A New Possibility to Work with Open Geospatial Data: Wherobots Cloud, WherobotsDB and Wherobots AI

BADEA, Ana Cornelia; BADEA, Gheorghe · 2025 · Crossref

DOI: 10.29302/revcad.2025.39.1

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Summary

This paper introduces Wherobots Cloud, a spatial intelligence cloud platform designed to streamline the access, processing, and analysis of open geospatial data at a planetary scale. The authors address the limitations of traditional Geographic Information Systems (GIS), which rely on local storage and desktop software, often struggling with massive datasets and requiring specialized expertise. By leveraging cloud computing, Wherobots aims to bridge the gap between complex geospatial data and actionable insights, integrating artificial intelligence (AI) and machine learning to automate tasks such as image analysis and feature extraction. The study positions the spatial intelligence cloud as an evolution of GIS, enhancing capabilities for applications in smart cities, precision agriculture, and disaster management. The platform comprises three main components: WherobotsDB, Wherobots AI, and the Wherobots Spatial Catalog. WherobotsDB is a cloud-native analytics engine optimized for geospatial workloads, supporting vector and raster data through SQL APIs aligned with the SQL/MM Part3 Spatial standard and Apache Sedona. It utilizes the SparkSQL query optimizer and supports programming languages including Python, Scala, and Java. Wherobots AI provides infrastructure-free inference pipelines for satellite and drone imagery, enabling scalable processing without local hardware constraints. The Wherobots Spatial Catalog aggregates open datasets, such as OpenStreetMap and Overture Maps, converting them into an optimized "Havasu" format for efficient analytics. Key features include map matching to correct inaccurate GNSS coordinates and automated workflows for clustering, object detection, and raster segmentation. The authors illustrate the platform’s utility through a case study using the Vehicle Energy Dataset (VED), which contains GPS tracking data from vehicles in Michigan. This example demonstrates how Wherobots can process large-scale location data to derive insights, reducing both time and human effort compared to traditional methods. The platform offers pre-processed open data, lowering the barrier to entry for users by allowing them to integrate publicly available geospatial data directly into their workflows. Additionally, the system supports flexible model integration and pay-as-you-go pricing, facilitating rapid development and cost reduction for geospatial analysis projects. The significance of this work lies in demonstrating that cloud-native platforms complement rather than replace traditional GIS. While desktop GIS remains optimal for geometry editing and static map generation, cloud-based solutions like Wherobots are superior for handling large datasets, slow rendering issues, and cloud-connected data. The paper concludes that spatial intelligence clouds, powered by major cloud infrastructure providers, enable deeper insights from location data by combining scalable computing resources with advanced AI/ML capabilities. This approach represents a paradigm shift in geospatial analysis, making planetary-scale data processing more accessible and efficient for organizations and researchers.

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