LiDAR 3D Point Cloud of Geospatial Data

Demonstrates how to visualize LiDAR UAV Data from the Defra survey using SciChart.js. A 1km x 1km slice of London is visualised as a 3D point-cloud with contour map overlaid. A heatmap legend on the right indicates the heightmap.

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drawExample.ts

ExampleDataProvider.ts

angular.ts

theme.ts

AscReader.ts

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1import {
2    CameraController,
3    EColorMapMode,
4    EDrawMeshAs,
5    EMeshPaletteMode,
6    ETitlePosition,
7    GradientColorPalette,
8    HeatmapLegend,
9    linearColorMapLerp,
10    MouseWheelZoomModifier3D,
11    NumericAxis3D,
12    OrbitModifier3D,
13    PixelPointMarker3D,
14    ScatterRenderableSeries3D,
15    SciChart3DSurface,
16    SurfaceMeshRenderableSeries3D,
17    TLinearColorMap,
18    UniformGridDataSeries3D,
19    Vector3,
20    XyzDataSeries3D,
21    zeroArray2D,
22} from "scichart";
23import { AscData, AscReader } from "./AscReader";
24import { appTheme } from "../../../theme";
25import { fetchLidarData } from "../../../ExampleData/ExampleDataProvider";
26
27type TMetadata = {
28    vertexColor: number;
29    pointScale: number;
30};
31
32export const drawExample = async (rootElement: string | HTMLDivElement) => {
33    // Load data from the server
34    const dataFromServer = await getDataFromServer();
35
36    // Create a SciChart3DSurface
37    const { wasmContext, sciChart3DSurface } = await SciChart3DSurface.create(rootElement, {
38        theme: appTheme.SciChartJsTheme,
39    });
40    sciChart3DSurface.worldDimensions = new Vector3(1000, 200, 1000);
41
42    // Create and attach a camera to the 3D Viewport
43    sciChart3DSurface.camera = new CameraController(wasmContext, {
44        position: new Vector3(800, 1000, 800),
45        target: new Vector3(0, 50, 0),
46    });
47
48    // Add an X,Y,Z axis to the viewport
49    sciChart3DSurface.xAxis = new NumericAxis3D(wasmContext, { axisTitle: "X Distance (Meters)" });
50    sciChart3DSurface.yAxis = new NumericAxis3D(wasmContext, { axisTitle: "Height (Meters)" });
51    sciChart3DSurface.zAxis = new NumericAxis3D(wasmContext, { axisTitle: "Z Distance (Meters)" });
52
53    // Create a ScatterRenderableSeries3D and configure as a point cloud with 1px markers
54    sciChart3DSurface.renderableSeries.add(
55        new ScatterRenderableSeries3D(wasmContext, {
56            pointMarker: new PixelPointMarker3D(wasmContext),
57            dataSeries: new XyzDataSeries3D(wasmContext, {
58                xValues: dataFromServer.ascData.XValues,
59                yValues: dataFromServer.ascData.YValues,
60                zValues: dataFromServer.ascData.ZValues,
61                metadata: dataFromServer.meta,
62            }),
63            opacity: 1,
64        })
65    );
66
67    // Also render the point-cloud data as a heightmap / topology map with contours
68    sciChart3DSurface.renderableSeries.add(
69        new SurfaceMeshRenderableSeries3D(wasmContext, {
70            dataSeries: new UniformGridDataSeries3D(wasmContext, {
71                xStart: 0,
72                xStep: dataFromServer.ascData.CellSize,
73                zStart: 0,
74                zStep: dataFromServer.ascData.CellSize,
75                yValues: dataFromServer.heightValues2D,
76            }),
77            minimum: 0,
78            maximum: 50,
79            drawSkirt: true,
80            opacity: 0.7,
81            meshColorPalette: new GradientColorPalette(wasmContext, {
82                gradientStops: [
83                    { offset: 1, color: appTheme.VividPink },
84                    { offset: 0.9, color: appTheme.VividOrange },
85                    { offset: 0.7, color: appTheme.MutedRed },
86                    { offset: 0.5, color: appTheme.VividGreen },
87                    { offset: 0.3, color: appTheme.VividSkyBlue },
88                    { offset: 0.2, color: appTheme.Indigo },
89                    { offset: 0, color: appTheme.DarkIndigo },
90                ],
91            }),
92            contourStroke: appTheme.PaleSkyBlue,
93            meshPaletteMode: EMeshPaletteMode.HEIGHT_MAP_INTERPOLATED,
94            contourStrokeThickness: 2,
95            drawMeshAs: EDrawMeshAs.SOLID_WITH_CONTOURS,
96        })
97    );
98
99    // Add interactivity modifiers for orbiting and zooming with the mousewheel
100    sciChart3DSurface.chartModifiers.add(new MouseWheelZoomModifier3D());
101    sciChart3DSurface.chartModifiers.add(new OrbitModifier3D());
102
103    return { sciChartSurface: sciChart3DSurface, wasmContext };
104};
105
106export const drawHeatmapLegend = async (rootElement: string | HTMLDivElement) => {
107    const { heatmapLegend, wasmContext } = await HeatmapLegend.create(rootElement, {
108        theme: {
109            ...appTheme.SciChartJsTheme,
110            sciChartBackground: appTheme.DarkIndigo + "BB",
111            loadingAnimationBackground: appTheme.DarkIndigo + "BB",
112        },
113        yAxisOptions: {
114            isInnerAxis: true,
115            labelStyle: {
116                fontSize: 12,
117                color: appTheme.ForegroundColor,
118            },
119            axisBorder: {
120                borderRight: 1,
121                color: appTheme.ForegroundColor + "77",
122            },
123            majorTickLineStyle: {
124                color: appTheme.ForegroundColor,
125                tickSize: 6,
126                strokeThickness: 1,
127            },
128            minorTickLineStyle: {
129                color: appTheme.ForegroundColor,
130                tickSize: 3,
131                strokeThickness: 1,
132            },
133        },
134        colorMap: {
135            minimum: 0,
136            maximum: 50,
137            gradientStops: [
138                { offset: 1, color: appTheme.VividPink },
139                { offset: 0.9, color: appTheme.VividOrange },
140                { offset: 0.7, color: appTheme.MutedRed },
141                { offset: 0.5, color: appTheme.VividGreen },
142                { offset: 0.3, color: appTheme.VividSkyBlue },
143                { offset: 0.2, color: appTheme.Indigo },
144                { offset: 0, color: appTheme.DarkIndigo },
145            ],
146        },
147    });
148
149    heatmapLegend.innerSciChartSurface.sciChartSurface.title = "Height (m)";
150
151    heatmapLegend.innerSciChartSurface.sciChartSurface.titleStyle = {
152        fontSize: 12,
153        color: appTheme.ForegroundColor,
154        position: ETitlePosition.Bottom,
155    };
156
157    return { sciChartSurface: heatmapLegend.innerSciChartSurface.sciChartSurface };
158};
159
160async function getDataFromServer() {
161    // The LinearColorMap type in SciChart allows you to generate a colour map based on a
162    // minimum and maximum value, e.g. min=0, max=50 means the gradient brush below is mapped into that range
163    //
164    const colorMap: TLinearColorMap = {
165        Minimum: 0,
166        Maximum: 50,
167        Mode: EColorMapMode.Interpolated,
168        GradientStops: [
169            { color: appTheme.DarkIndigo, offset: 0 },
170            { color: appTheme.Indigo, offset: 0.2 },
171            { color: appTheme.VividSkyBlue, offset: 0.3 },
172            { color: appTheme.VividGreen, offset: 0.5 },
173            { color: appTheme.MutedRed, offset: 0.7 },
174            { color: appTheme.VividOrange, offset: 0.9 },
175            { color: appTheme.VividPink, offset: 0 },
176        ],
177    };
178
179    // Read the ASC Lidar data file with optional color map data
180    const reader: AscReader = new AscReader((height) => {
181        // Linearly interpolate each heightValue into a colour and return to the ASCReader
182        // This will be injected into the SciChart XyzDataSeries3D to colour points in the point-cloud
183        return linearColorMapLerp(colorMap, height);
184    });
185
186    // See our source-code file tq3080_DSM_2M.js for format on this ASC Point cloud data
187    // find the source online at github: https://github.com/ABTSoftware/SciChart.JS.Examples/blob/master/Examples/src/server/Data/t
188    const rawData = await fetchLidarData();
189    const ascData: AscData = reader.parse(await rawData.text());
190
191    // Prepare metadata to contain the color values from ASCData
192    const meta: TMetadata[] = ascData.ColorValues.map((c) => ({
193        vertexColor: c,
194        pointScale: 0,
195    }));
196
197    // Prepare heightValues2D for the uniform surface mesh (transform point cloud to 2d array of heights)
198    const heightValues2D = zeroArray2D([ascData.NumberRows, ascData.NumberColumns]);
199    for (let index = 0, z = 0; z < ascData.NumberRows; z++) {
200        for (let x = 0; x < ascData.NumberColumns; x++) {
201            heightValues2D[z][x] = ascData.YValues[index++];
202        }
203    }
204
205    return {
206        ascData,
207        meta,
208        heightValues2D,
209    };
210}
211

LiDAR 3D Point Cloud Demo in Angular

Overview

This example demonstrates a comprehensive 3D visualization of LiDAR UAV data using Angular and SciChart.js. The demo visualizes a 1km x 1km section of geospatial data as both a 3D point cloud and a contour map, enhanced by a dynamic heatmap legend that indicates elevation.

Technical Implementation

The implementation leverages Angular standalone components with the ScichartAngularComponent to integrate high-performance 3D charts into an Angular application. The chart initialization is performed asynchronously, setting up a custom 3D camera via the SciChart.js CameraController and configuring numeric axes to define the world dimensions. The example fetches ASC formatted LiDAR data, parses it to extract coordinate and elevation information, and applies a dynamic gradient color mapping for height visualization as discussed in the Getting Started with SciChart JS documentation.

Features and Capabilities

Advanced features include real-time interactive 3D controls utilizing modifiers such as OrbitModifier3D and MouseWheelZoomModifier3D to enable smooth navigation around the point cloud. Furthermore, a detailed heatmap legend is rendered, which maps the elevation data to a vibrant color gradient, showcasing techniques from the Heatmap ColorMaps and Legends guide. The integration of WebAssembly via the SciChart.js backend ensures that even complex datasets render smoothly and efficiently, as highlighted in the SciChart.js for Web overview.

Integration and Best Practices

This demo follows best practices for Angular integration by utilizing standalone components as detailed in the Angular Standalone Components guide. Multiple ScichartAngularComponents are rendered on a single page to separate the main 3D chart from the heatmap legend, demonstrating effective techniques for managing complex chart layouts. The asynchronous data fetching and initialization ensure that charts update in real-time, and performance optimizations are achieved through efficient WebAssembly integration. Developers seeking further insights into component rendering and performance optimizations can refer to discussions on multiple component rendering and Angular Performance Optimization.

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