢 Practical Implementation: A look at the open-source library and command-line tool for building and reordering cloud-optimized shapefiles
🏗️ Browser Hurdles: Addressing the specific Firefox bug that impacts byte-range requests on compressed files

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #Shapefile #CloudOptimized #GIS #WebMapping #OpenSource #DataEngineering #HilbertCurve #SanbornMapCompany #SoftwareDevelopment #ProjectGeospatial #VectorData #WebDev #SpatialAnalysis

Moving beyond typical corporate management, Bruce explains why traditional organizational structures often fail in open-source environments. He dives into the deep-seated motivations that drive developers to contribute—ranging from intellectual stimulation to a belief in helping others—and provides a roadmap for how leaders can foster a culture of ownership and pride in code.

Highlights:
🛰️ Transitioning to Leadership: Bruce’s journey from software engineering to leading one of the world's most successful open-source projects
🔄 Corporate vs. Open Source: Why managing volunteers requires a complete departure from enterprise-level leadership tactics
🧩 Understanding Motivation: A look at the six core forces that drive open-source participation, including career advancement and mental stimulation
⚙️ The Power of Physical Presence: Why getting on a plane to meet contributors in person is the ultimate tool for motivation
🌐 The Servant Leader Model: Why leadership in open source must be about serving the team rather than exercising authority
🏢 The 5% Rule: Understanding the small percentage of "true" open-source contributors and how to nurture them within a company
🏗️ Building Global Culture: Overcoming challenges in communication, language barriers, and 24-hour development cycles

For more content like this check out www.projectgeospatial.com

#PostgreSQL #OpenSource #FOSS4G #Leadership #SoftwareDevelopment #TechCommunity #ServantLeadership #OpenSourceManagement #ProjectGeospatial #TechLeadership #BruceMomjian #GIS #CommunityBuilding #GlobalTeams #SoftwareEngineering

The presentation introduces two open-source libraries: E84-GDAL-AI-Common, which provides core utilities for LLM interaction and structured data extraction, and Natural Language Geometry (NLG), a library that translates human-readable spatial descriptions—like "within 10 miles of the coast"—into precise GIS polygons. Jason also discusses the trade-offs between autonomous "agents" and deterministic "workflows," the importance of tracing and evaluation suites, and how Element 84 built their own geocoding database to overcome the relevancy and polygon-retrieval limitations of traditional tools.

Highlights:
🛰️ The Reliability Gap: Why rushing to add AI to geospatial tools can compromise software quality, security, and energy efficiency
🔄 Natural Language Geometry (NLG): How to convert vague spatial phrases into a structured tree of operations and final GIS polygons
🧩 Beyond Point Geocoding: The shift from center-point results to full polygon retrieval for complex areas like the Gulf of Mexico
⚙️ Why Build a New AI Library? Moving away from the complexity of LangChain toward the strong typing and simplicity of E84-GDAL-AI-Common
🌐 Agents vs. Workflows: When to use autonomous AI agents for unpredictable human queries and when to use prescriptive pipelines for efficiency
🏢 Evaluations & Tracing: Using graph edit distance and tools like LangFuse to measure LLM performance and identify token bottlenecks
🏗️ Custom Geocoding Databases: Leveraging Who’s On First and Natural Earth data to provide context-aware results for ambiguous place names

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #LLM #AI #GIS #NaturalLanguageProcessing #Geocoding #OpenSource #SoftwareEngineering #DataScience #Element84 #MachineLearning #ProjectGeospatial #LangChain #Pydantic

Drawing from over 20 years of experience in geospatial outreach, Christopher outlines a specialized "stack" of tools designed for users who may not be tech-savvy—from city clerks to retired volunteers. He explains how low-barrier tools like geojson.io, Tableau Public, and OpenStreetMap (OSM) can be used to unlock "hidden" community data, such as water usage records or local infrastructure needs, and turn them into actionable insights for local decision-making.

Highlights:
🛰️ Extension and Outreach: How Iowa State University bridges the gap between academic research and local community challenges
🔄 Data Science for the Public Good: Insights from an intensive program that trains students to work on state and government challenges in rural areas
🧩 The Low-Barrier Stack: Why tools like geojson.io and Tableau Public are ideal for entry-level users to map and share data
⚙️ Facilitated VGI: Using OpenStreetMap to help communities map missing infrastructure like childcare centers and new housing developments
🌐 Custom Imagery in OSM: How to leverage state-level orthophoto servers to update OpenStreetMap when commercial satellite imagery lags behind
🏢 Tableau Public for Communities: A deep dive into using the free version of Tableau to create interactive dashboards with spatial data
🏗️ Advanced Visualization: Using Inkscape and QGIS to create custom vector graphics and simplified geometry for web-based maps

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #DataLiteracy #OpenSource #GIS #IowaState #TableauPublic #OpenStreetMap #CommunityDevelopment #RuralData #Education #ProjectGeospatial #VGI #DataScience #PublicService

Tom and Stephen detail their approach to "comparable experiences," which prioritizes high-contrast visual design, structured text descriptions, and accessible keyboard controls over purely visual map interactions. They share practical implementation strategies using a FOSS stack—including Django, PostGIS, Leaflet, and MapServer—to generate dynamic, deterministic text summaries of spatial data, while also explaining why they currently opt for traditional database intersections over non-deterministic AI solutions for accessibility descriptions.

Highlights:
🛰️ The Accessibility Challenge: Why maps are inherently difficult to make accessible due to their visual nature and reliance on color
🔄 Comparable Experiences: Moving beyond simple alt-text to provide rich, text-based alternatives for complex geospatial data
🧩 WCAG & Legal Standards: Navigating the web content accessibility guidelines and state/federal laws like the ADA in California
⚙️ Color Contrast Matrix: A look at using outlines and numbered labels to ensure map features meet the 3:1 contrast ratio requirement
🌐 Deterministic Descriptions: Using PostGIS on the backend to create consistent, live text summaries of what is currently visible on the map
🏢 Keyboard-First Navigation: Implementing custom pan controls and tab-accessible map widgets to support users who don't use a mouse
🏗️ Human-Centric Design: Why manual description sprints and expert accessibility consultants remain vital for high-quality remediation

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #Accessibility #WebMapping #WCAG #ADA #GIS #OpenSource #InclusiveDesign #Leaflet #PostGIS #CaliforniaParks #ProjectGeospatial #UXDesign #WebDevelopment

The presentation introduces the concept of "pipelines," a powerful new way to chain geospatial operations—such as clipping, reprojecting, and simplifying—without the need for intermediate files on disk. Dan also details the new Python API mapping, enhanced tab-completion for complex options, and over 10 brand-new native C++ utilities for raster calculation, zonal stats, and geometry cleaning that were previously only available as Python scripts or separate libraries.

Highlights:
🛰️ The "Burn the Village" Moment: Why the GDAL team decided to build a completely new CLI to fix decades of inconsistent command arguments
🔄 Single-Purpose Tools: Breaking down complex utilities into specialized commands like gdal_raster_clip, gdal_raster_mosaic, and gdal_raster_repro
🧩 Introducing Pipelines: How to use the exclamation mark (!) character to chain multiple vector and raster steps into a single readable command
⚙️ Automated Python Mapping: A look at the new gdal.alg module that provides direct, native access to all CLI tools without using subprocesses
🌐 Enhanced Discoverability: Using tab-completion to find available formats, EPSG codes, and even files stored in cloud-based virtual file systems (S3/GCS)
🏢 Serialized Workflows: Using the new .gdal_alg.json format to record and share complex processing chains as virtual datasets
🏗️ New Native Capabilities: A showcase of new C++ tools for raster reclassification, focal stats, moving window averages, and polygon coverage repair

For more content like this check out www.projectgeospatial.com

#GDAL #FOSS4G #Geospatial #CLI #GIS #Python #Cplusplus #RasterData #VectorData #OpenSource #DataEngineering #ProjectGeospatial #SoftwareDevelopment #Pipelines #ZonalStats

Julia and Jarrett break down the "apples to oranges" comparison, explaining that while COG is a specialized specification built on top of the TIFF format, Zarr is a generic format for storing hierarchies of n-dimensional arrays. They explore the critical roles of chunking and compression codecs in data performance and demonstrate how, with identical settings, the bytes on disk for a Zarr chunk and a COG tile can be virtually the same. The talk also looks ahead to emerging Zarr conventions, such as GeoZarr and multi-scales, that aim to bring COG-like features to the Zarr ecosystem.

Highlights:
🛰️ Defining the Formats: A look at the structural differences between Cloud Optimized GeoTIFFs and the hierarchical array format of Zarr
🔄 Linearization and Chunking: How data is projected from multi-dimensional space to a single-dimension string of bytes on disk
🧩 Compression Codecs: A deep dive into the five types of codecs—entropy, predictive, structural, quantization, and mapping—used to optimize data storage
⚙️ The Faster Myth: Why performance differences are often due to default compression and chunking settings rather than the format itself
🌐 Interoperability vs. Multi-dimensionality: When to stick with the portability of TIFF and when to leverage the n-dimensional power of Zarr
🏢 Zarr Conventions: Understanding how new, community-driven specifications are breaking the deadlock for Zarr's geospatial adoption
🏗️ Virtual Zarr Stores: Using references to access legacy COG data through a modern Zarr interface without duplicating storage

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #Zarr #COG #GeoTIFF #CloudNative #DataEngineering #Compression #BigData #GIS #OpenSource #Element84 #Xarray #Kerchunk #ProjectGeospatial #DataAnalysis

The presentation highlights the technical difficulties of monitoring beach plants, which are often silvery or sand-covered, making standard greenness indices like NDVI ineffective. Michele tests alternative methods, including the "Cacti Index" adapted for Sentinel-2 data and topographic position indexing (TPI), while discussing the limitations of current public data resolution and the critical need for perfectly paired imagery and elevation datasets to track these rapidly changing environments.

Highlights:
🛰️ The Role of Beach Plants: Why these often-ignored plants are essential for natural coastal protection and dune building
🔄 The Monitoring Challenge: Why traditional field sampling is too slow, expensive, and physically demanding for daily beach changes
🧩 Data Constraints: The difficulty of finding paired imagery and Digital Elevation Models (DEMs) from the same temporal window
⚙️ Beyond NDVI: Why standard vegetation indices fail for succulent, reflective beach plants and the search for better spectral alternatives
🌐 Topographic Position Index (TPI): Using elevation data to identify dunes and other micro-topographic features on the sandy shore
🏢 The "Cacti Index" Experiment: Adapting desert remote sensing techniques to find silvery, high-reflectance coastal vegetation
🏗️ A Plea for Data: Why current public data resolution (10m-30m) is insufficient for beach management and the need for finer spectral bands

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #RemoteSensing #CoastalEcology #BeachPlants #DuneRestoration #Sentinel2 #Rstats #QGIS #OpenSource #DataScience #ProjectGeospatial #Conservation #ClimateResilience #EcologicalMonitoring

The session outlines an opportunity for conference presenters—whether they delivered talks, workshops, or posters—to publish their work in a dedicated special issue. Jessica explains the benefits of open-access publishing, including retaining copyright and reaching a global audience, while detailing a transparent, collaborative peer-review process designed to be constructive and practitioner-friendly. She also provides practical tips for first-time authors on structuring short "two-pager" articles and sharing data through open repositories.

Highlights:
🛰️ Bridging the Gap: Why open-source geospatial work often misses out on academic citation and how a special issue can help
🔄 Open Access vs. Traditional Publishing: A look at how flipping the subscription model makes information free for everyone while authors keep their copyright
🧩 The STAX Review Process: An inside look at a transparent, consensus-based peer-review system where reviewers work together with authors
⚙️ The "Two-Pager" Format: A low-barrier entry point for publishing 800- to 1,200-word articles that focus on what was done and why it matters
🌐 Citable Workflows: Transforming GitHub repos and workshop slides into durable, citable entries in the scholarly record
🏢 Data Stewardship: Best practices for sharing datasets through repositories like Zenodo, FigShare, and Dryad to ensure long-term reusability
🏗️ Support for Authors: How the FOSS4G academic committee is helping non-academics navigate the publishing process without the need for "academic-ese"

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #OpenAccess #AcademicPublishing #OpenSource #DataScience #PeerReview #STAXJournal #ResearchImpact #GIS #ProjectGeospatial #DigitalScholarship #OpenData #ScienceCommunication #TechnicalWriting

Brian explores the critical factors that drive enterprise software adoption, including user experience, security, and the "derisking" of technology for decision-makers. He offers a candid look at the importance of maintaining open-source infrastructure in a culture obsessed with the "new and shiny," and makes a bold prediction about the future of GIS in the age of AI.

Highlights:
🛰️ The "Victory" of Open Source: Reflecting on the journey from Victoria 2007 to PostgreSQL topping the Stack Overflow developer surveys
🔄 Selling "Free" to the Enterprise: Why leading with "free" often breeds suspicion in corporate boardrooms and how to pivot to value
🧩 User Experience Beyond GIS: Designing map interfaces for field workers with gloves on, rather than just for other GIS professionals
⚙️ The Enterprise Security Nightmare: Lessons from recent high-profile geospatial hacks and the paramount importance of data security
🌐 Data Quality in the Age of AI: How artificial intelligence might finally help solve the industry's persistent "suboptimal data" issues
🏢 The Maintenance Challenge: Navigating the countercultural impulse required to support mature, reliable software over long time horizons
🏗️ The Future of GIS: Why the "world's favorite GIS" by 2030 might be an AI model like Claude rather than traditional desktop software

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #OpenSource #EnterpriseSoftware #GIS #PostgreSQL #PostGIS #GDAL #SoftwareDevelopment #TechTrends #DataSecurity #UXDesign #ProjectGeospatial #AI #Innovation

The presentation details EarthScope's massive "lift and shift" migration from legacy on-premise infrastructure to a cloud-native environment in AWS, managing over a petabyte of open data. Sophia showcases "GeoLab," a specialized JupyterHub environment that allows researchers to process large-scale geophysical datasets directly in the cloud using tools like Dask, PyTorch, and Apache Arrow. She also demonstrates real-world applications of geophysics, such as using GNSS reflectometry to measure snowpack and soil moisture with millimeter precision.

Highlights:
🛰️ What is EarthScope? An overview of the consortium’s role in providing open access to seismic and geodetic instrumentation and data
🔄 Migration to AWS: Breaking down decades of Fortran-based processing into modern AWS Lambdas and S3-based workflows
🧩 GeoLab & JupyterHub: A deep dive into the cloud-based research environment offering specialized containers for seismic and GNSS analysis
⚙️ Modern Data Formats: How EarthScope is moving toward Apache Arrow and TileDB to enable high-speed, zero-copy data engineering
🌐 Global Seismic Picking: A look at how researchers used EarthScope’s petabyte-scale data to build a global earthquake catalog using machine learning
🏢 Hazard Monitoring: Using seismometers and GNSS stations to track everything from hurricane-induced ocean vibrations to ionospheric disturbances caused by tsunamis
🏗️ GNSS Reflectometry: Explaining how satellite signal noise ratios are used to measure surface deformation, snowpack, and vegetation moisture

For more content like this check out www.projectgeospatial.com

#Geophysics #FOSS4G #CloudComputing #AWS #EarthScope #Seismology #GNSS #JupyterHub #DataScience #OpenSource #OpenData #ProjectGeospatial #Dask #MachineLearning #NaturalHazards

The presentation details a multi-agent architecture that leverages Spatial Knowledge Graphs (SKGs) to model complex relationships and Discrete Global Grid Systems (DGGS) to normalize and aggregate statistical data. Nathan demonstrates how these technologies work together to provide traceable, deterministic answers to natural language questions—such as identifying which census areas are compromised by power failures during a flood—while emphasizing the importance of semantic identity and interoperable data publishing standards to avoid creating new information silos.

Highlights:
🛰️ The Scalability Challenge: Why AI alone can't magically integrate spatial data and the need for upfront interoperability in National Spatial Data Infrastructures
🔄 Discrete Global Grid Systems (DGGS): An introduction to the emerging OGC standard that quantizes the world into uniform zones for efficient data normalization
🧩 Spatial Knowledge Graphs: Moving from feature-type centric tables to relationship-centric graphs that Large Language Models can traverse and understand
⚙️ Agentic AI Tools: Using the Model Context Protocol (MCP) to expose data endpoints such as DGGS servers and triple stores to autonomous AI agents
🌐 Disaster Resiliency Demo: Watch a real-world prototype query flood levels near Winnipeg and automatically correlate power infrastructure with census boundaries
🏢 Beyond Silos: The vision for "No STAC"—transforming SpatioTemporal Asset Catalogs into interoperable knowledge graphs for cross-agency data discovery
🏗️ Avoiding Monoliths: Why the goal should be a standardized approach for publishing federated graphs rather than "one big graph to rule them all"

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #GeoAI #KnowledgeGraphs #DGGS #DisasterResiliency #AI #OpenSource #DataEngineering #STAC #OGC #TerraFrame #ProjectGeospatial #MachineLearning #SemanticWeb

Jarrett shares the project's journey from its roots in NASA's Cumulus project to its current state as a lightweight, STAC-native orchestrator used to manage over 30 petabytes of data. He provides a candid look at the technical "pendulum swings" the project has experienced, including the bold decision to remove all infrastructure-as-code dependencies and serverless framework logic in response to licensing changes, while distilling five years of development into three key lessons on funding, project vision, and managing technical debt.

Highlights:
🛰️ What is Cirrus? An introduction to the data orchestration component of Element 84's Filmpop suite for building geospatial data lakes
🔄 The Evolution of STAC Workflows: How Cirrus uses STAC items as the primary message format to track state across distributed processing tasks
🧩 Scaling to Petabytes: Managing massive archives like Sentinel-2 with a system that handles 30,000+ items a day and nearly 80 million total items
⚙️ The Serverless Redesign: Why Element 84 deleted 4,400 lines of code to remove closed-source build dependencies and move toward a "bring-your-own-IaC" model
🌐 Lessons in Open Source: Reflections on how funding sources, project vision, and inevitable failures shape the long-term success of a software project
🏢 From Cirrus to Swoop: A look at a cloud-agnostic "meta-orchestrator" experiment designed for Kubernetes and Argo Workflows
🏗️ Operational Reliability: Why making failures cheap, visible, and easy to resolve is a core design principle for high-volume data pipelines

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #DataOrchestration #STAC #Cirrus #OpenSource #AWS #CloudNative #DataEngineering #Python #Serverless #Element84 #ProjectGeospatial #DataLake #SoftwareDevelopment

The presentation compares traditional "static" (JSON blobs on blob storage) and "dynamic" (API/database-backed) access patterns, highlighting where each falls short when handling millions of items. Pete demonstrates how STAC GeoParquet provides a middle ground—enabling bulk metadata access, efficient full-scans, and direct-to-file searching without the overhead of a database. He also showcases "stac-map," a browser-based visualizer that uses DuckDB to filter and display GeoParquet files directly in the browser, and discusses how organized metadata snapshots can be used to feed and train Large Language Models (LLMs).

Highlights:
🛰️ The STAC Journey: A brief history of the SpatioTemporal Asset Catalog and its recent adoption as an OGC Community Standard
🔄 Static vs. API Patterns: A critical look at the trade-offs between cheap, simple blob storage and powerful but complex database-backed APIs
🧩 Enter STAC GeoParquet: How converting JSON metadata to a columnar format results in massive storage savings—from 21MB down to 200KB for 10,000 items
⚙️ Performance at Scale: Real-world results from a 10-million-item glacier dataset, showing how sorted GeoParquet files can reduce search times from 187 seconds to just 3 seconds
🌐 Universal Compatibility: Why using Parquet allows non-geospatial tools like DuckDB to analyze STAC metadata without specialized GIS libraries
🏢 The "One STAC" Vision: Moving toward a world where every database provides a GeoParquet snapshot for easier crawling and cross-catalog discovery
🏗️ STAC-Map Demo: A look at a new public tool for snapshotting STAC searches and uploading bundles for interactive browser-based filtering

For more content like this check out www.projectgeospatial.com

#Geospatial #FOSS4G #STAC #GeoParquet #Metadata #CloudNative #BigData #GIS #OpenSource #DuckDB #DataEngineering #Element84 #ProjectGeospatial #DataDiscovery #Xarray