Time: 13:30 PM - 14:30 PM
Technical level: Intro / Medium
Accurate building height information is critical for use cases like telco wireless coverage optimization, urban planning, infrastructure management, and risk assessment. Traditional methods for extracting this data often involve complex workflows, heavy infrastructure, and significant processing time. This presentation introduces a modern, cloud-native approach that leverages the scalability and performance of Snowflake & Databricks combined with LiDAR-derived High-Resolution Digital Elevation Models (HRDEM) and HRDEM Mosaic datasets, an open dataset from the Canadian Government.
We will demonstrate how to efficiently store, query, and process large geospatial datasets in cloud-native platforms, eliminating the need for on-premises infrastructure. By integrating HRDEM and HRDEM Mosaic data, we can accurately calculate building heights at scale, enabling advanced analytics and decision-making in industries such as telco and insurance. The session will cover key steps, including data ingestion, transformation, and height computation using SQL, python, the geospatial capabilities of cloud-native platforms and third parties geospatial libraries.
Attendees will gain insights into:
Best practices for managing LiDAR-derived elevation data in a cloud-native environment.
Techniques for extracting building height information with precision and efficiency.
How this approach supports scalable, cost-effective geospatial analytics.
Explore how cloud-native architectures are transforming geospatial workflows and unlocking new possibilities for urban intelligence.
Room: C