Organizations have used geospatial machine studying (ML) for property threat evaluation, catastrophe response, and infrastructure planning. These methods labored effectively however couldn’t scale past specialised use circumstances. Every query required a number of geospatial datasets, every with its personal mannequin and infrequently 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 subtle spatial evaluation by means of pure language queries—with out requiring geographic information system (GIS) experience or customized information engineering pipelines.
Geospatial intelligence adoption obstacles
Two technical obstacles have prevented these specialised geospatial methods from reaching 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 information 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 threat 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 is no such thing as a 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 determination resampling, and deploy specialised computing infrastructure.
Fixing technical obstacles alone wasn’t enough. 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 essentially 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 akin to Spark, Python, and DuckDB. Datasets containing latitude and longitude coordinates, metropolis names, or zip codes may 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 akin 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 give attention to constructing geospatial intelligence capabilities slightly 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 quick evaluation, changing months of knowledge engineering with on the spot entry to analysis-ready geospatial options.
The H3 indexing engine addresses the foundation explanation for 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 world scale. H3 divides your entire Earth into nested hexagonal cells, making a common grid system the place each location has a standardized identifier. The engine extracts information from raster photographs or various vector shapes akin to census tract polygons and converts them into options hooked up 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 frequent spatial index.
The engine additionally handles the methodological complexities that historically required GIS experience. It may index information to H3 cells at any precision from decision 0 (about 1,000 km hexagons masking continents) all the way down to decision 15 (about 1 meter hexagons masking 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 actual proportional allocation based mostly on intersection areas. It additionally routinely aggregates or disaggregates information when combining datasets at completely different scales, eliminating the handbook 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 power infrastructure, environmental circumstances, and pure hazards all initially in various raster and vector codecs, now pre-indexed to H3 cells at decision 8 (with extra resolutions accessible on demand). You possibly can question this information with acquainted instruments akin to SQL, Python, Spark, Snowflake, and Databricks with out proprietary GIS software program. H3 cell identifiers grow to be simple 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 akin 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 threat, wildfire publicity, and earthquake vulnerability. Every mannequin was educated on particular datasets and incapable of answering questions exterior its slim 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. Relatively than requiring pre-trained fashions for each query, these methods motive by means of novel eventualities by combining accessible 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 acceptable instruments and information sources. The agent understands spatial relationships conceptually: level information aggregates to polygons, grid cells map to administrative boundaries, proximity requires acceptable distance metrics. At every step, it causes about what info comes subsequent and adjusts when information reveals surprising 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 clear up essential 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 giant 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 akin 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 based mostly on site visitors patterns.
Amazon SageMaker AI Inference capabilities handle a number of operational challenges particular to agent architectures. Geospatial brokers dealing with variable question hundreds all through the day profit from computerized scaling on GPU cases akin to G5, P4d, and P5 based mostly 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 site visitors routing. Constructed-in integration with Amazon CloudWatch for monitoring, AWS Identification 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 cut back operational complexity. Information scientists can give attention to constructing agent capabilities, defining which H3 Hub datasets to question for particular questions, refining prompting methods for spatial reasoning, and optimizing tool-calling patterns slightly than managing underlying infrastructure. Organizations can even experiment with completely different open supply fashions. Such initiatives, which beforehand required separate groups for information engineering, mannequin growth, and platform operations, have now grow to be 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 straight. The agent orchestrates the entire workflow from pure language query to visualization with out handbook intervention.
Agent workflow
When a consumer poses a pure language query about spatial relationships—akin to “Which neighborhoods in Los Angeles face each excessive flood threat and financial vulnerability?”—the agent executes a multistep reasoning course of. The reasoning mannequin first analyzes the query and identifies required info: flood threat 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, setting up SQL queries that be a part of datasets on H3 cell IDs. After executing these queries, the mannequin analyzes outcomes to determine 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 recuperate from errors. If preliminary queries return surprising outcomes, the mannequin can refine its method, choose extra datasets, or modify spatial operations—capabilities of that static, preprogrammed workflow.
Design choices addressing enterprise necessities
Constructing a manufacturing geospatial agent required addressing the 5 enterprise necessities recognized by means of deployment evaluation. Three key design choices illustrate how the structure balances accessibility, transparency, and adaptability.
Insurance coverage underwriters perceive flood threat 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 acceptable H3 Hub queries. The reasoning mannequin interprets domain-specific terminology like “susceptible 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 choices. The agent can log its reasoning course of at every step: which datasets had been thought of 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 threat from state forestry information, and property traits from native assessor information—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 slightly than a black field.
Pre-built dashboards serve identified questions however fail when analysts have to discover variations. The agent structure offers flexibility through the use of tool-calling to dynamically compose analyses. Relatively than predefining workflows for each state of affairs, the reasoning mannequin determines which H3 Hub datasets to question and easy methods to mix them based mostly on the particular query. This permits 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 akin to Llama 3 and DeepSeek-R1 to work together with H3 Hub. The mannequin receives software descriptions specifying accessible datasets, question parameters, and return codecs, then constructs acceptable software calls throughout reasoning. SageMaker AI endpoints deal with the inference, whereas customized software logic manages software execution and outcome 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 akin to g5.2xlarge for growth 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 based mostly on the InvocationsPerInstance metric maintains response occasions throughout variable load whereas minimizing prices throughout low-traffic intervals. Spatial analyses involving giant 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 simple queries.
CloudWatch metrics monitor inference latency, error charges, and token throughput throughout the deployment. Customized metrics log reasoning chain depth, variety of software 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 abilities. In insurance coverage threat evaluation, the agent predicts which areas within the Los Angeles area are prone to witness elevated insurance coverage charges by computing a composite threat rating from flood threat, fireplace hazard severity, crime charges and the FEMA nationwide threat index datasets at completely different spatial resolutions and codecs, now queryable by means of frequent H3 cell IDs. An underwriter asks the query in pure language, and the agent handles dataset choice, spatial joins, threat 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 areas, crime statistics, and factors of curiosity to determine under-served markets and growth alternatives. This evaluation informs data-driven choices for department placement, product concentrating on, and monetary inclusion initiatives. Beforehand, assembling these datasets and performing spatial evaluation required weeks of GIS specialist time. Now, the agent delivers ends in minutes by means of conversational interplay.
For city infrastructure planning, the agent helps town of Chandler, Arizona, plan sustainable city growth over the following decade. It combines inhabitants progress projections, housing growth patterns, median earnings developments, and infrastructure information together with buildings, energy strains, and cell towers—all listed to H3 cells. City planners discover eventualities by asking questions like “which areas will expertise inhabitants progress however lack ample infrastructure?” The agent causes by means of the analytical necessities, executes acceptable 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.
In regards to the authors
Vikram Gundeti at the moment serves because the Chief Expertise 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 group.
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 based mostly out of New York. He works with clients offering technical help and architectural steerage on varied AWS providers. Previous to AWS, Aditya labored for over 16 years in software program engineering and structure roles for varied large-scale enterprises.
