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Adopting AI

Thursday, 7 March 2024

10:00am MT

Data Quality and Innovative Training Strategies
n Government Applications


While pre-trained AI models provide a convenient starting point, their performance is limited by the quality and coverage of their training data. Attend this webinar to unlock the full potential of AI in public service. Explore innovative data strategies to drive ethical, efficient, and inclusive government solutions.

Sponsored by



Join this enlightening webinar that delves into the critical aspects of adopting AI in government operations, with a special focus on ensuring high data quality and employing innovative training strategies. This session is designed for policymakers, tech leaders, and AI enthusiasts keen on exploring how artificial intelligence can transform public services, enhance policy making, and improve operational efficiencies. .


This event aims to unveil strategies for enhancing data collection, curation, and refinement to accurately reflect the true data distribution, a challenge that remains as complex as ever. Furthermore, we will explore the cutting-edge approach of leveraging Software as a Service (SaaS) to generate unlimited, customized synthetic data. This method addresses the hurdles of cost, bias, security, and privacy associated with real data acquisition, facilitating the development of more refined, efficient, and unbiased AI systems. Designed to be both informative and practical, this webinar promises to equip government professionals with the knowledge to innovate and improve their AI-driven initiatives.

Key Takeaways:


Attend this webinar to learn:

  • Strategies for Enhancing Data Quality: Understand the importance of accuracy, reliability, and bias mitigation in government datasets and learn how to implement robust data governance frameworks.

  • Innovative AI Training Techniques: Discover how simulated environments, transfer learning, and crowdsourcing can be leveraged to improve AI models with limited resources.

Who Should Attend:  

  • Data Scientists and Analysts in Government Agencies

  • Federal, State and Local Government IT Directors

  • AI and Machine Learning Engineers in Public Sector

  • Public Policy and Digital Transformation Advisors

  • Urban Planning and Smart City Coordinators

  • Government Data Privacy and Security Officers

  • Civic Innovation and Technology Strategists

  • Public Sector Project Managers in Technology Initiatives

  • Government Technology Infrastructure Architects

  • Digital Services Managers at State and Local Levels

Attending this webinar will offer these professionals a comprehensive insight into the power of custom data and how AI can be utilized.

You’ll leave equipped to enhance your AI with custom datasets tailored to your needs. The future of AI relies on data - help it reach its full potential.

  • Collaboration for AI Advancement: Learn about the benefits of partnerships between government agencies, academia, and the private sector in accelerating AI adoption and innovation.

  • Navigating AI Implementation Challenges: Gain insights into addressing scalability, security, regulatory compliance, and public trust issues when deploying AI solutions in the public sector.



Thursday, 7 March 2024
10:00am - 11:30am
All times in US Denver Mountain time



Welcome Introduction and Overview

Nadine Alameh, Executive Director, Taylor Geospatial Institute

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Welcome remarks and an overview of the webinar objectives​



AI Advancement and Adoption Across the Changing Landscape of Situational Awareness in Public Sectors

Daniela Moody, Chief Technical Officer, Geosite

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Situational awareness, a dual-technology space that has experienced accelerated growth in the past decade, involves understanding of an environment, qualifying and quantifying risks and exposure, as well as predicting potential scenarios and their impacts. Today it is critically dependent on AI technology advancement and rapid adoption across all market sectors. We are at a technology innovation crossroads with government customer needs and expectations vastly outweighing the time and resources required to meet those needs. Daniela will discuss the geospatial AI advances the insurance sector has been adopting and implementing at scale to increase their situational awareness and enable rapid response during natural catastrophic events. Additionally, she will expand on the challenges of integrating geospatial AI technology into de facto insurance software platforms and navigating the complexities of security, regulatory compliance and trust. 



Unlimited Synthetic Data for Training and Tuning AI

Chris Andrews, COO and Head of Product,

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Chris Andrews will discuss the how data scientists, data engineers, and developers can use a synthetic data platform to create and deploy unlimited, customized synthetic data generation for computer vision-related machine learning and artificial intelligence workflows, reducing expense, closing gaps, and overcoming bias, security, and privacy issues when compared with the use or acquisition of real data. He will cover how this makes it easier for users to create synthetic data for enterprise workflows by providing a collaborative environment, samples, and cloud resources to quickly get started defining new data generation applications, creating datasets in high performance compute environments, and comparing existing and synthetic datasets to optimize AI training and validation.



Data Quality Assessment for Computer Vision Model Training

Marc Bosch, Computer Vision Science Director and Managing Director, Accenture Federal Services

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A major challenge in CV/ML is the quality of the training data. Training data used in current supervised deep learning techniques is, almost exclusively, responsible for the success or failure of the system when is designed to solve very specific tasks. The ability to capture the true distribution of data is still more alchemy than science. In this talk, Dr. Bosch will discuss several strategies to overcome this during data collection, curation and/or refinement.



Panel Discussion / Audience Q&A

Moderator: Nadine Alameh, Executive Director, Taylor Geospatial Institute
Marc Bosch, Computer Vision Science Director and Managing Director, Accenture Federal Services

Daniela Moody, Chief Technical Officer, Geosite
Chris Andrews, COO and Head of Product,

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A panel discussion of data on demand, covering synthetic data to reality capture for the AI revolution, with live audience question-and-answers.



Closing Remarks

Nadine Alameh, Executive Director, Taylor Geospatial Institute

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Session wrap-up and audience take-aways.

Conference Format

Hosted on an exciting and interactive virtual event platform, this event series features a virtual auditorium, plus audience interaction via Q&A and Polls.

This event may qualify for GIS Certification Institute continuing education credits.

To submit for GISP Points, visit to self-submit the event curriculum for approval



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Nadine Alameh

Executive Director, Taylor Geospatial Institute

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Nadine Alameh is the Executive Director of the Taylor Geospatial Institute, a position she assumed in September 2023. A world-renowned geospatial expert, Nadine was previously the CEO and president of the Open Geospatial Consortium. She is also an appointed member of the U.S. Department of Interior’s National Geospatial Advisory Committee and a board member of the United Nations Geospatial Global Information Management Private Sector Network. Before taking the helm at OGC, Nadine held various roles in industry, from the chief architect for innovation in Northrop Grumman’s Civil Solutions Unit, to CEO of an aviation data exchange startup, to senior technical advisor to NASA’s Applied Science Program. In the early 2000s, she launched and led several successful startups. Nadine has received numerous honors during her career, including the 2019 Geomatics Canada Leadership in Diversity Award, the 2022 Geospatial World Diversity Champion of the Year Award, and the 2023 Women in Technology Leadership Award in the nonprofit and academia category. Nadine earned a doctorate in computer and information systems engineering from the Massachusetts Institute of Technology, where she also earned master’s degrees in civil and environmental engineering and city planning. She earned a bachelor’s degree in engineering from the American University of Beirut.

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Marc Bosch

Computer Vision Science Director and Managing Director
Accenture Federal Services

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Marc Bosch is a Computer Vision lead and Managing Director at Accenture. He is currently serving as a principal investigator in several IARPA and DARPA programs. Dr. Bosch’s research interest include image/video processing, computer vision, machine learning, and computational photography. In 2012 he joined Texas Instruments as a computer vision/computational photography engineer. From 2013-16 he was a senior video engineer at Qualcomm, Inc. From 2016-19 he was at Johns Hopkins University APL. He received a degree in Telecommunications engineering from Technical University of Catalonia (UPC), Barcelona, Spain, in 2007 and a M.S. and Ph.D. degrees in electrical and computer engineering from Purdue University, West Lafayette, IN in 2009 and 2012 respectively.

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Chris Andrews

COO and Head of Product,

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Chris Andrews is COO and Head of Product at, helping customers overcome the costs and limitations of using real-world data to train AI and ML systems. Chris previously led a team at Esri responsible for 3D, Defense, Urban Planning, and AEC products. Prior to Esri, Chris was the lead product manager for Autodesk’s InfraWorks.

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Daniela Moody

Chief Technical Officer, Geosite

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Dr. Daniela Moody is the Chief Technology Officer at Geosite. Previously, Daniela served as the VP of Artificial Intelligence at Arturo, and as the Chief Technology Officer/VPE at Ursa Space Systems (Ursa), focusing on analytic solutions to derive business intelligence from multimodal aerial and satellite data. Daniela also worked at Descartes Labs as technical project lead for broad area food security monitoring efforts. She was at Los Alamos National Laboratory for 9 years prior to joining Descartes Labs, working on remote sensing and machine learning applications in various research areas, including space systems, astronomy, on orbit RF and radar systems, and nuclear non-proliferation. She received her M.S and Ph.D. in Electrical Engineering from the University of Maryland, College Park with a focus in signal processing, computer vision, and machine learning. Daniela is a Senior IEEE member, an active member of the Forbes Technology Council and a National Academy of Engineers/Frontiers of Engineering Alumna.



RenderedAI spkr logo.png was established after the realization that many industries were about to explode with massive investments in hardware-intensive imagery collection and analysis.  Without the ability to access data during the design and development process, organizations are unable to validate analysis pipelines and business models before launching expensive hardware, sometimes literally, into the market. From space-based satellite imaging to manufacturing and security inspection, computer vision hardware and applications are proliferating across every industry. Relying on collected data alone carries risks and costs due to dataset biases and real data is simply not available for new sensors and platforms. Simulating sensor behavior and data output is a well-established technique used during the design and inception process for new equipment, but historically was not done at a scale sufficient to generate annotated data for the purpose of training computer vision algorithms. was founded to connect simulation with data generation for computer vision and the team quickly demonstrated the potential for using simulated data to train Artificial Intelligence and Machine Learning systems with customers in the geospatial industry. Along the way, the team observed that simulated, or synthetic, data required an iterative workflow best supported by a platform that could encompass simulation tools, compute management, and domain-specific content. The platform as a service has been in production since late 2021 and is helping customers across multiple industries and countries to reduce bias and overcome cost and availability issues when training algorithms to solve critical problems.

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