Organizations have used geospatial machine studying (ML) for property danger evaluation, catastrophe response, and infrastructure planning. These methods labored properly however couldn’t scale past specialised use circumstances. Every query required a number of geospatial datasets, every with its personal mannequin and sometimes its personal workflow, limiting these capabilities to a handful of high-value use circumstances on the largest enterprises that might afford the funding. On this put up, you’ll discover ways to deploy geospatial AI brokers that may reply complicated spatial questions in minutes as a substitute of months. By combining Foursquare Spatial H3 Hub’s analysis-ready geospatial information with reasoning fashions deployed on Amazon SageMaker AI, you’ll be able to construct brokers that allow nontechnical area specialists to carry out refined spatial evaluation by means of pure language queries—with out requiring geographic info system (GIS) experience or customized information engineering pipelines.
Geospatial intelligence adoption obstacles
Two technical obstacles have prevented these specialised geospatial methods from attaining broader adoption. First, geospatial information arrives in a bewildering array of codecs—satellite tv for pc imagery saved as GeoTIFF rasters, administrative boundaries saved as shapefile vectors, climate fashions saved as NetCDF grids, and property data in proprietary cadastral codecs—every requiring completely different parsing libraries and customized information pipelines. Second, becoming a member of datasets throughout spatial granularities is nontrivial: property insurance coverage information geocoded to particular person addresses should mix with local weather danger information at 1 km grid cells and census demographics aggregated to dam teams, requiring organizations to spend months constructing customized processing pipelines earlier than answering their first enterprise query. Briefly, there isn’t any common be a part of key to mix these datasets. This implies organizations can’t experiment with geospatial intelligence with out first constructing information engineering pipelines to normalize various codecs, implement spatial processing for coordinate transformations and backbone resampling, and deploy specialised computing infrastructure.
Fixing technical obstacles alone wasn’t adequate. Earlier methods nonetheless required 6–12 month implementations with specialised GIS groups. 5 enterprise necessities remained unaddressed: making geospatial evaluation accessible to nontechnical area specialists, displaying how AI reaches conclusions, supporting versatile evaluation, delivering interactive response occasions, and providing value predictability at scale.
Three applied sciences converging to deal with adoption challenges
Addressing these technical and enterprise obstacles requires a basically completely different method. This structure combines three applied sciences to deal with these gaps:
- Foursquare Spatial H3 Hub for analysis-ready information – This service transforms inaccessible raster and vector geospatial information into analysis-ready options, listed to the H3 hierarchical grid system, in tabular format that information scientists can question utilizing acquainted instruments corresponding to Spark, Python, and DuckDB. Datasets containing latitude and longitude coordinates, metropolis names, or zip codes might be simply enriched by becoming a member of on a typical H3 cell, eliminating months of knowledge preparation and specialised GIS experience.
- Reasoning fashions and agentic AI for adaptive workflows – Fashions corresponding to DeepSeek-R1 and Llama 3 break down complicated issues, motive by means of multistep workflows, and orchestrate actions throughout information sources. They dynamically decide which datasets to mix and plan analytical sequences that beforehand required GIS experience—remodeling static, preconfigured workflows into adaptive reasoning methods.
- Amazon SageMaker AI for cost-effective generative AI inference – This Amazon SageMaker AI functionality offers managed infrastructure for deploying open supply fashions with optimized inference runtimes, auto scaling, and operational tooling. Groups can concentrate on constructing geospatial intelligence capabilities quite than managing underlying infrastructure.
Collectively, these applied sciences allow organizations to entry analysis-ready geospatial information, deploy adaptive reasoning brokers, and run manufacturing inference with out constructing specialised infrastructure. On this put up, we show a manufacturing geospatial agent that mixes Foursquare Spatial H3 Hub with reasoning fashions deployed on Amazon SageMaker AI.
Evaluation-ready geospatial information with Foursquare Spatial H3 Hub
Foursquare’s Spatial H3 Hub eliminates conventional geospatial adoption obstacles by means of a proprietary H3 indexing engine. This engine has remodeled dozens of disparate geospatial datasets into an Iceberg catalog prepared for instant evaluation, changing months of knowledge engineering with immediate entry to analysis-ready geospatial options.
The H3 indexing engine addresses the foundation reason behind geospatial complexity: the huge array of codecs and coordinate methods which have traditionally restricted entry to geographic info. The engine converts spatial information, raster imagery, or vector datasets by indexing it into the H3 hierarchical spatial grid at international scale. H3 divides all the Earth into nested hexagonal cells, making a common grid system the place each location has a standardized identifier. The engine extracts information from raster pictures or various vector shapes corresponding to census tract polygons and converts them into options connected to H3 cell IDs in tabular format, the place the cell ID turns into a common be a part of key that abstracts away format complexity and coordinate methods. An insurance coverage firm’s property information, Nationwide Oceanic and Atmospheric Administration (NOAA) local weather projections, census demographics, and infrastructure networks can all be mixed as a result of they share this widespread spatial index.
The engine additionally handles the methodological complexities that historically required GIS experience. It might probably index information to H3 cells at any precision from decision 0 (about 1,000 km hexagons protecting continents) all the way down to decision 15 (about 1 meter hexagons protecting particular person buildings). You possibly can select the suitable decision for every use case—coarser resolutions for regional local weather evaluation, finer resolutions for property-level evaluation. When boundaries don’t align completely—like a census tract overlapping a number of H3 hexagons—the engine intelligently handles partial overlaps by means of both quick centroid-based approximation or precise proportional allocation primarily based on intersection areas. It additionally robotically aggregates or disaggregates information when combining datasets at completely different scales, eliminating the guide preprocessing that historically consumed months of GIS specialist time.
Constructed on this indexing basis, Foursquare Spatial H3 Hub delivers an Iceberg catalog containing datasets spanning vitality infrastructure, environmental situations, and pure hazards all initially in various raster and vector codecs, now pre-indexed to H3 cells at decision 8 (with extra resolutions obtainable on demand). You possibly can question this information with acquainted instruments corresponding to SQL, Python, Spark, Snowflake, and Databricks with out proprietary GIS software program. H3 cell identifiers turn into easy column values that be a part of like every other attribute, so you’ll be able to quickly validate geospatial hypotheses by becoming a member of their proprietary information with Foursquare’s H3 catalog.
Reasoning fashions for spatial Intelligence
Reasoning fashions corresponding to DeepSeek-R1 change how AI handles geospatial intelligence. Conventional geospatial methods operated as collections of static, purpose-built fashions, with separate fashions for flood danger, wildfire publicity, and earthquake vulnerability. Every mannequin was skilled on particular datasets and incapable of answering questions outdoors its slender area. When necessities shifted or new information emerged, organizations confronted months of retraining. Reasoning fashions change this paradigm by decomposing complicated issues, planning multistep workflows, and orchestrating actions throughout information sources dynamically. Fairly than requiring pre-trained fashions for each query, these methods motive by means of novel situations by combining obtainable information in methods by no means explicitly programmed. Requested “which neighborhoods face compounding local weather and financial dangers?”, a reasoning agent determines it wants flood publicity information, family earnings, property density, and neighborhood boundaries after which executes that analytical pipeline by calling applicable instruments and information sources. The agent understands spatial relationships conceptually: level information aggregates to polygons, grid cells map to administrative boundaries, proximity requires applicable distance metrics. At every step, it causes about what info comes subsequent and adjusts when information reveals sudden patterns, remodeling geospatial evaluation from pre-scripted queries into adaptive investigation.
Deploying brokers on Amazon SageMaker AI
Evaluation-ready geospatial information and reasoning-capable fashions remedy crucial elements of the puzzle, however manufacturing deployment creates new challenges. Geospatial brokers want sustained inference capability to course of queries, execute reasoning chains, retrieve information, and generate visualizations. Organizations face a selection: construct customized inference infrastructure with GPU clusters, load balancers, and auto scaling insurance policies, or depend on business massive language mannequin (LLM) APIs the place prices scale unpredictably with utilization and information governance turns into complicated.
Amazon SageMaker AI offers managed infrastructure for deploying and working open supply generative AI fashions in manufacturing. You possibly can deploy fashions from Hugging Face or Amazon SageMaker AI JumpStart—together with reasoning fashions corresponding to DeepSeek-R1, Llama 3, or Qwen—to SageMaker AI real-time or asynchronous inference endpoints with out managing underlying infrastructure. Amazon SageMaker AI Inference handles occasion provisioning, helps optimized serving runtimes like vLLM and SGLang, and offers auto scaling primarily based on visitors patterns.
Amazon SageMaker AI Inference capabilities tackle a number of operational challenges particular to agent architectures. Geospatial brokers dealing with variable question masses all through the day profit from automated scaling on GPU cases corresponding to G5, P4d, and P5 primarily based on request quantity or customized metrics. Lengthy-running spatial analyses that exceed typical API timeouts can path to asynchronous inference endpoints the place SageMaker AI queues request, course of them, and ship outcomes to Amazon Easy Storage Service (Amazon S3), enabling complicated multi-dataset analyses with out client-side timeout points. For architectures using a number of fashions, multi-container endpoints host completely different fashions on shared infrastructure with impartial scaling insurance policies and visitors routing. Constructed-in integration with Amazon CloudWatch for monitoring, AWS Id and Entry Administration (IAM) for entry management, and Amazon Digital Non-public Cloud (Amazon VPC) for community isolation simplifies operational necessities.
Foursquare Spatial H3 Hub and Amazon SageMaker AI collectively scale back operational complexity. Information scientists can concentrate on constructing agent capabilities, defining which H3 Hub datasets to question for particular questions, refining prompting methods for spatial reasoning, and optimizing tool-calling patterns quite than managing underlying infrastructure. Organizations also can experiment with completely different open supply fashions. Such initiatives, which beforehand required separate groups for information engineering, mannequin improvement, and platform operations, have now turn into accessible to smaller groups with out specialised infrastructure experience.
Designing the Foursquare Spatial Agent
The Foursquare Spatial Agent structure combines reasoning fashions deployed on SageMaker AI with tool-calling capabilities that question Foursquare Spatial H3 Hub instantly. The agent orchestrates the entire workflow from pure language query to visualization with out guide intervention.
Agent workflow
When a consumer poses a pure language query about spatial relationships—corresponding to “Which neighborhoods in Los Angeles face each excessive flood danger and financial vulnerability?”—the agent executes a multistep reasoning course of. The reasoning mannequin first analyzes the query and identifies required info: flood danger scores, financial indicators like earnings and employment, and neighborhood boundaries. It then determines which H3 Hub datasets comprise related info by reasoning over dataset descriptions. With datasets chosen, the mannequin calls H3 Hub question instruments, establishing SQL queries that be a part of datasets on H3 cell IDs. After executing these queries, the mannequin analyzes outcomes to establish spatial patterns and statistical relationships. Lastly, it generates Vega specs for charts and Kepler.gl specs for maps that visualize the findings.
This workflow makes use of the reasoning mannequin’s capacity to plan, adapt, and get well from errors. If preliminary queries return sudden outcomes, the mannequin can refine its method, choose extra datasets, or alter spatial operations—capabilities of that static, preprogrammed workflow.
Design selections addressing enterprise necessities
Constructing a manufacturing geospatial agent required addressing the 5 enterprise necessities recognized by means of deployment evaluation. Three key design selections illustrate how the structure balances accessibility, transparency, and adaptability.
Insurance coverage underwriters perceive flood danger and property publicity however don’t write SQL or Python. The agent structure makes geospatial evaluation accessible by accepting pure language questions and translating them into applicable H3 Hub queries. The reasoning mannequin interprets domain-specific terminology like “weak neighborhoods” or “high-risk areas” and maps these ideas to related datasets and analytical operations. This eliminates the bottleneck the place area specialists should submit evaluation requests to information groups, enabling self-service exploration.
Area specialists additionally want to know how the agent arrived at conclusions, particularly when analyses inform enterprise selections. The agent can log its reasoning course of at every step: which datasets had been thought-about and why, what spatial operations had been deliberate, which queries had been executed, and the way outcomes had been interpreted. Each visualization contains metadata displaying which H3 cells and supply datasets contributed to the evaluation. This transparency means customers can validate the agent’s analytical method and perceive the information sources behind conclusions. If an insurance coverage underwriter sees a high-risk evaluation for a property, they’ll hint again by means of the reasoning chain to see it mixed flood publicity information from Federal Emergency Administration Company (FEMA), wildfire danger from state forestry information, and property traits from native assessor data—constructing confidence in AI-generated insights. Implementation makes use of structured logging to seize reasoning steps, making the agent’s decision-making course of inspectable and debuggable quite than a black field.
Pre-built dashboards serve identified questions however fail when analysts must discover variations. The agent structure offers flexibility through the use of tool-calling to dynamically compose analyses. Fairly than predefining workflows for each situation, the reasoning mannequin determines which H3 Hub datasets to question and the right way to mix them primarily based on the precise query. This allows the agent to deal with unexpected analytical questions with out requiring new engineering work for every variation. The agent makes use of perform calling APIs supported by fashions corresponding to Llama 3 and DeepSeek-R1 to work together with H3 Hub. The mannequin receives device descriptions specifying obtainable datasets, question parameters, and return codecs, then constructs applicable device calls throughout reasoning. SageMaker AI endpoints deal with the inference, whereas customized software logic manages device execution and consequence meeting.
SageMaker AI deployment structure
The Foursquare Spatial Agent deploys on SageMaker AI real-time inference endpoints with configuration optimized for manufacturing geospatial workloads. The deployment makes use of G5 cases corresponding to g5.2xlarge for improvement and g5.12xlarge for manufacturing, offering cost-effective GPU inference for fashions within the 7B–70B parameter vary generally used for agent reasoning. A goal monitoring scaling coverage primarily based on the InvocationsPerInstance metric maintains response occasions throughout variable load whereas minimizing prices throughout low-traffic intervals. Spatial analyses involving massive geographic extents or many datasets be a part of path to asynchronous inference endpoints, permitting queries that may take 60 seconds or extra to finish with out exceeding typical API timeout limits whereas sustaining responsive conduct for extra easy queries.
CloudWatch metrics observe inference latency, error charges, and token throughput throughout the deployment. Customized metrics log reasoning chain depth, variety of device calls per question, and dataset entry patterns, enabling steady optimization of agent efficiency. This deployment structure offers production-grade reliability whereas sustaining flexibility for experimentation with completely different fashions and prompting methods.
Foursquare Spatial Agent in motion
The next demonstrations present how organizations throughout insurance coverage, banking, and concrete planning can use this functionality to reply complicated spatial questions in minutes—collapsing timelines that beforehand stretched throughout quarters into interactive workflows accessible to area specialists with out specialised technical expertise. In insurance coverage danger evaluation, the agent predicts which areas within the Los Angeles area are more likely to witness elevated insurance coverage charges by computing a composite danger rating from flood danger, fireplace hazard severity, crime charges and the FEMA nationwide danger index datasets at completely different spatial resolutions and codecs, now queryable by means of widespread H3 cell IDs. An underwriter asks the query in pure language, and the agent handles dataset choice, spatial joins, danger aggregation, and map visualization with out requiring GIS experience.
For banking market evaluation, the agent offers a 360-degree view of Los Angeles’s financial institution community planning. It combines demographic information together with inhabitants, earnings, and age distribution with healthcare facility places, crime statistics, and factors of curiosity to establish under-served markets and enlargement alternatives. This evaluation informs data-driven selections for department placement, product focusing on, and monetary inclusion initiatives. Beforehand, assembling these datasets and performing spatial evaluation required weeks of GIS specialist time. Now, the agent delivers leads to minutes by means of conversational interplay.
For city infrastructure planning, the agent helps town of Chandler, Arizona, plan sustainable city improvement over the subsequent decade. It combines inhabitants development projections, housing improvement patterns, median earnings developments, and infrastructure information together with buildings, energy strains, and cell towers—all listed to H3 cells. City planners discover situations by asking questions like “which areas will expertise inhabitants development however lack enough infrastructure?” The agent causes by means of the analytical necessities, executes applicable spatial queries, and generates visualizations displaying infrastructure gaps that want funding.
The democratization of geospatial intelligence
Foursquare Spatial H3 Hub, reasoning fashions, and Amazon SageMaker AI collectively take away the obstacles. Organizations can now entry standardized geospatial information, deploy reasoning brokers with tool-calling capabilities, and run manufacturing inference with out constructing specialised infrastructure.
To deploy geospatial AI brokers:
- Entry Foursquare Spatial H3 Hub for analysis-ready datasets.
- Deploy reasoning fashions on Amazon SageMaker AI with SageMaker JumpStart or Hugging Face.
- Construct agent capabilities that join fashions to H3 Hub datasets by means of tool-calling.
Concerning the authors
Vikram Gundeti at the moment serves because the Chief Know-how Officer (CTO) of Foursquare, the place he leads the technical technique, choice making, and analysis for the corporate’s Geospatial Platform. Earlier than becoming a member of Foursquare, Vikram held the place of Principal Engineer at Amazon, the place he made his mark as a founding engineer on the Amazon Alexa crew.
Amit Modi is a Senior Supervisor of Product Administration at Amazon SageMaker AI, the place he focuses on ModelOps and Inference. His evaluation of enterprise adoption patterns and design of the SageMaker deployment method described on this put up emerged from work with enterprise clients.
Aditya Badhwar is a Senior Options Architect at AWS primarily based out of New York. He works with clients offering technical help and architectural steering on numerous AWS providers. Previous to AWS, Aditya labored for over 16 years in software program engineering and structure roles for numerous large-scale enterprises.
