案例研究 | AI 辅助工具 | QGIS | ArchaeoTrace | 插件。AI 辅助的 QGIS 插件能否真正加速等高线数字化，还是仅仅转移了工作量？我们在波兰的一个小型砂矿进行了测试，对比了当前地表与20世纪上半叶的测量数据……
Case Study | AI-Assisted Tools | QGIS | ArchaeoTrace | Plugin Can an AI-assisted QGIS plugin actually speed up contour digitising, or does it just move the work around? We tested it on a small sand mine in Poland, comparing the present-day land surface with a survey from the first half of the 20th century of […]
案例研究 | AI 辅助工具 | QGIS | ArchaeoTrace | 插件 AI 辅助的 QGIS 插件究竟能否真正加速等高线数字化，还是仅仅将工作量转移到了其他环节？我们在波兰的一处小型砂矿进行了测试，对比了当前的地表形态与同一地块在 20 世纪上半叶的测绘数据。对于许多 GIS 分析师而言，数字化历史地形图是一项常见任务。从扫描地图中手动描绘等高线可能耗时、重复，且容易在细微之处出错。一款 QGIS 插件承诺利用计算机视觉以及日益增强的 AI 辅助功能来自动化这一描绘步骤。ArchaeoTrace 便是其中之一，我们使用它重建了波兰一处小型采砂场跨越约一个世纪的地形演变历史。 扫描地图用于描述地表形态 最古老的来源：WIG 1:25,000 测绘图幅 采砂活动的三个阶段 采矿前、中期和现代 人工审核 人工审核仍然必不可少 ArchaeoTrace 的实际功能 ArchaeoTrace 是一款专为从扫描地形图中提取矢量线而设计的 QGIS 插件，主要面向考古和历史制图领域。它集成了多种描绘方法，而非依赖单一算法。拥有多种描绘方法至关重要，因为不同的扫描件对不同方法的响应效果各异。 Ink Centerline（墨迹中心线）：以三个尺度（9、15 和 31 像素）运行，跟随绘制的线条并保持在其中心位置。 Live-Wire（活线）：一种有界的方向感知路径工具（320 像素范围），在您勾勒时自动吸附到附近最强的边缘。 光标到路径混合滑块（0-100%）：允许操作员决定在多大程度上信任自动描绘结果与自身手部移动之间的平衡，而不是接受全有或全无的结果。 可选的智能恢复模式：调用轻量级分割模型（EfficientSAM-Ti），帮助弥合扫描图中等高线模糊或中断处的间隙。 自由手绘及一系列传统和高级方法（LSD、HED、MobileSAM、SAM ViT-B、Canny）可供上述方法表现不佳的地图使用。 描绘的线条可以携带高程属性和点高程，该插件还可以直接从数字化的等高线生成实验性的线性 TIN DEM 及山体阴影。所有处理均在本地运行，无需将数据发送至远程服务器进行推理。 该插件的基础安装目标为 QGIS 3.22 和 Python 3.8，无需额外的 pip 包；可选的 AI 辅助方法需要 Python 3.10+ 环境并安装额外依赖项，这一点值得注意。 案例研究：波兰的一处砂矿，三个时期 为了了解该插件的实际用途，我们选择了一处小型采砂场。我们在大约一个世纪的时间跨度内，重建了其表面在三个阶段的状态——采矿前、开采期间和今天。 采矿前：1929-39 年的基线。最早的来源是图幅 P42 S32 H（亦编目为 4232 H），属于 WIG（Wojskowy Instytut Geograficzny，军事地理研究所）1:25,000 系列《Mapa Szczegółowa Polski》（波兰详细地图），于两次世界大战之间完成测绘。该系列的图幅由波兰新实地测量数据和在测绘较少地区使用的旧基础测绘数据混合编制而成，这就是为什么该图幅上的一些底层线条仍带有早期沙皇俄国测绘留下的西里尔字母地名。对这张扫描图运行 ArchaeoTrace 生成了一套数字化等高线，随后我们利用这些等高线构建了采矿开始前地面的线性 TIN DEM。 中期：采矿开始之时。一张来自 Geoportal 的波兰地形图（1960-80 年代）显示了砂矿开采正在进行时的同一区域。对该图幅进行数字化并从中重建 DEM，得到了第二个表面，大致处于该场地工作寿命的中期。 今天：卫星影像和现代 LiDAR DEM。对于当前的表面，我们无需手动数字化任何内容。Google 卫星正射影像直接展示了坑洞及周边土地，而 1 米分辨率的 LiDAR 衍生 DEM 提供了独立的高程模型，用于校验历史重建结果。 拖动滑块：任选两个时间切片 下方的交互工具在同一色阶上保留了所有三个 DEM：采矿前（1929-39）、中期（1960-80，开采开始后）和今天。使用下拉菜单选择要比较的两个对象，然后拖动分隔线。一旦任意两个表面并列显示，开采留下的凹陷便一目了然。 | 时期 | 来源 | 高程模型 | | :--- | :--- | :--- | | 1929-39 | WIG 1:25,000 图幅 P42 S32 H (4232 H) | 基于数字化等高线的线性 TIN DEM | | 1960-80 | Geoportal 地形图 | 基于数字化等高线的线性 TIN DEM | | 今天 | Google 卫星正射影像；Geoportal 1 m LiDAR | LiDAR 衍生 DEM | 不足之处 没有任何一种描绘方法能在开箱即用的情况下同样出色地处理两张历史图幅。最古老的扫描图具有更细、更不规则的手绘等高线，需要不同于较干净、更具机械绘制风格的中期 Geoportal 图幅的尺度和混合设置组合。没有一种设置适用于所有地图，插件本身也不作此声称。新用户必须在可用算法（以及 Ink Centerline 内的多尺度设置）中进行试验，以找到适合其特定地图的方法；这一试错过程没有捷径可走。 可选的 AI 辅助方法带来了真实的依赖负担：需要 Python 3.10+ 环境和超出 QGIS 自身基线的额外包，这对于试图在受限制机构机器上复现结果的用户来说是一种摩擦。该插件自身的文档对此坦率承认：正如文档所述，“地形输出是可审查的假设，而非考古学上的地面真值。”这句话应被视为字面意思，而非样板式的谨慎声明。我们审查的自动描绘结果仍需修正，无论是将顶点微调回其滑出的等高线上，还是闭合软件因历史墨水褪色而留下的间隙。 结论 ArchaeoTrace 是一款有助于加速……
Case Study | AI-Assisted Tools | QGIS | ArchaeoTrace | Plugin Can an AI-assisted QGIS plugin actually speed up contour digitising, or does it just move the work around? We tested it on a small sand mine in Poland, comparing the present-day land surface with a survey from the first half of the 20th century of the same ground. Digitising historical topographic maps is a familiar task for many GIS analysts. Tracing contour lines by hand from a scanned map can be slow, repetitive, and easy to get subtly wrong. A QGIS plugin promises to automate the tracing step using computer vision and, increasingly, AI assistance. ArchaeoTrace is one of them, and we used it to rebuild the terrain history of a small sand-mining site in Poland across roughly a century. Scanned maps were used to describe the land surface Oldest source: WIG 1:25 000 survey sheet Three stages of sand extraction Pre-mining, mid-century, and present day Manual review Manual review remains essential What ArchaeoTrace actually does ArchaeoTrace is a QGIS plugin built for pulling vector lines out of scanned topographic maps, designed primarily for archaeological and historical cartography. It bundles several tracing methods rather than a single algorithm. Having several tracing methods matters because different scans respond better to different approaches. Ink Centerline , run at three scales (9, 15 and 31 pixels), follows a drawn line and stays on its centre. Live-Wire , a bounded, direction-aware path tool (320px reach) that snaps to the strongest nearby edge as you sketch. A cursor-to-path blend slider (0-100%) lets the operator decide how much to trust the automated trace versus their own hand movement, rather than accepting an all-or-nothing result. An optional Smart Recovery mode calls a lightweight segmentation model (EfficientSAM-Ti) to help bridge gaps where a contour line is faint or interrupted on the scan. Freehand drawing and a set of legacy and advanced methods (LSD, HED, MobileSAM, SAM ViT-B, Canny) are available for maps where none of the above behave well. Traced lines can carry elevation attributes and spot heights, and the plugin can generate an experimental linear-TIN DEM plus hillshade directly from the digitised contours. All of this runs locally, with no data sent to a remote server for inference. The plugin’s baseline install targets QGIS 3.22 and Python 3.8 with no additional pip packages; the optional AI-assisted methods need a Python 3.10+ environment with extra dependencies installed, which is worth checking. The case study: a sand mine in Poland, three time periods To see what the plugin is actually good for, we picked a small sand-mining site. We reconstructed its surface at three stages – before mining, during extraction, and today – across roughly a century. Before mining: the 1929-39 baseline. The earliest source is sheet P42 S32 H (also catalogued as 4232 H), part of the WIG ( Wojskowy Instytut Geograficzny ) 1:25 000 series, Mapa Szczegółowa Polski , surveyed in the interwar period. Sheets in this series were compiled from a mix of fresh Polish fieldwork and older base survey data in less-mapped areas, which is why some of the underlying linework on this sheet still carries Cyrillic place names from an earlier Imperial Russian survey. Running ArchaeoTrace over this scan produced a set of digitised contours, which we then used to build a linear-TIN DEM of the ground before mining began. Mid-century: when mining began. A Polish Geoportal topographic map from the 1960s-80s shows the same area once sand extraction was underway. Digitising this sheet and rebuilding a DEM from it gives a second surface, roughly midway through the site’s working life. Today: satellite imagery and a modern LiDAR DEM. For the present-day surface, we didn’t need to digitise anything by hand. A Google satellite orthophoto shows the pit and surrounding land directly, while a 1-metre LiDAR-derived DEM provides an independent elevation model for checking the historical reconstructions. Drag the slider: pick any two time slices The interactive tool below holds all three DEMs on the same colour ramp: pre-mining (1929-39), mid-century (1960s-80s, once extraction was under way), and today. Use the dropdowns to choose which two to compare, then drag the divider. The depression left by extraction is unmistakable once any two surfaces sit side by side. Period Source Elevation model 1929-39 WIG 1:25 000 sheet P42 S32 H (4232 H) Linear-TIN DEM from digitised contours 1960s-80s Geoportal topographic map Linear-TIN DEM from digitised contours Present day Google satellite orthophoto; Geoportal 1 m LiDAR LiDAR-derived DEM Where it struggles None of the tracing methods worked equally well on both historical sheets straight out of the box. The oldest scan, with its finer, more irregular hand-drawn contour lines, needed a different combination of scale and blend settings than the cleaner, more mechanically-drawn mid-century Geoportal sheet. There is no single setting that suits every map, and the plugin does not pretend otherwise. New users have to experiment across the available algorithms (and the multi-scale settings within Ink Centerline) to find what works for their specific map; there is no shortcut to that trial-and-error step. The optional AI-assisted methods bring real dependency weight: a Python 3.10+ environment and extra packages beyond QGIS’s own baseline, which is friction for anyone trying to reproduce results on a locked-down institutional machine. The plugin’s own documentation is candid about this: as it puts it, “terrain outputs are reviewable hypotheses rather than archaeological ground truth.” That line is worth taking at face value rather than as boilerplate caution. The automated traces we reviewed still required correction, whether that meant nudging a vertex back onto a contour it had slid off, or closing a gap the software had left where the historical ink had faded. The verdict ArchaeoTrace is a useful tool for speeding up the slow part of digitising old maps. It reduced the time needed to produce a first-pass set of contour vectors. For anyone rebuilding terrain history from archival maps as a recurring task rather than a one-off, that time saving adds up fast. Manual review and correction are essential before relying on the resulting DEM for anything consequential. Automated tracing shaves hours off the first draft. It does not replace the second pair of eyes. Sources ArchaeoTrace plugin (QGIS), GitHub: AI-Vectorizer-for-Archaeology README WIG 1:25 000 Mapa Szczegółowa Polski , sheet P42 S32 H (4232 H), 1929-39 Geoportal.gov.pl : historical topographic map series and 1-metre LiDAR DEM Google satellite imagery Did you like this post? Read more and subscribe to our monthly newsletter!