Digs 将楼层平面图转化为 AI 可读的建筑智能数据
了解 Digs 如何实现高质量蓝图标注的规模化，助力 AI 驱动的建筑流程。
2026年9月1日 | 美国
执行摘要
随着 Digs 不断扩展面向住宅建造商和房主的 AI 驱动协作平台，该公司需要一种可靠的方式，将复杂的平面图和蓝图图像转化为其 AI 系统可以使用的结构化数据。
通过与 Uber AI Solutions (UAIS) 合作，Digs 建立了一套灵活的数据标注流程，可根据蓝图标注要求的变化进行调整。此次合作保持了高质量的数据标注，将支持范围扩展到更细分的建筑元素，提高了运营可见性，并为持续推进 AI 产品开发奠定了可复用的基础。
关键结果
99.5%+
注释质量保持良好
1,000+
新增的房间名称识别任务
Building AI-powered construction workflows depends on sophisticated data annotation
Digs 正在打造一款由人工智能驱动的应用，帮助住宅建造商和业主整理施工文件、协作制定方案、自动流转任务，并维护房屋的数字档案。为了让这些流程顺利运行，该应用首先需要理解住宅平面图和蓝图，而这些图纸在布局、符号、尺寸、朝向和建筑细节等方面差异很大。
As Digs introduced new AI capabilities, its data annotation needs became more sophisticated. Work that began with room segmentation expanded into room naming, measurements, wall interpretation, and other blueprint elements. The challenge was not simply labeling more images. Digs needed a data annotation operation capable of adapting as the product evolved while also preserving downstream consistency builders and homeowners depended on.
Finding an annotation partner that could adapt as the product evolved
Digs partnered with Uber AI Solutions (UAIS). The UAIS team quickly aligned with Digs’ existing technical environment and evolving workflows, rather than forcing them into a rigid operational structure. UAIS assembled a dedicated annotation team and worked within Digs’ established review process. As requirements changed, workflows were adjusted incrementally. Adjusted workflows supported rotated layouts, updated segmentation rules, room naming, measurements, and increasingly specialized blueprint elements without rebuilding the operation from scratch.
The collaboration also addressed an operational visibility gap. Because review and ingestion processes existed across separate systems, blocked work and emerging issues could be difficult to identify quickly. UAIS introduced dashboard reporting and automated Slack updates that surfaced queue status, active operations, and blocked items, giving both teams greater day-to-day visibility while reducing manual coordination.
Sustaining 99.5%+ blueprint annotation accuracy as AI requirements expanded
The engagement established a stable annotation operation capable of supporting changing blueprint requirements while sustaining greater than 99.5% quality across core workflows.
Reporting visibility made it easier to identify workflow issues and coordinate production, while the flexible operating model allowed Digs to introduce new annotation tasks as its AI product evolved. For example, the new workflow was able to easily absorb an add-on project of over 1,000 tasks for room name identification.
凭借在管理复杂标注项目和适应不断变化需求方面的运营经验，UAIS 使 Digs 能够扩展结构化施工数据的应用，无需每次标注要求变动时都建立新的内部流程。
可扩展的基础，支持 AI 驱动的施工工作流程
住宅建筑领域的 AI 系统依赖于对高度多样化的视觉文档进行准确解读。要构建这些功能，需要数据标注流程能够随着产品本身的发展不断调整。
通过与 Uber AI Solutions 合作，Digs 建立了一套可靠的蓝图数据标注工作流程，能够响应不断变化的需求，保持质量，并在不增加不必要复杂性的情况下扩展运营。如今，Digs 正在为现代住宅施工与居住打造 AI 驱动的工作流程。
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