The Future of Drone Data Storage: Cloud Workflows, Metadata & Big Data Management
- Anvita Shrivastava
- 2 days ago
- 6 min read
As the use of drones in mapping with aerial photography and LiDAR becomes more commonplace, new opportunities have arisen to do so with high volumes of data; however, there is also a need to have methods to manage, organize, and transmit this large amount of data from drones efficiently through sharing and long-term storage or archival methods. This blog discusses new ways that modern-day processing options, such as cloud storage, various forms of metadata, and big data management approaches, will shape the future of the storage of drone data. The article also includes a discussion on how software such as GeoExpress by LizardTech and MrSID is still quite relevant and can be utilized within next-generation workflows.
Why Drone Data Needs Smarter Storage Solutions
Explosion of Data Volume
Modern drones — especially when capturing high-resolution imagery or LiDAR point clouds — generate hundreds of gigabytes (or more) per flight. Simple storage of raw GeoTIFFs or uncompressed point clouds quickly becomes unsustainable. Without compression and smart management, organizations end up with massive storage bills, slow retrieval times, and inefficient workflows.
Demand for Fast Access + Multi-Resolution Use Cases
Drone data isn’t just archived — it’s used for many purposes: mapping, inspections, 3D modeling, change detection, urban planning, GIS analysis, and more. These use cases often require fast, random access to different spatial subsets (not necessarily the whole dataset) or lower-resolution “overview” data for quick preview, while preserving high-resolution data for analysis.
Collaboration, Sharing & Cloud-Native Workflows
In modern projects, data often needs to be shared with stakeholders: GIS analysts, decision-makers, clients, or public servants. Cloud workflows (on AWS, Azure, GCP, or private cloud) are increasingly common. Effective metadata and data-management strategies — including cataloging, tagging, versioning, and indexing — become essential to make data discoverable, manageable, and interoperable.
All these demands drive the need for compression, efficient formats, metadata hygiene, and scalable storage/serving frameworks.
GeoExpress and MrSID, Solutions to Core Geographic Data Problems
The Emergence of Cloud-Based Tools and Data Formats. The emergence of new cloud-based tools and formats does not lessen the continued utility of established geospatial formats and tools that fulfill core needs for archive, delivery, and compatibility. For this reason, the use of MrSID and GeoExpress from LizardTech continues to make sense for geospatial professionals.
What is MrSID (and GeoExpress)?
MrSID (Multiresolution Seamless Image Database) was developed to support the needs of GIS raster imagery and uses a proprietary wavelet algorithm for the compression of such images.
MrSID uses both lossless and lossy compression methods, thus allowing for either an exact image file (lossless) or an optimized, smaller (lossy) one to be created based on the user's needs.
The latest major release of MrSID (version 4 or MG4) also adds support for LiDAR and multispectral datasets. As such, it will fit well with a majority of datasets created by drone technology.
The software developed by LizardTech for encoding imagery and point clouds into MrSID (or other supported formats), providing options for editing and manipulating these types of two-dimensional and three-dimensional data (to include reprojection, mosaicking, color balancing, cropping, and so on), is GeoExpress.
Benefits of Using GeoExpress + MrSID for Drone Data
Benefit | Explanation |
Massive storage savings | MrSID compression can reduce file size significantly compared to raw GeoTIFF or uncompressed LiDAR, making storage and transmission more manageable. |
Multi-resolution access | Because MrSID stores data at multiple resolutions internally, you can quickly access lower-resolution overviews for browsing, while preserving full detail for analysis. |
Interoperability with GIS tools | MrSID is supported in many major GIS and CAD platforms (e.g., ESRI ArcGIS, ERDAS, Global Mapper, QGIS, etc.) — facilitating easy integration into existing workflows. |
Support for imagery + LiDAR | With MG4, drone users working with both aerial images and point clouds can compress both into one unified, efficient format. |
Simplified delivery & sharing | Compressed MrSID files (or converted to other deliverable formats) are easier to share, stream, or host — especially in web or cloud environments. |
Integrating Modern Cloud & Big-Data Practices: What’s New (and What’s Complementary)
MrSID continues to be an important benchmark, but drone-based data workflows have been updated substantially over time as advancements have occurred in the use of cloud-based technologies, and in particular, through the introduction of new software products focused on the use of cloud or "big data" technologies.
Storage Formats Optimized for Cloud-Based Solutions -- Storage Protocols like Zarr, Cloud Optimized TIFF, and Storage Units
As more new cloud-based storage systems are developed and used, many of the recent technologies incorporate a number of emerging best practices for creating cloud-optimized storage protocols and systems, including chunked and lazy loading storage, parallel processing, streaming, and real-time access, and metadata-based catalogs. Please see: For example, the Zarr protocol (available in Amazon AWS S3, Google Cloud Platform, Microsoft Azure) provides for compressed storage of data in chunks, allowing for the easy creation of multi-dimensional arrays and Geospatial datasets collected from UAVs.
The use of these storage protocols allows for easier execution of distributed analytic workflows using Dask and Xarray, enables collaborative workflows among multiple users of the same dataset, and allows for on-demand and scalable access to datasets stored in cloud-optimized formats. These capabilities are particularly advantageous for organizations maintaining UAV fleets, conducting periodic data collections (i.e., surveys), and maintaining large data repositories of drone-acquired datasets.
Big Data Management Layers and Metadata Cataloguing
For organizations that gather large volumes of data (such as recurring drone flights, old-style laser-detection scans, many different types of multispectral imaging, and others), conventional compressed file formats simply do not provide enough information. Instead, organizations need to implement the following:
Metadata Management - Data should be tagged and classified according to its spatial extent, acquisition date/time stamp, sensor type, resolution, project metadata, licensing, and so on.
Indexing and Searchability - Queries should be able to return results like: "show me all flights taken over site X between 5-2025 and 10-2025, including LiDAR data? or "show me all orthomosaics made at 5 cm/px or less resolution?"
Version Control, Tracking, and History - This is especially important if the organization plans to perform time series analysis, detect changes, or respond to regulatory audits.
Large-Scale Application BackEnd Storage - A scalable, distributed cloud storage solution + database should be able to handle a large number of users, massive amounts of geospatial data, and the various patterns of use (streaming vs batch processes) that organizations may wish to utilise.
Recent academic research is also aligned with this type of approach. For instance, a product like GeoRocket (a scalable cloud data storage system for large volumes of geospatial dataset files) is designed to support massive amounts of information by processing each section of the data stream in an independent manner. This means the system is compatible with the Big Data workflow of working with multiple different types of geospatial file formats.
Main Recommendations for Creating Future-Proof Workflow for Drones Data Storage
If you are currently working with drone data, or if you are planning on setting up the infrastructure for your drone operations, then this is one way to start:
Adopt a Hybrid Data Strategy - Utilize Cloud Optimized Chunked Formats (like Zarr), Catalog Metadata for Active Processing and Analysis, Store Master Copies as MrSID Files Using GeoExpress for Long Term Storage and Distribution.
Create Strong Metadata Practices - Keep an Accurate Record of Key Metadata for All Flights and/or Surveys - including Date/Time, Sensor Type, Resolution, Geolocation, Project Name, Licence and Processing History. This will allow for easier future retrievals, sharing, compliance, and analysis.
Apply Version Control and Indexing - If the data will be accessed again (for example: to check for changes or conduct temporal analysis), make sure that your data storage back-end has version control features built in to enable easy accessing and working with historical versions of your data, spatial indexing to quickly locate where to search in your dataset, and efficient querying capabilities.
Use Multi-Resolution and Tiling for Delivery/Sharing with Clients - Do Not Send Clients Complete Raw Datasets. Instead, Share Only Tiles or Lower-Resolution Overview Maps of the Data. This Way, Clients Will Only Download What They Need.
Think Long-Term Scalability When Planning Your Drone Operations - The Volume of Data Collected Over Months/Years Will Increase Dramatically. Choosing Scalable Storage (Cloud-Based or Object Store) and Storing Your Data as Chunked Files and Building a Metadata-Driven Management System for Your Data Will Avoid Major Operational Headaches in the Future.
The future of drone storage is best served by implementing flexible solutions that integrate cloud-native capabilities and compressed archives associated with their processing methods. New technologies and formats continue to emerge related to cloud-based, massive data processing, yet technologies such as GeoExpress and MrSID (available at LizardTech) have proven themselves over the course of many years to be reliable, efficient, supported by many different organizations, and successful in supporting the long-term development of the geospatial industry.
Hybrid methods, such as those that use modern practices related to the processing and delivery of drone imagery, along with established means of compressing and delivering large datasets, offer the simplest route forward for organizations that are responsible for managing drone-based imagery and LiDAR datasets (also known as LiDAR). Hybrid methods will be scalable, cost-effective, and backed by the strength of both past and present technologies.
For more information or any questions regarding the drone data, please don't hesitate to contact us at:
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