Slide 1: Financial Results Presentation for the First Quarter of the Fiscal Year Ending March 2027
Financial Results Presentation for the First Quarter of the Fiscal Year Ending March 2027 (FY2027) Dynamic Map Platform Co., Ltd. Aug. 7, 2026 T S E Growth 336A
Slide 2: Table of Contents
Table of Contents 01 Company and Business Overview 02 AI-Native Data 03 Financial Results for the First Quarter and Full-Year Forecast for FY2027 04 Business Pipeline Update 05 Appendix
Slide 3: Company and Business Overview
01 Company and Business Overview 3
Slide 4: Company Overview
01 ｜ Company and Business Overview Company Overview Company Name Dynamic Map Platform Co., Ltd. (Securities Code: 336A TSE Growth) Established June 13, 2016 Head Office Address 2-12-4 Shibuya, Shibuya-ku, Tokyo Business Locations Japan, U.S., Germany, Saudi Arabia, UAE, South Korea Employees 249 (a
Slide 5: Dynamic Map Platform At-a-Glance
01 ｜ Company and Business Overview Dynamic Map Platform At-a-Glance Global expansion powered by data for physical AI, with growth driven by a license-based business model 26 74% JPY 5.7 Billion 121 % YoY 1.8 30% Physical AI Countries (1) Overseas License-Based Million km Consolidated Market CAGR (2
Slide 6: Highlights
01 ｜ Company and Business Overview Highlights Global Deep-Tech A deep-tech company building a global high-precision location information platform known as a dynamic map. High Growth With a strong customer base that includes 10 major Japanese automakers, General Motors, and the Japanese government,
Slide 7: High-Precision 3D Data Platform
01 ｜ Company and Business Overview As a provider of a high-precision 3D data platform, we aggregate a wide range of information. We aim to enable analysis, control, and prediction, thereby driving innovation that helps address societal challenges. Traffic Advertising Information Information Real Es
Slide 8: Our High-Precision 3D Data Platform and Services
01 ｜ Company and Business Overview Our High-Precision 3D Data Platform and Services High-precision 3D map data that accurately represents roads, surrounding environments, and spatial structures, serving as a foundation for autonomous driving and various spatial services. Data for Autonomous Driving
Slide 9: Building One of the World’s Largest High-Precision 3D Data Platforms
01 ｜ Company and Business Overview Building One of the World’s Largest High-Precision 3D Data Platforms Building one of the world’s largest high-precision 3D data platforms, with coverage of 1,800,000 km and significant potential to support industrial digitalization and help address societal challen
Slide 10: Overview of Our Business Model: Two Pillars of Project-Based and License-Based Business
01 ｜ Company and Business Overview Overview of Our Business Model: Two Pillars of Project-Based and License-Based Business Project-Based Business: We selectively accept projects based on targeted gross profit margins. License-Based Business: We leverage established data assets and systems to generat
Slide 11: Building a Dynamic Map Platform by Integrating Various Types of Data
01 ｜ Company and Business Overview Building a Dynamic Map Platform by Integrating Various Types of Data Started with static data (high-precision 3D data) for public roads, and expanded into specific areas such as airports, ports, and logistics centers. Now building dynamic maps through the integrati
Slide 12: Building a Surveying Network: The Need to Expand Surveying Capabilities
01 ｜ Company and Business Overview Building a Surveying Network: The Need to Expand Surveying Capabilities To expand business in specific areas such as airports, ports, and logistics centers, we are diversifying data acquisition methods, including satellite data, aerial imagery, and drone surveying,
Slide 13: Business Expansion Initiatives via M&A
01 ｜ Company and Business Overview Business Expansion Initiatives via M&A In 2019, we acquired Ushr, Inc., now Dynamic Map Platform North America, as part of our horizontal integration strategy and established a leading position in the global HD map market. Following our IPO in 2025, we began pursui
Slide 14: Accelerating Growth Through Strategic M&A
01 ｜ Company and Business Overview Accelerating Growth Through Strategic M&A Our M&A initiatives began with the acquisition of Ushr, Inc. in 2019. Following our IPO in 2025, we resumed our M&A activities. A total of three acquisitions have already been completed, and we plan to further expand our gr
Slide 15: A Data Platform for Physical AI
01 ｜ Company and Business Overview A Data Platform for Physical AI Providing a “data platform” for the physical AI era across diverse industries Expanding from autonomous driving to mobility, infrastructure, advertising, and entertainment Advertisement Entertainment Retail/Advertisement Entertainmen
Slide 16: Potential Applications Across Japan’s 17 Strategic Growth Sectors
01 ｜ Company and Business Overview Potential Applications of Our Data Across the Japanese Government’s 17 Strategic Growth Sectors High-precision 3D data: Potential to contribute to solving challenges and advancing industries across a wide range of strategic sectors 1. AI and Semiconductors 2. Shipb
Slide 17: AI-Native Data
Al-Native Data DYNAMIC MAP PLATFORM W 7
Slide 18: AI-Native Modeling of Real-World 3D Environments
02 ｜ AI-Native Data AI-Native Modeling of Real-World 3D Environments Integrates images, point cloud data, and high-precision 3D map data to generate 3D environment models that can be used for AI training and validation. Builds a highly accurate data foundation that reflects real-world road structure
Slide 19: 3D Environment Modeling with 3DGS: Free-Viewpoint Video
02 ｜ AI-Native Data 3D Environment Modeling with 3DGS: A Data Foundation for AI Training and Validation 3D Environment Model: Free-Viewpoint Video 19
Slide 20: 3D Environment Modeling with 3DGS: Straight-Driving Scenario
02 ｜ AI-Native Data 3D Environment Modeling with 3DGS: A Data Foundation for AI Training and Validation 3D Environment Model: Straight-Driving Scenario 20
Slide 21: 3D Environment Modeling with 3DGS: Right-Turn Scenario
02 ｜ AI-Native Data 3D Environment Modeling with 3DGS: A Data Foundation for AI Training and Validation 3D Environment Model: Right-Turn Scenario 21
Slide 22: Building a Data Foundation for Physical AI
02 ｜ AI-Native Data Building a Data Foundation for Physical AI A key challenge in AI development is the shortage of training and validation data. Real-world data alone has limitations in both scale and diversity. By combining DMP's AI-native datasets with generative AI technologies, we expand the tr
Slide 23: Generating Training and Validation Data: Daytime / Clear Weather
02 ｜ AI-Native Data Generating Training and Validation Data from AI-Native Datasets Training and validation data generation: Daytime / Clear Weather 23
Slide 24: Generating Training and Validation Data: Nighttime
02 ｜ AI-Native Data Generating Training and Validation Data from AI-Native Datasets Training and validation data generation: Nighttime 24
Slide 25: Generating Training and Validation Data: Daytime / Heavy Rain
02 ｜ AI-Native Data Generating Training and Validation Data from AI-Native Datasets Training and validation data generation: Daytime / Heavy Rain 25
Slide 26: Collaboration with NVIDIA and MathWorks on Data Generation for Physical AI
02 ｜ AI-Native Data Collaboration with NVIDIA and MathWorks on Data Generation for Physical AI Training and Validation Using DMP’s high-precision 3D map data as source data, 3D scenes and scenarios are created with MathWorks RoadRunner, and NVIDIA Cosmos Transfer is used to generate high-quality vid
Slide 27: Financial Results for the First Quarter and Full-Year Forecast for FY2027
03 Financial Results for the First Quarter and Full-Year Forecast for FY2027 27
Slide 28: Key Initiatives
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 Progress in Strengthening the Foundation for Expanded Enterprise Licensing and Growth in the Physical AI Business Expansion of enterprise license orders from a leading global semiconductor manufacturer and autonomous tru
Slide 29: Additional Enterprise License Order for AI Applications from a Semiconductor Manufacturer
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 Received an Additional Enterprise License Order for AI Applications (Data for AI) from a Leading Global Semiconductor Manufacturer Additional order received for Europe, South Korea, and Japan data following successful ut
Slide 30: Providing AI-Native Datasets Built on Globally Developed 3D Data Assets
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 Providing AI-Native Datasets Built on Globally Developed High-Precision 3D Data Assets Accelerating the Data for AI business through AI-native datasets Released a sample AI-native dataset on Hugging Face in June 2026, ge
Slide 31: Integrating Four Data Layers for Multimodal AI Data
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 Integrating Four Data Layers to Provide the Multimodal Data Required for AI Development RAW data, high-precision 3D data, semantic data, and 3DGS data are integrated within a unified spatial framework. This integrated fr
Slide 32: Expanding High-Precision 3D Data Applications from Autonomous Driving to Physical AI
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 Expanding High-Precision 3D Data Applications from Autonomous Driving to Physical AI High-precision 3D data developed for the Tokyo Ryutsu Center (TRC) is being utilized by members of the Heiwajima Autonomous Driving Con
Slide 33: Contributing to Open Innovation for the Deployment of Physical AI
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 Contributing to Open Innovation for the Deployment of Physical AI The Heiwajima Autonomous Driving Conference, established in May 2025, brings together industry, government, and academia and utilizes the Tokyo Ryutsu Cen
Slide 34: Growing Adoption of 3Dmapspocket in Real Estate Development
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 Growing Adoption of 3Dmapspocket® in Real Estate Development 3Dmapspocket® is a comprehensive 3D spatial platform that enables measurement, analysis, object placement, and other spatial assessments in a one-stop environm
Slide 35: 3Dmapspocket Provided Free After the Kumamoto Earthquake
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 3Dmapspocket® Provided Free of Charge to Support Damage Assessment and Recovery Efforts Following the Kumamoto Earthquake 3Dmapspocket® provided free of charge to support situational awareness and recovery efforts in ear
Slide 36: License-Based Revenue Increased Year Over Year, Adjusted EBITDA Within Budget
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 License-Based Revenue Increased Year Over Year, While Adjusted EBITDA Remained Within Budget Despite a Larger Loss License-Based Revenue grew year over year, driven primarily by enterprise license projects, increasing 10
Slide 37: On Track to Achieve Positive Adjusted EBITDA for the Full Year
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 On Track to Achieve Positive Adjusted EBITDA for the Full Year Full-year guidance remains unchanged, with revenue of JPY7.0 billion, license-based revenue of JPY3.0 billion, and adjusted EBITDA expected to return to posi
Slide 38: Key Initiatives for the Fiscal Year Ending March 2027
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 Business environment and initiatives Strengthening AI-native data and product development and expanding enterprise licenses Strengthening “AI-native” data and product development to meet demand for physical AI. Continuin
Slide 39: Automotive Enterprise Licenses Drive License-Based Revenue
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 Automotive Enterprise Licenses Drive License-Based Revenue; Initiatives Underway to Win and Expand Project-Based License-based business: Enterprise licenses continue to drive growth in AI-related use cases; mass-producti
Slide 40: Expanding an 80%+ Gross Margin License-Based Business to Drive Mid- to Long-Term Growth
03 ｜ Financial Results for the First Quarter and Full-Year Forecast for FY2027 Expanding an 80%+ Gross Margin License-Based Business to Drive 10-30% Mid- to Long-Term Growth With the business foundation for growth now in place, we will shift to a license-driven revenue structure and move into the p
Slide 41: Business Pipeline Update
04 Business Pipeline Update 41
Slide 42: License-Based Business (Automotive)
04 ｜ Business Pipeline Update License-Based Business (Automotive) In addition to growth in vehicle model adoption, we expect demand for our data to increase as AI adoption in autonomous driving and ADAS development accelerates. Our data has been adopted by a total of six customers across 39 vehicle
Slide 43: License-Based Business (3D Data)
04 ｜ Business Pipeline Update License-Based Business (3D Data) Leveraging our global data assets, applications are expanding into infrastructure management, real estate, simulation, gaming, and other industries. We are also advancing data provision through sales partners and focusing on building a r
Slide 44: Project-Based Business (3D Data)
04 ｜ Business Pipeline Update Project-Based Business (3D Data) Backed by strong relationships with the Japanese government, we have a proven track record of winning multiple national projects in the 3D data business. Amid growing private-sector–led automation investment, projects with private compa
Slide 45: Project-Based Business (Automotive)
04 ｜ Business Pipeline Update Project-Based Business (Automotive) Expanding HD map coverage while securing stable revenue and limiting upfront investment. Advances new development in new countries and regions, while continuing to secure update orders in existing markets. Key Pipeline (revenues recog
Slide 46: High-Precision 3D Data Platform
We aggregate various information as a high-precision 3D data platform provider. We aim to realize a world where analysis, control, and prediction are possible, thereby achieving innovations that contribute to solving societal challenges. Traffic Advertising Information Information Real Estate Inform
Slide 47: Appendix
Appendix DYNAMIC MAP PLATFORM W 47
Slide 48: Consolidated Statements of Income
05 ｜ Appendix Consolidated Statements of Income Consolidated Statements of Income FY2026 Q1 FY2027 Q1 Change (millions of yen) Net sales 1,459 1,147 -311 Cost of sales 1,022 1,445 +422 Gross profit 436 (297) -733 SG&A expenses 632 702 +69 Operating profit (loss) (196) (999) -803 Non-operating incom
Slide 49: Consolidated Balance Sheet
05 ｜ Appendix Consolidated Balance Sheet Consolidated Balance Sheet March 2026 June 2026 Change Assets (millions of yen) Cash and deposits 3,658 4,253 +595 Committed line agreements Other current assets 2,953 1,952 -1,000 Secured with two financial institutions, with a total Total current assets 6,6
Slide 50: Trend in Net Sales and Adjusted EBITDA
05 ｜ Appendix Trend in Net Sales and Adjusted EBITDA Net Sales (millions of yen) Adjusted EBITDA (millions of yen) 7,465 7,000 5,567 5,686 FY2024 FY2025 FY2026 FY2027 (Forecast) 4,000 50 6,293 3,092 -501 -609 4,572 -2,203 3,000 2,594 994 1,171 FY2024 FY2025 FY2026 FY2027 (Forecast) License-based rev
Slide 51: Consolidated Results: Quarterly Cumulative Net Sales Trends
05 ｜ Appendix Consolidated Results: Quarterly Cumulative Net Sales Trends Cumulative Net Sales (millions of yen) 7,465 5,686 4,012 6,293 3,092 3,373 5,567 2,248 2,451 3,168 2,094 1,459 3,107 1,506 1,724 2,594 873 1,577 665 1,171 1,279 677 843 944 343 524 586 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 FY202
Slide 52: Revenue Reached JPY 2.3 Billion in Q4, and Adjusted EBITDA Also Improved
05 ｜ Appendix Revenue Reached JPY 2.3 Billion Mainly from Enterprise Licenses in the Q4, and Adjusted EBITDA Also Improved In the fourth quarter, the Company recorded revenues of JPY 2.3 billion, primarily from enterprise licenses. Adjusted EBITDA improved significantly in the fourth quarter, turnin
Slide 53: Understanding Physical AI
05 ｜ Appendix Understanding Physical AI Physical AI refers to AI that operates and interacts in the physical world, including autonomous driving systems and robotics, and differs from conventional AI that operates primarily in digital environments. Since its establishment, DMP has generated and accu
Slide 54: Providing Data to the High-Growth Physical AI Market
05 ｜ Appendix Providing Data to the High-Growth Physical AI Market The physical AI market is expected to achieve strong growth over the next decade, driven by expanding applications in robotics, autonomous driving, and smart infrastructure. DMP is already providing data for AI training and validatio
Slide 55: Contributing to the Social Implementation of Physical AI Through Data for AI
05 ｜ Appendix Contributing to the Social Implementation of Physical AI Through “Data for AI” From a data perspective, there are five major challenges to the social implementation of physical AI, which correspond to five categories of spatial data. The Company develops its Data for AI business by pro
Slide 56: Expansion of Enterprise Licenses Driven by Growing Demand for Data for AI
05 ｜ Appendix Expansion of Enterprise Licenses Driven by Growing Demand for Data for AI In addition to mass-production licenses, enterprise licenses are expanding among major automotive OEM groups, global semiconductor manufacturers, and autonomous driving system developers. Major automotive OEM Glo
Slide 57: Modular E2E AI System
05 ｜ Appendix Modular E2E AI System Multiple variations exist and are evolving step by step in E2E AI-based autonomous driving technology. Due to the heavy processing load, the difficulty of safety validation, and the challenge of securing massive resources required by monolithic E2E AI systems, som
Slide 58: Autonomous Driving Systems and Value of High-Precision Map Data
05 ｜ Appendix Autonomous Driving Systems and Value of High-Precision Map Data As a platform provider that delivers high-precision location information from large volumes of high-quality source data, the Company can offer the value of high-precision map data to any autonomous driving system. We respo
Slide 59: WP.29 Agreement on International Standards for Automated Driving Systems
05 ｜ Appendix WP.29 Agreement on International Standards for Automated Driving Systems and Its Impact on DMP's Business In June 2026, the UNECE World Forum for Harmonization of Vehicle Regulations (WP.29) reached agreement on international standards for automated driving systems, including Level 3 a
Slide 60: Disclaimer
Disclaimer This document has been prepared by Dynamic Map Platform Co., Ltd. (the “Company”) solely for informational purposes. This document contains forward-looking statements, which are predictions about the future that reflect management's judgment based on currently available information. As su