When deploying CesiumJS projects, the library's substantial size (~100MB) often slows CI builds. This analysis evaluates domestic CDN options for external Cesium loading to optimize deployment efficiency without compromising functionality.
When deploying CesiumJS projects, the library's substantial size (~100MB) often slows CI builds. This analysis evaluates domestic CDN options for external Cesium loading to optimize deployment efficiency without compromising functionality.
Following Bentley Systems' acquisition of Cesium and its increasing commercial integration, GIS professionals are exploring alternative open-source solutions for 3D geospatial projects. Maptalks emerges as a viable option, particularly for projects requiring domestic innovation ecosystem compliance.
As an alternative to the MapPlus solution presented in our previous article 《Creating Dynamic Marathon Route Visualizations with MapPlus: A No-Code GIS Solution》, this guide demonstrates a Cesium-based implementation for dynamic route visualization. Developed with ChatGPT assistance and requiring no commercial subscriptions, this approach achieves approximately 70% of MapPlus functionality.
When working on data processing projects with ArcGIS Desktop (non-Pro version), extending arcpy functionality often requires installing third-party libraries. Since Python 2 doesn't include pip by default, this guide outlines the installation process.
C:\Python27\ArcGIS10.X)
During a recent data processing project, I developed an ArcPy script to automate geodatabase schema documentation for technical reports, eliminating manual field inventory tasks. This solution leverages arcpy for data extraction and xlwt for Excel output generation.
The script produces structured Excel sheets detailing field names, aliases, data types, lengths, and spatial references: 
When managing large volumes of high-resolution satellite imagery (e.g., 1:10,000 scale sheets), visualizing coverage gaps becomes challenging due to performance limitations in ArcMap. This script automates the creation of vector polygons representing raster extents for efficient gap analysis.
Visualization: Red outlines = actual coverage, Green polygons = generated extents
Detail showing alignment accuracy
# -*- coding: utf-8 -*-
import os
import time
import arcpy
def create_directory(path):
"""Ensure directory exists, create if missing"""
if os.path.isdir(path):
if not os.path.exists(path):
os.makedirs(path)
else:
parent_dir = os.path.dirname(path)
if not os.path.exists(parent_dir):
os.makedirs(parent_dir)
if __name__ == '__main__':
# Configure input/output
raster_directory = r'F:\gisData\data0929\refY202410072'
output_shapefile = os.path.join(raster_directory, 'shp', 'RasterExtent.shp')
start_time = time.time()
arcpy.env.workspace = raster_directory
raster_list = arcpy.ListRasters()
if not raster_list:
raise IOError("Error: No raster files found!")
# Prepare output feature class
if arcpy.Exists(output_shapefile):
arcpy.Delete_management(output_shapefile)
create_directory(output_shapefile)
output_fc = arcpy.CreateFeatureclass_management(
os.path.dirname(output_shapefile),
"RasterExtent.shp",
"POLYGON"
)
arcpy.AddField_management(output_fc, "RasterName", "TEXT", 50)
# Process rasters
with arcpy.da.InsertCursor(output_fc, ["SHAPE@", "RasterName"]) as cursor:
for raster_file in raster_list:
raster = arcpy.Raster(raster_file)
extent = raster.extent
# Construct polygon from extent
polygon_points = arcpy.Array([
arcpy.Point(extent.XMin, extent.YMin), # LL
arcpy.Point(extent.XMin, extent.YMax), # UL
arcpy.Point(extent.XMax, extent.YMax), # UR
arcpy.Point(extent.XMax, extent.YMin), # LR
arcpy.Point(extent.XMin, extent.YMin) # Close ring
])
cursor.insertRow([arcpy.Polygon(polygon_points), raster_file])
# Performance metrics
elapsed = time.time() - start_time
print(f"Process completed in: {elapsed:.2f} seconds")Extent Extraction:
Note: Requires ArcGIS Desktop with Spatial Analyst extension. Execution time scales linearly with raster count.
Following our previous article on Processing China Marathon Data for Spatial Analysis, this guide demonstrates heatmap visualization of marathon venues using QGIS.
In follow-up to our tutorial on Creating Dynamic Marathon Route Visualizations with MapPlus: A No-Code GIS Solution, we compiled a comprehensive dataset of Chinese marathons through automated data processing. This article documents the methodology and technical implementation.
Marathon distribution heatmap in QGIS
During discussions in our GIS community chat, a colleague humorously noted: "After three years in 3D GIS, my most frequent projects are dashboards." This resonated widely among GIS developers, highlighting a common industry reality—dashboard projects dominate the WebGIS landscape. While not technically complex, these projects demand meticulous attention to aesthetic details like color schemes that consume disproportionate time.
Introducing BigDataView: An open-source repository of dashboard templates designed to alleviate this pain point.
Building on previous world map solutions—either officially approved but misaligned or precisely georeferenced but non-compliant—we present Amap's dataset that combines regulatory compliance, spatial accuracy, and practical usability.
Source:
Amap DistrictLayer API
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