How do you manage drone, GIS and survey data in one place?
TL;DR: Managing drone, GIS and survey data in one place means storing outputs like orthomosaics, point clouds and survey files in a shared, cloud-based system everyone can access with the same version of the truth. This replaces scattered drives, desktop GIS software and email attachments with one map that specialists and non-specialists can both use, comment on and export from.
Key takeaways
- According to FMI and Autodesk's joint research, 95.5% of the data captured on construction and engineering projects goes unused, largely because it sits in disconnected systems nobody else can open.
- A McKinsey study cited by GIS consultancy Blue Raster found 80% of companies admit to segregated IT systems, and 62% have no defined process for integrating new and existing data sources.
- Point clouds, orthomosaics and survey files each come from different software and often use different formats (LAS/LAZ, GeoTIFF, LandXML, shapefiles), which is why centralizing them takes more than just a shared folder.
- Open standards from the Open Geospatial Consortium (OGC), including the LAS specification for point clouds, exist specifically to let this data move between tools without losing accuracy.
Why do drone, GIS and survey data end up scattered across different tools?
Drone data, GIS layers and survey files end up scattered because each is usually captured and processed in its own specialist tool, by a different person, for a different immediate purpose. Nobody sets out to create silos; they form gradually as more data sources and more contributors join a project.
A typical site might have drone imagery processed in Pix4D or DroneDeploy, cadastral and asset layers held in a desktop GIS package, and survey data delivered as LandXML or DXF files from a third-party surveyor. Each of these tools is built to do its own job well. None of them is built to be the single place a project manager, a client or a field crew goes to see everything at once.
The result is what FMI and Autodesk describe in their "Harnessing the Data Advantage in Construction" research: 95.5% of the data captured on projects goes unused. That is not because the data is worthless. It is because it stays locked in a format or a login that only one specialist can access, so nobody else ever sees it, let alone acts on it. The same research links poor data management to USD 1.84 trillion in losses across the US construction industry in a single year, with 14% of all rework attributed to bad or unavailable data.
What does "managing data in one place" actually mean for geospatial teams?
For a geospatial team, managing data in one place means every output, drone imagery, GIS layers and survey files, lives in a single shared environment that specialists and non-specialists can both open, rather than in separate desktop applications or personal drives. The test: can a project manager or client find current data without asking a specialist to export it?
This is a different goal from "buying a bigger GIS system" or "getting everyone the same software license." A construction site manager does not need to learn ArcGIS to check this month's drone survey against last month's. What they need is a map they can open in a browser, with the layers already loaded and up to date.
That distinction matters because it changes what "centralizing" data actually requires. It is less about consolidating processing tools (which will always be specialized) and more about giving every output, wherever it was produced, a single, accessible home once it is ready to be shared. Blue Raster, a GIS consultancy that works with construction and infrastructure clients, notes that when GIS and project data stay siloed, staff lose real time re-entering or reformatting data that already exists somewhere else in the organization, time that could go toward analysis instead of admin.
What file types and formats do you need to bring together?
Bringing drone, GIS and survey data together means handling several file types: orthomosaics and DEMs from drone processing, point clouds in LAS or LAZ format, vector layers such as shapefiles or GeoJSON, and survey deliverables like LandXML or DXF. Each format was built for a different tool, which is why they rarely open together without a translation step.
Orthomosaics and digital elevation models (DEMs) are typically the outputs of drone photogrammetry software after processing raw aerial imagery. Point clouds, whether from drone LiDAR, terrestrial laser scanning or photogrammetry, are commonly delivered as LAS or its compressed form LAZ, the open point cloud standard maintained under the American Society for Photogrammetry and Remote Sensing (ASPRS) and recognized by the OGC. Survey teams, meanwhile, tend to work in LandXML or DXF for design surfaces, alignments and cadastral boundaries, formats built for CAD and civil engineering workflows rather than web-based mapping.
None of these formats is wrong for its purpose. The problem is only that a system built to centralize this data needs to read all of them, at their original resolution, without forcing every contributor to convert their files by hand before they can be shared.
What are the risks of keeping drone, GIS and survey data in silos?
The main risks of siloed geospatial data are version confusion, wasted rework, and decisions made on outdated information because the people who need to see current data cannot access it without going through a specialist. These risks compound as more people and more capture methods get added to a project.
Version confusion is the most common failure mode. When a drone survey lives on one person's desktop and a design surface lives in someone else's CAD file, there is no single reliable answer to "which version is current." Teams end up comparing screenshots in email threads or relying on whoever last remembers what changed. On a live construction or mining site, that lag can mean progress is reported against the wrong baseline, or a hazard flagged in a drone survey does not reach the people working near it.
The rework cost is well documented: FMI's research attributes 14% of all construction rework globally to insufficient or inaccessible data, not to errors in the data itself. Fixing this is rarely a data quality problem. It is an access problem, solved by giving people a single place to look rather than asking them to hunt across systems.
How do you centralize drone, GIS and survey data without losing control?
You centralize drone, GIS and survey data by moving finished outputs into a shared, cloud-based platform with role-based access, while leaving specialist processing tools in place for the work they do best. This means specialists keep the tools they need, and everyone else gets a single map to check instead of a request queue.
In practice, this usually looks like a two-layer setup. The first layer is unchanged: drone pilots still process imagery in their photogrammetry software of choice, surveyors still deliver LandXML from their preferred package, and GIS specialists still do deep spatial analysis in a full GIS suite where that is genuinely needed. The second layer is new: once an output is ready to share, it gets uploaded to a shared visualization and collaboration platform where anyone with the right permission, whether a project manager, an executive, or an external client, can view it, comment on it, and export a report from it, without needing the originating software installed.
Role-based access is what makes this safe rather than chaotic. A client might get a view-only link to check progress. A field crew might get commenting rights to flag an issue directly on the map. A GIS specialist might retain full edit and export access. Centralizing does not mean flattening these distinctions; it means giving each person the right level of access to the same underlying data, instead of a different, possibly outdated, copy of it.
What role do open standards play in a centralized workflow?
Open standards matter because they let drone, GIS and survey data move between systems without losing accuracy or requiring proprietary software at every step. The Open Geospatial Consortium (OGC) maintains more than 80 specifications for exactly this purpose, including the LAS specification that has become the default way point cloud data is exchanged across LiDAR hardware and software.
Without open, well-supported formats, "centralizing" data would just mean forcing everyone onto one vendor's proprietary system, trading one kind of lock-in for another. A platform built around open standards like LAS/LAZ for point clouds, GeoTIFF for raster imagery, and common vector formats for GIS layers can ingest data from whichever tool a specialist prefers, which is what makes it possible to keep specialist workflows intact while still centralizing the outputs.
This is also why format support is worth checking carefully before choosing a system. A platform that only handles a narrow set of formats will recreate the exact silo problem it was meant to solve, just with fewer tools able to contribute to it.
How to choose a way to centralize your geospatial data
The right approach depends on how much specialist GIS work your team does versus how many non-specialists need access to the outputs. Teams that live inside a full GIS suite may prefer a heavier enterprise platform. Teams where only one or two people are trained specialists, but many more need to see the map, are better served by a lighter, cloud-based collaboration layer alongside their existing processing tools.
Key things to check before committing: does the platform read your actual file types (LAS/LAZ, LandXML, GeoTIFF, shapefiles) without forcing conversion; does it support view-only sharing for clients who should not need a login or training; and does it keep a record of comments and changes so teams are not relying on email threads to track decisions.
Birdi is one option worth considering for teams in this position: it is a collaborative geospatial platform built to sit between specialist tools like Pix4D, ArcGIS or DroneDeploy and the wider business, letting non-specialists view, comment on and export from the same map without needing GIS training. Construction teams like Mirvac have used it to save an estimated 32 hours per site each month on progress reporting, and utilities like CitiPower and Powercor have used it to speed up pole inspection reporting roughly fourfold. It is not a replacement for a full GIS suite or heavy spatial analysis, so an organization that needs deep enterprise GIS capability across a large specialist team may still be better served by a platform like ArcGIS. You can see how the GeoAI feature set or core features fit a specific workflow before deciding.
Frequently asked questions
What is the difference between GIS data and survey data?
GIS data typically refers to spatial layers used for mapping and analysis, such as boundaries, asset locations or land use, often in vector formats like shapefiles. Survey data usually refers to precise measurements captured by a licensed surveyor, delivered in formats like LandXML or DXF, and used for design, construction staking or legal boundaries.
Can drone data be integrated with existing GIS systems?
Yes. Drone outputs like orthomosaics, DEMs and point clouds can be imported into most GIS systems, provided the formats match, commonly GeoTIFF for imagery and LAS/LAZ for point clouds. The main integration challenge is less about compatibility and more about keeping the imported data current as new drone surveys are captured.
What file formats should a centralized geospatial platform support?
At minimum, a centralized platform should support LAS/LAZ for point clouds, GeoTIFF for orthomosaics and DEMs, common vector formats like shapefiles or GeoJSON for GIS layers, and LandXML or DXF for survey and design data. Support for these open, widely used formats avoids forcing teams to convert files before sharing them.
Do non-technical team members need GIS training to view centralized drone and survey data?
No, not if the platform is built for that purpose. Cloud-based geospatial platforms are designed to let non-specialists view maps, add comments and export reports through a simple interface, while GIS specialists retain access to deeper analysis tools. Training is only needed for the specialist processing and analysis work, not for viewing shared outputs.
How much data does poor data management actually cost a project?
According to FMI and Autodesk's research, poor data management contributed to an estimated USD 1.84 trillion in losses across the US construction industry in a single year, with 14% of all rework linked to insufficient or inaccessible data. The scale varies by project, but the underlying cause, data that exists but cannot be found or trusted, is consistent across industries.
Sources
- FMI Corporation and Autodesk. "Harnessing the Data Advantage in Construction." FMI Corp, 2020. https://fmicorp.com/insights/thought-leadership/beyond-the-buzz-harnessing-the-power-of-data-analytics-in-construction
- Blue Raster. "Solving Construction Data Silos with GIS Data Interoperability." Blue Raster, 2024. https://blueraster.com/stories/gis-data-interoperability-construction-engineering/
- Open Geospatial Consortium. "LAS Specification – Open Format for LiDAR Point Cloud Data." OGC, 2024. https://www.ogc.org/standards/las/
- Open Geospatial Consortium. "OGC Standards." OGC, 2026. https://www.ogc.org/