Automating Map Production With FME and Python

Jun 6, 20250 likes31 views

People still love a good paper map, but every time a request lands on a GIS team’s desk, it takes time to create that perfect, individual map—even when you're ready and have projects prepped. Then come the inevitable changes and iterations that add even more time to the process. This presentation explores a solution for automating map production using FME and Python. FME handles the setup of variables, leveraging GIS reference layers and parameters to manage details like map orientation, label sizes, and layout elements. Python takes over to export PDF maps for each location and template size, uploading them monthly to ArcGIS Online. The result? Fresh, regularly updated maps, ready for anyone to grab anytime—saving you time, effort, and endless revisions while keeping users happy with up-to-date, accessible maps.

Automating
Map Production
with FME and
Python
Locus
The Peak of Data
and AI 2025

2025
The
Peak
of
Data
and
AI
Pip Norris
Principal Data Professional
Locus

The Problem
Why generating manual maps slows us down
The Solution
FME + Python working together
How It Works
Key steps in the workﬂow
The Impact
What we achieved
Agenda

2025
The
Peak
of
Data
and
AI
Today I’m going to show you how we
automated map generation using FME
and Python - saving time, reducing
manual work, and delivering fresh,
consistent maps to users every month.

The Map Request
Bottleneck
● Time-consuming manual process
● Repeated map requests = repeated work
● Endless updates, versioning headaches
and GIS teams stretched thin

2025
The
Peak
of
Data
and
AI
What if you could automate it?

• Automate layout + export
• Standardize variables (scale, label
size, etc.)
• Reduce turnaround time drastically
Manual to Magical

2025
The
Peak
of
Data
and
AI
FME and Python
● Parameters
● Layout | Logic
● GIS layers
● Batch export to PDF
● Automate uploads to ArcGIS
Online

2025
The
Peak
of
Data
and
AI
The Workﬂow
Deﬁne
parameters
in FME
Use GIS
reference
layers
Run Python
script
Export PDFs
Upload to
ArcGIS
Online

2025
The
Peak
of
Data
and
AI
The Workspace

2025
The
Peak
of
Data
and
AI
The Final Product

2025
The
Peak
of
Data
and
AI
The Automation

2025
The
Peak
of
Data
and
AI
Results you can map
● Maps updated automatically
● No more repeat work
● Happy team!
The Resul

2025
The
Peak
of
Data
and
AI
Scale your impact - not
your effort.

2025
The
Peak
of
Data
and
AI
ThankYou
Pip Norris
Locus
pip.norris@locus.co.nz

Efficient data migration is a critical challenge in geospatial data management, especially when working with complex data structures. This presentation explores an automated approach to migrating ESRI Geodatabases using FME and XML-based control files. A key advantage of this method is its adaptability: changes to the data model are seamlessly incorporated into the migration process without requiring modifications to the underlying FME workflow. By separating data model definitions from migration logic, this approach ensures flexibility, reduces maintenance effort, and enhances scalability.

Modernizing Vermont's Cyanobacteria Tracking ProgramSafe Software

The Cyanobacteria Tracking Program in Vermont is a collaborative effort between the Departments of Health (VDH) and Environmental Conservation (DEC), and the Lake Champlain Committee. Since 2003, trained volunteers, staff, and citizens have regularly monitored Vermont's bays, lakes, and ponds during the cyanobacteria season. This crucial information is shared with the public through the state's Cyanobacteria Tracker Map. In 2024, with new FME Flow licensing, the VDH GIS and Data Viz team overhauled a critical component: real-time data synchronization and the notification system. Using FME Flow, we can now monitor reports in near real-time, alert staff of new blooms, and flag potential errors in the data. Several enhancements have also been implemented this season. We can now monitor blooms near drinking water intakes and notify DEC staff. Another exciting enhancement is the integration with our Cooling Sites map, an interactive tool that helps the public find places to cool down on hot days. This map features cool indoor locations and parks with swimming access. However, some beaches may be closed due to blooms or elevated E. coli levels. Thanks to FME, we can now integrate these key pieces of information and update our Cooling Sites map with beach closure information in real-time. FME has made this real-time data integration not only possible but also easy to implement and manage. We are excited to continue modernizing our data systems using FME.

Leveraging Geo-Data for Sustainable Watercourse ManagementSafe Software

The Anti-Masterclass Live - Peak of Data & AI 2025Safe Software

Mastering Compliance in ArcGIS with AI: Are you Really in Control?Safe Software

A few years ago, a small incident in our production environment made us pause and reflect: Were people actually following our working agreements? We had accidentally published data from a production file share as an editable development service. What seemed like a one‑off mistake turned out to be a real ‘can of worms.’ Instead of just reacting to issues, we decided to focus on proactively preventing them. Let’s be honest: manually checking whether all working agreements are followed is nearly impossible — especially in environments with many users constantly creating and modifying data. You might think things are under control, but mistakes happen — especially under pressure. So, we started looking for a more foolproof way to automatically check whether working agreements are being followed and to inform users when they’re not. In this session, I’ll show how we use FME Flow automations to monitor every change in real time — from creating items to updating sharing settings. We can dynamically validate whether all working agreements are being followed, tailored to each client and environment. We’ve also integrated AI to detect potential data leaks early on. This has already helped us clean up over 250,000 sensitive records across multiple organizations. Curious what this could mean for your organization? Whether you’re using ArcGIS software, managing it as an admin, or safeguarding data as a privacy officer — come see what’s possible when you start connecting data across your applications.

Powering Multi-Page Web Applications Using Flow Apps and FME Data StreamingSafe Software

Unleash the potential of FME Flow to build and deploy advanced multi-page web applications with ease. Discover how Flow Apps and FME’s data streaming capabilities empower you to create interactive web experiences directly within FME Platform. Without the need for dedicated web-hosting infrastructure, FME enhances both data accessibility and user experience. Join us to explore how to unlock the full potential of FME for your web projects and seamlessly integrate data-driven applications into your workflows.

Improving Data Integrity: Synchronization between EAM and ArcGIS Utility Netw...Safe Software

Utilities and water companies play a key role in the creation of clean drinking water. The creation and maintenance of clean drinking water is becoming a critical problem due to pollution and pressure on the environment. A lot of data is necessary to create clean drinking water. For fieldworkers, two types of data are key: Asset data in an asset management system (EAM for example) and Geographic data in a GIS (ArcGIS Utility Network ). Keeping this type of data up to date and in sync is a challenge for many organizations, leading to duplicating data and creating a bulk of extra attributes and data to keep everything in sync. Using FME, it is possible to synchronize Enterprise Asset Management (EAM) data with the ArcGIS Utility Network in real time. Changes (creation, modification, deletion) in ArcGIS Pro are relayed to EAM via FME, and vice versa. This ensures continuous synchronization of both systems without daily bulk updates, minimizes risks, and seamlessly integrates with ArcGIS Utility Network services. This presentation focuses on the use of FME at a Dutch water company, to create a sync between the asset management and GIS.

The Future of Data, AI, and AR: Innovation Inspired by You.pdfSafe Software

FME for Distribution & Transmission Integrity Management Program (DIMP & TIMP)Safe Software

Peoples Gas in Chicago, IL has changed to a new Distribution & Transmission Integrity Management Program (DIMP & TIMP) software provider in recent years. In order to successfully deploy the new software we have created a series of ETL processes using FME Form to transform our gas facility data to meet the required DIMP & TIMP data specifications. This presentation will provide an overview of how we used FME to transform data from ESRI’s Utility Network and several other internal and external sources to meet the strict data specifications for the DIMP and TIMP software solutions.

Enabling BIM / GIS integrations with Other Systems with FMESafe Software

Jacobs has successfully utilized FME to tackle the complexities of integrating diverse data sources in a confidential $1 billion campus improvement project. The project aimed to create a comprehensive digital twin by merging Building Information Modeling (BIM) data, Construction Operations Building Information Exchange (COBie) data, and various other data sources into a unified Geographic Information System (GIS) platform. The challenge lay in the disparate nature of these data sources, which were siloed and incompatible with each other, hindering efficient data management and decision-making processes. To address this, Jacobs leveraged FME to automate the extraction, transformation, and loading (ETL) of data between ArcGIS Indoors and IBM Maximo. This process ensured accurate transfer of maintainable asset and work order data, creating a comprehensive 2D and 3D representation of the campus for Facility Management. FME's server capabilities enabled real-time updates and synchronization between ArcGIS Indoors and Maximo, facilitating automatic updates of asset information and work orders. Additionally, Survey123 forms allowed field personnel to capture and submit data directly from their mobile devices, triggering FME workflows via webhooks for real-time data updates. This seamless integration has significantly enhanced data management, improved decision-making processes, and ensured data consistency across the project lifecycle.

From Swamp to Data Lakes: Slowly Changing Dimensions (SDC) with FMESafe Software

In fast-changing environments, retaining historical accuracy is crucial but often overlooked. How do you ensure reliable, consistent reporting in a dynamic data landscape? This session is designed for data professionals looking to maintain historical accuracy and improve the efficiency of data lake management. When you want to replicate previous reports or analysis as it ran at a specific time or day in the past, a typical database or data lake may have changed over time, and your old queries and analyses break. When using Slowly Changing Dimensions on your data lake, you have a way to preserve the evolving nature of your schema and account for historical records/truths. By adding three columns that log the timestamp of a record and never deleting a column, you can replicate your analysis at any time in the past. With FME, we build up this history and have a special trick using hashing to check which records and schemas have changed efficiently! This presentation will teach you about the data lake medallion approach combined with Slowly Changing Dimensions and how we can speed up the insertion and updating of new records using widely available hashing techniques in FME. This session will equip you with the knowledge to transform your data swamp into a robust and beautiful data lake, ready to handle historical analyses with ease. Let FME be your guide in making your data lake future-proof!

No-Code Workflows for CAD & 3D Data: Scaling AI-Driven InfrastructureSafe Software

When projects depend on fast, reliable spatial data, every minute counts. AI Clearing needed a faster way to handle complex spatial data from drone surveys, CAD designs and 3D project models across construction sites. With FME Form, they built no-code workflows to clean, convert, integrate, and validate dozens of data formats – cutting analysis time from 5 hours to just 30 minutes. Join us, our partner Globema, and customer AI Clearing to see how they: -Automate processing of 2D, 3D, drone, spatial, and non-spatial data -Analyze construction progress 10x faster and with fewer errors -Handle diverse formats like DWG, KML, SHP, and PDF with ease -Scale their workflows for international projects in solar, roads, and pipelines If you work with complex data, join us to learn how to optimize your own processes and transform your results with FME.

Supporting the NextGen 911 Digital Transformation with FMESafe Software

Next Generation 911 involves the transformation of our 911 system from an old analog one to the new digital internet based architecture. The evolution of NG911 opens up a host of new opportunities to improve the system. This includes everything from device based location, to real time text. This can improve location accuracy dramatically as well as provide live updates from the citizen in need along with real time sensor updates. There is also the opportunity to provide multi-media attachments and medical records if the end user approves. This digital transformation and enhancements all require the support of new NENA and CRTC standards, along with integration across a variety of data streams. This presentation will focus on how FME has supported NG911 transformations to date, and how we are positioning FME to support the enhanced capabilities to come. This session will be of interest to emergency services, municipalities and anyone who may be interested to know more about how emergency services are being improved to provide more accurate, localized information in order to improve the speed and relevance of emergency response and ultimately save more lives and provide better outcomes for those in need.

Integrating Legacy and OTL Data for Enhanced Asset Management at The Agency f...Safe Software

The Agency for Roads and Traffic (AWV) manages road infrastructure in Flanders and has developed the Object Type Library (OTL) to enhance asset management. This development is phased, resulting in a mix of legacy data and OTL data, which sometimes overlaps and can be contradictory. The integration project aims to consolidate all legacy data into the OTL database, creating a single, consistent dataset without duplicates or inconsistencies. To achieve this goal, various technologies are used: 1. Data retrieval: Data is extracted from original databases via API calls. 2. Data storage: Data is stored in a PostGIS database, serving as the source for further analysis. 3. Reporting in standardized Excel files: Assets are analyzed to identify corresponding items, and differences between corresponding legacy and OTL assets are presented to the business through user-friendly Excel reports. 4. Geographic visualization: Geographic data is visualized in ArcGIS Online. 5. Processing of business decisions: The business records decisions regarding which assets, values, and geometries to retain in the reports. This input is then processed to update the OTL database through API calls and by uploading JSON files into an AWV-specific tool. During the project, both systems remain active, meaning that changes on both the legacy and OTL sides must be captured. Automated FME processes enable this cycle to be repeated until the OTL is fully synchronized with the legacy system.

National Fuels Treatments Initiative: Building a Seamless Map of Hazardous Fu...Safe Software

The National Fuels Treatments Initiative (NFT) is transforming wildfire mitigation by creating a standardized map of nationwide fuels treatment locations across all land ownerships in the United States. While existing state and federal systems capture this data in diverse formats, NFT bridges these gaps, delivering the first truly integrated national view. This dataset will be used to measure the implementation of the National Cohesive Wildland Strategy and demonstrate the positive impact of collective investments in hazardous fuels reduction nationwide. In Phase 1, we developed an ETL pipeline template in FME Form, leveraging a schema-agnostic workflow with dynamic feature handling intended for fast roll-out and light maintenance. This was key as the initiative scaled from a few to over fifty contributors nationwide. By directly pulling from agency data stores, oftentimes ArcGIS Feature Services, NFT preserves existing structures, minimizing preparation needs. External mapping tables ensure consistent attribute and domain alignment, while robust change detection processes keep data current and actionable. Now in Phase 2, we’re migrating pipelines to FME Flow to take advantage of advanced scheduling, monitoring dashboards, and automated notifications to streamline operations. Join us to explore how this initiative exemplifies the power of technology, blending FME, ArcGIS Online, and AWS to solve a national business problem with a scalable, automated solution.

Providing an OGC API Processes REST Interface for FME FlowSafe Software

This presentation will showcase an adapter for FME Flow that provides REST endpoints for FME Workspaces following the OGC API Processes specification. The implementation delivers robust, user-friendly API endpoints, including standardized methods for parameter provision. Additionally, it enhances security and user management by supporting OAuth2 authentication. Join us to discover how these advancements can elevate your enterprise integration workflows and ensure seamless, secure interactions with FME Flow.

Reducing Conflicts and Increasing Safety Along the Cycling Networks of East-F...Safe Software

In partnership with the Belgian Province of East-Flanders this project aimed to reduce conflicts and increase safety along a cycling route between the cities of Oudenaarde and Ghent. To achieve this goal, the current cycling network data needed some extra key information, including: Speed limits for segments, Access restrictions for different users (pedestrians, cyclists, motor vehicles, etc.), Priority rules at intersections. Using a 360° camera and GPS mounted on a measuring bicycle, we collected images of traffic signs and ground markings along the cycling lanes building up mobile mapping data. Image recognition technologies identified the road signs, creating a dataset with their locations and codes. The data processing entailed three FME workspaces. These included identifying valid intersections with other networks (e.g., roads, railways), creating a topological network between segments and intersections and linking road signs to segments and intersections based on proximity and orientation. Additional features, such as speed zones, inheritance of speed and access to neighbouring segments were also implemented to further enhance the data. The final results were visualized in ArcGIS, enabling analysis for the end users. The project provided them with key insights, including statistics on accessible road segments, speed limits, and intersection priorities. These will make the cycling paths more safe and uniform, by reducing conflicts between users.

Integration of Utility Data into 3D BIM Models Using a 3D Solids Modeling Wor...Safe Software

Jacobs has developed a 3D utility solids modelling workflow to improve the integration of utility data into 3D Building Information Modeling (BIM) environments. This workflow, a collaborative effort between the New Zealand Geospatial Team and the Australian Data Capture Team, employs FME to convert 2D utility data into detailed 3D representations, supporting enhanced spatial analysis and clash detection. To enable the automation of this process, Jacobs has also developed a survey data standard that standardizes the capture of existing utilities. This standard ensures consistency in data collection, forming the foundation for the subsequent automated validation and modelling steps. The workflow begins with the acquisition of utility survey data, including attributes such as location, depth, diameter, and material of utility assets like pipes and manholes. This data is validated through a custom-built tool that ensures completeness and logical consistency, including checks for proper connectivity between network components. Following validation, the data is processed using an automated modelling tool to generate 3D solids from 2D geometric representations. These solids are then integrated into BIM models to facilitate compatibility with 3D workflows and enable detailed spatial analyses. The workflow contributes to improved spatial understanding by visualizing the relationships between utilities and other infrastructure elements. The automation of validation and modeling processes ensures consistent and accurate outputs, minimizing errors and increasing workflow efficiency. This methodology highlights the application of FME in addressing challenges associated with geospatial data transformation and demonstrates its utility in enhancing data integration within BIM frameworks. By enabling accurate 3D representation of utility networks, the workflow supports improved design collaboration and decision-making in complex infrastructure projects

Mastering AI Workflows with FME - Peak of Data & AI 2025Safe Software

Harness the full potential of AI with FME: From creating high-quality training data to optimizing models and utilizing results, FME supports every step of your AI workflow. Seamlessly integrate a wide range of models, including those for data enhancement, forecasting, image and object recognition, and large language models. Customize AI models to meet your exact needs with FME’s powerful tools for training, optimization, and seamless integration

Floods in Valencia: Two FME-Powered Stories of Data ResilienceSafe Software

In October 2024, the Spanish region of Valencia faced severe flooding that underscored the critical need for accessible and actionable data. This presentation will explore two innovative use cases where FME facilitated data integration and availability during the crisis. The first case demonstrates how FME was used to process and convert satellite imagery and other geospatial data into formats tailored for rapid analysis by emergency teams. The second case delves into making human mobility data—collected from mobile phone signals—accessible as source-destination matrices, offering key insights into population movements during and after the flooding. These stories highlight how FME's powerful capabilities can bridge the gap between raw data and decision-making, fostering resilience and preparedness in the face of natural disasters. Attendees will gain practical insights into how FME can support crisis management and urban planning in a changing climate.

FME as an Orchestration Tool - Peak of Data & AI 2025Safe Software

High Availability On-Premises FME Flow.pdfSafe Software

FME Flow is a highly robust tool for transforming data both automatically and by user-initiated workflows. At the Finnish telecommunications company Elisa, FME Flow serves processes and internal stakeholders that require 24/7 availability from underlying systems, while imposing limitations on the use of cloud based systems. In response to these business requirements, Elisa has implemented a high-availability on-premises setup of FME Flow, where all components of the system have been duplicated or clustered. The goal of the presentation is to provide insights into the architecture behind the high-availability functionality. The presentation will show in basic technical terms how the different parts of the system work together. Basic level understanding of IT technologies is required to understand the technical portion of the presentation, namely understanding the purpose of the following components: load balancer, FME Flow host nodes, FME Flow worker nodes, network file storage drives, databases, and external authentication services. The presentation will also outline our lessons learned from the high-availability project, both benefits and challenges to consider.

Integrating Survey123 and R&H Data Using FMESafe Software

West Virginia Department of Transportation (WVDOT) actively engages in several field data collection initiatives using Collector and Survey 123. A critical component for effective asset management and enhanced analytical capabilities is the integration of Geographic Information System (GIS) data with Linear Referencing System (LRS) data. Currently, RouteID and Measures are not captured in Survey 123. However, we can bridge this gap through FME Flow automation. When a survey is submitted through Survey 123 for ArcGIS Portal (10.8.1), it triggers FME Flow automation. This process uses a customized workbench that interacts with a modified version of Esri's Geometry to Measure API. The result is a JSON response that includes RouteID and Measures, which are then applied to the feature service record.

FME for Good: Integrating Multiple Data Sources with APIs to Support Local Ch...Safe Software

Have-a-skate-with-Bob (HASB-KC) is a local charity that holds two Hockey Tournaments every year to raise money in the fight against Pancreatic Cancer. The FME Form software is used to integrate and exchange data via API, between Google Forms, Google Sheets, Stripe payments, SmartWaiver, and the GoDaddy email marketing tools to build a grass-roots Customer Relationship Management (CRM) system for the charity. The CRM is used to communicate effectively and readily with the participants of the hockey events and most importantly the local area sponsors of the event. Communication consists of a BLOG used to inform participants of event details including, the ever-important team rosters. Funds raised by these events are used to support families in the local area to fight cancer and support PanCan research efforts to find a cure against this insidious disease. FME Form removes the tedium and error-prone manual ETL processes against these systems into 1 or 2 workbenches that put the data needed at the fingertips of the event organizers daily freeing them to work on outreach and marketing of the events in the community.

Data Validation and System InteroperabilitySafe Software

Developing Schemas with FME and Excel - Peak of Data & AI 2025Safe Software

When working with other team members who may not know the Esri GIS platform or may not be database professionals; discussing schema development or changes can be difficult. I have been using Excel to help illustrate and discuss schema design/changes during meetings and it has proven a useful tool to help illustrate how a schema will be built. With just a few extra columns, that Excel file can be sent to FME to create new feature classes/tables. This presentation will go thru the steps needed to accomplish this task and provide some lessons learned and tips/tricks that I use to speed the process.

If You Use Databricks, You Definitely Need FMESafe Software

Providing Better Biodiversity Through Better DataSafe Software

This session explores how FME is transforming data workflows at Ireland’s National Biodiversity Data Centre (NBDC) by eliminating manual data manipulation, incorporating machine learning, and enhancing overall efficiency. Attendees will gain insight into how NBDC is using FME to document and understand internal processes, make decision-making fully transparent, and shine a light on underlying code to improve clarity and reduce silent failures. The presentation will also outline NBDC’s future plans for FME, including empowering staff to access and query data independently, without relying on external consultants. It will also showcase ambitions to connect to new data sources, unlock the full potential of its valuable datasets, create living atlases, and place its valuable data directly into the hands of decision-makers across Ireland—ensuring that biodiversity is not only protected but actively enhanced.

COBOL Programming with VSCode - IBM CertificateVICTOR MAESTRE RAMIREZ

Agentic Techniques in Retrieval-Augmented Generation with Azure AI SearchMaxim Salnikov

Discover how Agentic Retrieval in Azure AI Search takes Retrieval-Augmented Generation (RAG) to the next level by intelligently breaking down complex queries, leveraging full conversation history, and executing parallel searches through a new LLM-powered query planner. This session introduces a cutting-edge approach that delivers significantly more accurate, relevant, and grounded answers—unlocking new capabilities for building smarter, more responsive generative AI applications. Traditional Retrieval-Augmented Generation (RAG) pipelines work well for simple queries—but when users ask complex, multi-part questions or refer to previous conversation history, they often fall short. That’s where Agentic Retrieval comes in: a game-changing advancement in Azure AI Search that brings LLM-powered reasoning directly into the retrieval layer. This session unveils how agentic techniques elevate your RAG-based applications by introducing intelligent query planning, subquery decomposition, parallel execution, and result merging—all orchestrated by a new Knowledge Agent. You’ll learn how this approach significantly boosts relevance, groundedness, and answer quality, especially for sophisticated enterprise use cases. Key takeaways: - Understand the evolution from keyword and vector search to agentic query orchestration - See how full conversation context improves retrieval accuracy - Explore measurable improvements in answer relevance and completeness (up to 40% gains!) - Get hands-on guidance on integrating Agentic Retrieval with Azure AI Foundry and SDKs - Discover how to build scalable, AI-first applications powered by this new paradigm Whether you're building intelligent copilots, enterprise Q&A bots, or AI-driven search solutions, this session will equip you with the tools and patterns to push beyond traditional RAG.

More Related Content

More from Safe Software (20)