DSH-WebGIS

1. 安装并启动 DeepSeek Harness：npx @deepseek-ai/dsh web
2. 在终端执行上面的安装命令（CLI 会解析插件并核验来源）
3. 用 dsh plugins list 确认已安装，必要时重启 Harness 生效

插件以当前 dsh 进程的权限运行，安装时可能执行代码。请先通读仓库源码与许可证，确认无破坏性命令与越权访问；本站只做索引，不对第三方插件安全性作担保。

事实信息来自公开插件目录快照（2026-10-01），介绍文案由本站再加工。

以下为插件仓库 README 全文（原始内容，由公开目录抓取整理）。

# dsh-webgis

A WebGIS plugin that lets LLMs truly see the geographic world. Built on DeepSeek Harness (DSH), it embeds a full GIS workbench into an AI conversation: models can load map data, understand maps, and run spatial analysis through dialogue. Its long-term goal is genuine 3D spatial understanding.

Author: Frank Wang · Feedback: [cywanghn@gmail.com](mailto:cywanghn@gmail.com)

## What's New in 0.1.4

- Added a Worker-based execution path for geometry and spatial-statistics operations, selected by operation-specific workload thresholds.
- Worker jobs forward cancellation signals, enforce time budgets, and terminate on completion, cancellation or timeout. Plugin disposal requests cleanup of active workers and queued jobs.
- Added transferable geometry payloads, bounded job concurrency, shared timeout policies, and regression tests for encoding, dispatch, cancellation and timeouts.
- Added a regular-grid size guard with actionable input guidance.

Scope: small workloads still execute synchronously on the host thread. Workload thresholds do not guarantee that every complex geometry runs off-thread; this release does not promise universally non-blocking execution. Worker payload encoding and result decoding also require host-thread work.

Compatibility: this release targets the DSH **Web profile** and requires the host's `webServer` service. The official Desktop transport is **not yet supported**. The existing DSH `0.1.5-rc1` compatibility baseline is unchanged; this is not a claim of validation against newer Desktop releases.

### What's New in 0.1.3

- Migrated to DuckDB's official Node Neo driver (`@duckdb/node-api`). Installing the plugin no longer requires compiling the legacy `duckdb` native module, `pnpm approve-builds`, or `pnpm rebuild duckdb`.
- Optimized very large CSV coordinate-point layers: DuckDB reads coordinates in columnar chunks and builds GeoArrow directly, avoiding huge numbers of `{ lon, lat }` JavaScript objects.
- Sampling, viewport culling, empty-view behavior, and picking semantics remain unchanged. Line, polygon, and geometry-column layers continue to use the established WKB conversion path.

### What's New in 0.1.2

- Adapted for DSH `0.1.5-rc1`: retargeted the new shell layout (the conversation moved into the host `main` slot) and migrated to the 0.1.5 package set.
- Export vector features from the basemap. “Export the map data in the current view” extracts real vector-tile features into new point, line, and polygon layers; a request such as “export the rivers” can limit the output to one kind.
- Added a spatial-statistics suite: Gini, Shannon entropy, and Getis-Ord Gi\* hot-spot analysis with Benjamini–Hochberg FDR correction, in addition to Moran's I. Each index inspects fields, reports what it finds, and asks you to confirm inputs before computing.
- Unified click highlighting: polygons use a deep-blue outline and blue fill, lines use a thick blue stroke, and points use blue dots, always rendered on top.

### What's New in 0.1.1

- Fixed the “API key required” watermark on the default Carto raster basemap.
- Fixed Carto vector basemaps (Positron / Dark / Voyager) not rendering.
- Added a Measure tool: line length, snapping to previously drawn vertices, and closing a polygon at its start point to show perimeter and area.
- Improved loading performance for very large SHP files.

Tested against DSH `0.1.5-rc1`.

Install: `npx --yes @deepseek-ai/dsh plugin --profile web add dsh-webgis`

### pnpm and the DuckDB runtime

The plugin uses DuckDB's official Node Neo package (`@duckdb/node-api`). It installs a platform-specific prebuilt binding through optional dependencies, so pnpm ≥ 10 does **not** need `pnpm approve-builds` or `pnpm rebuild duckdb`. A normal `pnpm install` is sufficient. The target platform must still be supported by DuckDB Node Neo and be able to download packages from the npm registry.

## Highlights

- 🗺️ Conversational GIS — load data, navigate maps, and run spatial analysis through natural-language tool calls
- 🚀 Smooth massive-data rendering — from million-row local CSV files to database results with hundreds of thousands of rows, a tiered rendering pipeline keeps the map responsive (see [Massive Data Loading](#massive-data-loading))
- 🧮 GIS toolbox — 60+ AI-callable tools: buffers, overlays, kernel density, Moran's I, Gini, Shannon entropy, Getis-Ord Gi\*, OD matrices, and hex-bin heatmaps
- 🛰️ Read the basemap too — extract rivers, roads, buildings, place names, and other real vector-basemap features into analysable layers
- 🖌️ Manual workflows — interactively draw points, lines, and polygons; manage layers; import and export SHP / CSV / GeoJSON
- 🔌 Multiple data sources — PostGIS, local files, the basemap itself, and online map services

---

## Quick Start

1. Open DeepSeek Harness and start a new conversation.
2. Select GIS mode.
3. Choose your task workspace.
4. Ask the model, for example: *“Load xxx and show it on the map.”*

---

## Features

### Compute: conversational GIS analysis

Once data is loaded, tell the AI something like *“show this as a hex density heatmap”* or *“which points fall inside this polygon?”*. More than 60 geo-processing tools can be chained behind the scenes:

- Construct — buffer, centroid, convex hull, bounding box, dissolve, simplify, explode, smooth, grid, Voronoi, and OD matrices that show origin–destination flows
- Overlay — clip, intersect, difference, and union
- Query — attribute filtering, spatial joins, and selection by location
- Transform — coordinate-system reprojection
- Spatial statistics — kernel-density heatmaps, average nearest neighbor (ANN), global Moran's I with LISA local clusters, Gini, Shannon entropy, and Getis-Ord Gi\* hot-spot analysis
- Visualization — raw points, planar heatmaps, hex-bin heatmaps, arcs, trips, walls, and radial modes; adjust colors, sizes, and stroke widths
- Attribute editing — batch-update field values, assign sequence numbers, and add columns
- Basemap extraction — turn vector-basemap features into layers (see [Reading the Basemap](#reading-the-basemap))
- Layer management — list, remove, show/hide, inspect layer details, and view feature statistics

Every result becomes a new live layer that the next tool can continue to process, forming a complete analysis chain.

Indices ask before computing. Statistical tools follow an *inspect → confirm → compute* loop: a Moran's I request first reports numeric fields, missing-value counts, and the geometry family, then waits for confirmation. If a required parameter is missing, the tool explains what it needs instead of guessing.

### Reading the Basemap

Vector basemaps contain real feature data: waterways, roads, buildings, parks, land use, boundaries, points of interest, and place names. The plugin can extract features from tiles in the current view into normal layers:

> - “Export the map data in the current view” → three layers: points / lines / polygons
> - “Export the rivers in this view” → one layer, with same-named segments merged into one feature

The extracted layers behave like any imported data: measure, buffer, and analyse them. This turns visible map content into layers; it is not bulk extraction. Vector tiles are cut by zoom level, so farther zoom levels expose fewer layers, and POIs/buildings generally appear only at city or street zoom. For bulk data, use Geofabrik or Overpass and import a file.

### Draw: manual drawing and editing

- The layer panel lists datasets, analysis results, database results, and imported layers, with show/hide and delete controls
- The Create button opens a drawing toolbar on demand: points, lines, polygons, Bézier smoothing, and snapping to existing-layer vertices
- Right-click a layer to inspect its attribute table, export GeoJSON / CSV / SHP, or delete it
- Import SHP (`.shp` / `.zip`), CSV, or GeoJSON; CSV automatically detects a WKT geometry column and otherwise tries longitude/latitude columns

### Maps and data services

- Basemap switcher — Carto Light / Dark / Voyager, OpenFreeMap, and satellite imagery, selectable from the lower-left corner
- Online overlay services — register WMTS / WMS / XYZ raster services; WMS supports the `{bbox-epsg-3857}` template
- Data-source routing — prefer data already on screen, and query the configured database only when needed; the AI does not invent data

---

## Massive Data Loading

From hundreds of rows to millions, the UI should remain responsive. The core idea is “count first, render in tiers, materialize on demand”, rather than pushing everything to the browser at once.

Huge local CSV files (DuckDB):

- A CSV is loaded once into a DuckDB in-memory table, with millisecond-level filtering afterwards; 1.68 million rows loaded in roughly 2.7 seconds in our tests
- Large files initially show a sampled view with clustering, while the full table remains in memory for later SQL, conditional, or polygon-fence filtering
- Coordinate point layers read coordinates in DuckDB columnar chunks and build GeoArrow without million-scale JS row objects; the client still controls fetch volume by zoom tier and viewport
- Built-in memory management — LRU table eviction plus a memory limit — helps prevent host OOM failures

PostGIS databases:

- Read-only queries; a `count` is issued first to decide whether and how to load results
- Non-4326 geometries are automatically wrapped in `ST_Transform`; EWKB geometries are converted to GeoJSON for display
- `statement_timeout` and read-only transactions prevent slow or misdirected queries from blocking the conversation

Sampling is always transparent: when a large layer displays only a sample, it is labelled “N rows total (sampled)”, and statistics derived from the sample are flagged as such. The AI should never mistake a sample for the full dataset.

Combined with deck.gl's GPU-driven 3D rendering — hex columns, wall extrusions, and OD arcs — hundreds of thousands of rows remain smooth to zoom, filter, and analyse.

---

## Installation and Configuration

Requirements: a DSH installation with the web profile, plus `pnpm` on your PATH.

```bash
dsh plugin --profile web add dsh-webgis
# After installation restart with: dsh web
# Then start a new conversation and select GIS mode.
```

Optional configuration (Settings → Plugins → WebGIS plugin configuration, or the plugin configuration file `cordis.patch.yml`):

- Vision model — provider / model / baseURL / apiKey. Configure this for visual features such as “where is this place?” and reading a map from a screenshot. If the main model is multimodal, the plugin uses it directly and this can remain empty. If the main model is text-only and no vision model is configured, visual tools still return structured coordinate and feature data, but do not analyse images.
- PostGIS database — host / port / database / user / password. Passwords are stored in DSH's credential store and never written to disk in plaintext.
- DuckDB thresholds and memory limit.

### Example prompts

> - “Load this `xxx.shp` onto the map”
> - “Are these points spatially clustered? Show them as a hex heatmap”
> - “Filter the points from `dataset A` that fall inside `region B`”
> - “Export the map data in the current view”
> - “Is the population of these counties clustered, or random?” (runs Moran's I)
> - “Where is this place?” (the AI captures a screenshot and analyses it)

---

## Roadmap: from 2D maps to the 3D world

- Phase 1 (in progress): 2D map-vision understanding — the AI reads map content and answers “what is where” and “where is this place”.
- Phase 2 (in progress): conversational operation and analysis — moving from seeing to computing, closing the load → analyse → visualise loop.
- Phase 3 (long-term): genuine 3D-world understanding — moving from reading 2D symbols to understanding buildings and terrain, occlusion and spatial relations, and real-world-scale reasoning. Today's 3D visuals — hex columns, wall extrusions, and OD arcs — are only the beginning. The goal is for AI to see, reason, and answer within real 3D scenes with a genuine spatial worldview.

> Long-term vision: not merely describing a picture, but giving the model spatial cognition that understands the 3D world behind the map.

> Note: DeepSeek vision models are still evolving quickly, and vision-related features have not yet received extensive deep testing.